AI in treasury management: Transforming treasury operations with intelligent workflows and decision support

Treasury leaders are under increasing pressure to provide faster liquidity visibility, improve forecast accuracy, strengthen payment controls, and support enterprise financial decisions while managing increasingly complex global banking environments. As organizations expand across regions, currencies, legal entities, and banking relationships, treasury teams must continuously consolidate financial information from multiple sources before they can evaluate cash availability, funding requirements, financial exposures, and liquidity risks.

For many treasury organizations, critical decisions still depend on manual preparation activities. Cash managers may spend significant time collecting bank statements, validating balances, reconciling transactions, and preparing daily liquidity views before treasury leadership can assess funding requirements. Treasury analysts may consolidate AP, AR, payroll, tax, and business forecasts from multiple systems to prepare liquidity projections. Payment operations teams may review payment queues, beneficiary changes, sanctions alerts, and approval records before authorized payment release.

These activities are not simply administrative tasks. They form the foundation for treasury decisions that directly influence enterprise liquidity, borrowing requirements, investment opportunities, working-capital management, and financial resilience. However, the manual effort required to prepare information can limit how quickly treasury teams respond to changing business conditions. When information is fragmented or delayed, treasury teams have less time to evaluate exceptions and respond to changing conditions.

The complexity increases as treasury operates across interconnected financial systems. Treasury management systems (TMS) such as Kyriba, GTreasury, SAP Treasury, Oracle Treasury, and other platforms remain the system of record for treasury transactions, balances, exposures, liquidity structures, and financial controls. However, treasury teams still need to connect information across TMS platforms, ERP systems, banking portals, payment hubs, market-data sources, accounting platforms, and policy repositories to create a complete operating view.

AI can reduce this preparation burden by aggregating financial information, reconciling records, detecting anomalies, interpreting approved policies, preparing forecasts, and assembling decision-support materials.

AI does not replace the TMS. Instead, it extends the value of existing treasury technology investments by helping teams analyze information across connected systems, identify exceptions, prepare decision-support materials, and improve workflow visibility. For treasury managers and TMS owners, the opportunity is not replacing established treasury platforms, but enabling those platforms to support faster analysis, stronger controls, and more proactive decision-making. For treasurers and CFOs, this creates an opportunity to improve enterprise cash visibility, strengthen liquidity resilience, reduce avoidable funding costs, improve working-capital decisions, and increase confidence in financialrisk management.

The strategic importance of treasury continues to increase as CFOs and treasurers focus on cash resilience, funding efficiency, payment security, and financial-risk oversight. Treasury transformation initiatives are increasingly focused on improving data availability, forecasting capabilities, process standardization, and integration across financial systems. Industry research from the Association for Financial Professionals (AFP) continues to identify liquidity management, cash visibility, and payment security as important priorities for treasury organizations.[1]

AI is well suited for treasury because much of the function involves structured financial artifacts, recurring analysis, exception management, policy interpretation, and evidence preparation. However, the opportunity is not a generic chatbot answering treasury questions. A cash manager may need AI to consolidate BAI2, MT940, CAMT.052, and CAMT.053 bank statements into a global cash position worksheet, identify unusual movements, and prepare a liquidity review package. A treasury analyst may need AI to analyze forecast-versus-actual variance across AP, AR, payroll, tax, and treasury flows to identify liquidity drivers. A payment operations lead may need AI to analyze ISO 20022 pain.001 payment files, identify first-time beneficiary risks, compare bank-change activity, and prepare exceptions before authorized payment release.

The value of AI in treasury management is not autonomous financial execution. It is improving the quality, speed, and consistency of treasury decisions that affect enterprise financial outcomes. By improving cash visibility, forecasting reliability, payment controls, and risk analysis, AI can help treasury leaders make better-informed decisions around liquidity positioning, funding requirements, investment opportunities, and financial resilience.

AI can aggregate financial information, identify anomalies, compare activity against policies, prepare forecasts, classify exceptions, and generate reporting materials. However, treasurers, cash managers, payment operations leads, FX risk managers, treasury accountants, and other authorized roles continue to own funding decisions, payment approvals, hedge decisions, accounting attestations, and regulatory obligations.

Treasury operates as a connected finance function across the broader finance and order-to-cash ecosystem, making clear process boundaries essential for AI implementation. This article focuses on treasury-owned activities such as liquidity visibility, payment controls, funding decisions, bank-account governance, financialrisk management, and treasury reporting. Adjacent finance processes retain ownership of their operational activities: accounts payable manages invoice approval and procure-to-pay execution, accounts receivable and cash application manage customer payment matching and receivables posting, and financial close manages period-end accounting and reporting. Treasury teams use information from these processes to manage liquidity, funding decisions, and financial risk while maintaining ownership of treasury controls and decisions.

Because treasury decisions depend on financial materiality, timing, and control requirements, AI opportunities must be mapped at the sub-process level rather than broad categories such as “AI for treasury” or “AI for payments.” A practical treasury AI opportunity requires defined artifacts, systems, exception categories, decision owners, approval boundaries, and measurable outcomes.

This article uses the treasury management operating model to break treasury activities into functions, processes, and sub-processes to identify where AI can support cash visibility, liquidity planning, payment controls, financialrisk management, and treasury governance.

• How AI is transforming treasury management operations
• Why AI use cases in treasury management must be mapped at the sub-process level
• Treasury management operating model and AI opportunity mapping across treasury processes
• High-value AI use cases in treasury management
• How agentic AI works in treasury management workflows
• How to prioritize AI use cases in treasury management
• Governance, risk, and responsible AI in treasury management
• How ZBrain operationalizes AI use cases in treasury management
• Future of AI in treasury management

How AI is transforming treasury management operations

Treasury organizations are moving from periodic reporting and manual information consolidation toward more connected, data-driven operating models. As treasury organizations mature, the focus is shifting from improving transaction processing to building more intelligent treasury operating models. This evolution requires connecting existing treasury systems, standardizing financial data, improving process visibility, and applying AI where teams spend significant effort preparing information before decisions are made. The objective is not to automate treasury judgment or isolated tasks, but to reduce manual data preparation so treasury professionals can focus on higher-value analysis, liquidity insight, risk identification, decision support, and stronger financial controls.

A modern treasury environment depends on multiple information flows. Bank statements provide cash visibility. ERP systems provide operational cash requirements. Payment systems provide upcoming obligations. Forecasting platforms provide expected inflows and outflows. Market-data systems provide financial-risk information. Policy repositories define investment limits, approval requirements, and treasury controls.

The TMS remains the system of record for treasury balances, transactions, exposures, and controls. AI can extend its value by connecting information across TMS platforms, ERP systems, banking channels, payment hubs, market-data sources, and policy repositories to prepare analysis, identify exceptions, and support controlled workflows.

For example, daily cash positioning requires more than collecting bank balances. Treasury teams must determine whether available cash aligns with expected payments, funding requirements, target balances, and liquidity policies. AI can combine bank statements, TMS records, scheduled payments, forecasts, and policy rules to prepare a controlled liquidity view for cash manager review. Similarly, liquidity forecasting requires more than generating a projection. Treasury teams need to understand why forecasts change and what actions may be required. AI can analyze forecast-versus-actual variance, identify drivers behind deviations, and prepare scenario analysis to support treasury planning.

The highest-value treasury AI opportunities generally fall into five categories:

• Document-heavy treasury work: Financial agreements, confirmations, fee statements, covenant documents, bank mandates, signatory records, and hedge designation memos require structured review and evidence preparation. AI can extract information, identify missing details, and prepare documentation packages.
• Narrative-heavy treasury work: Forecast commentary, treasury committee packs, variance explanations, policy exception summaries, and covenant reporting require interpretation of approved financial information. AI can draft summaries while maintaining links to supporting evidence.
• Exception-heavy treasury work: Unmatched bank movements, payment repairs, sanctions alerts, beneficiary changes, forecast deviations, and policy-limit exceptions require classification and prioritization. AI can identify patterns and route cases to responsible reviewers.
• Knowledge-heavy treasury work: Treasury policies, sweep structures, investment limits, debt covenants, fee schedules, hedge-accounting guidance, and sanctions requirements require frequent interpretation. AI can retrieve approved policies and assess activities such as cash sweeps, investments, borrowing, hedging, fee payments, and sanctions screening against defined rules, limits, and approval requirements.
• Workflow-heavy treasury work: Daily cash positioning, payment review, forecasting updates, hedge confirmation matching, account administration, and intercompany funding require coordination across systems and teams. AI can assemble information and prepare work packets while preserving approval boundaries.

Measuring the impact of AI in treasury management

Treasury leaders should evaluate AI opportunities against measurable operational outcomes rather than technology capability alone. Metrics should be defined for the specific workflow and compared with an established baseline

Relevant treasury metrics include:

Cash visibility

• Percentage of cash accounts visible through centralized reporting
• Time required to prepare daily cash position
• Number of bank accounts consolidated
• Volume of unmatched bank transactions

Liquidity forecasting

• Forecast accuracy
• Forecast-versus-actual variance
• Time required for forecast preparation
• Liquidity scenario preparation cycle time

Payments

• Payment exception rate
• Payment repair cycle time
• Fraud review workload
• Sanctions screening review volume

Financial risk

• Exposure identification completeness
• Hedge coverage monitoring
• Counterparty exposure visibility
• Hedge documentation preparation time

Treasury operations

• Manual analyst preparation effort
• Reporting cycle time
• Bank fee variance identification
• Policy exception resolution time

The practical design rule is that AI in treasury management should begin with decisions that matter to treasury teams. The right question is not “Where can AI be added?” but: “Where does treasury spend significant effort preparing information before an authorized person makes a financial decision?”

Why AI use cases in treasury management must be mapped at the sub-process level

Treasury management is a connected enterprise function, not a single workflow. It spans cash visibility, forecasting, payments, banking administration, liquidity structures, funding, investments, financial risk, accounting support, and governance reporting. Each area depends on different financial artifacts, systems, controls, exceptions, and decision owners.

Broad categories such as “AI for treasury,” “AI for payments,” or “AI for cash management” are too broad to define implementation requirements. They do not identify:

• Which financial artifacts are required
• Which systems must be connected
• Which exceptions must be handled
• Which controls apply
• Which role owns the final decision
• Which outcomes should be measured

A better approach is to map AI use cases to the treasury management operating model which comprises the following:

Function:
A governed treasury domain with defined ownership, controls, systems, artifacts, and decision responsibilities.

Examples:

• Cash positioning and bank polling
• Liquidity forecasting
• Payment controls
• FX risk management

Process:
A recurring treasury workflow area within a treasury function.

Examples:

• Bank statement consolidation
• Forecast preparation
• Payment queue review
• Hedge confirmation matching

Sub-process:
A specific treasury activity with defined inputs, outputs, exceptions, controls, and accountable reviewers.

Examples:

• CAMT.053 statement reconciliation
• First-time beneficiary anomaly detection
• Forecast-versus-actual variance analysis
• Hedge designation memo preparation

AI-enabled opportunity:
An AI capability applied within a defined sub-process to improve analysis, exception identification, evidence preparation, or decision support.

Examples:

• Anomaly detection applied to bank transactions to identify unexplained movements.
• Time-series forecasting applied to cashflow history and approved forecast inputs.
• Policy-grounded retrieval applied to treasury policies and authority limits.
• Document intelligence applied to debt agreements and hedge documentation.

Mapping AI opportunities at the sub-process level makes them more buildable, measurable, and governable. For example:

“AI for payments” does not define whether the workflow involves ISO 20022 pain.001 files, NACHA transactions, wires, sanctions screening, beneficiary validation, payment repair, or approval controls.

A defined use case such as:

“First-time beneficiary and bank-change anomaly detection for ISO 20022 payment queues with payment operations lead review”

This definition establishes:

• The financial artifact
• The AI capability
• The control requirement
• The accountable reviewer
• The decision boundary

Sub-process mapping also reveals implementation dependencies.

A liquidity forecasting workflow may require:

• AP payment schedules
• AR collection forecasts
• Payroll information
• Tax flows
• Treasury transactions
• Historical forecast performance

A payment control workflow may require:

• Payment files
• Beneficiary records
• Bank-account information
• Sanctions results
• Approval matrices
• Payment policies

By defining these dependencies upfront, treasury leaders can prioritize realistic AI opportunities, establish appropriate controls, measure expected impact, and scale AI workflows across treasury operations.

Treasury management operating model and AI opportunity mapping across treasury processes

The treasury management operating model maps how organizations manage cash visibility, liquidity decisions, payment controls, banking structures, financial risk, and governance activities. Each function is decomposed into processes and sub-processes to identify where AI can support specific treasury activities using defined artifacts, systems, controls, and human review boundaries.

Treasury management operates across interconnected financial activities that begin with cash visibility and continue through liquidity planning, payment execution controls, funding decisions, risk management, accounting support, and governance reporting. Each function depends on upstream information, downstream decisions, and multiple systems of record, making it difficult to identify AI opportunities without understanding the complete operating model.

The following operating model focuses on enterprise corporate treasury within the broader finance ecosystem, not bank treasury, asset-liability management for financial institutions, or capital markets activities. It covers ten core functions grouped across four operating areas:

Daily cash and liquidity operations

Function 1: Cash positioning and bank polling

Turning bank-reported balances, transactions, and expected settlements into a controlled global cash position.

Function overview

Cash positioning and bank polling provide visibility into enterprise cash across entities, banks, accounts, and currencies. The function consolidates prior-day and intraday bank statements, transaction records, expected settlements, and treasury-system data into a global cash position used for liquidity decisions, funding analysis, and treasury reporting.

Daily cash positioning often requires manual consolidation across multiple banking relationships, statement validation, balance reconciliation, and liquidity preparation before treasury teams can assess funding requirements.

Treasury owns cash visibility, liquidity interpretation, and funding recommendations, while accounts payable and accounts receivable remain responsible for transaction execution.

Teams involved

Treasury operations teams, cash managers, treasury analysts, regional treasury, treasury technology teams, shared services teams, corporate accounting teams, and internal audit teams manage cashpositioning activities.

What AI helps with

AI helps treasury teams improve liquidity visibility by aggregating bank statements, TMS records, settlement data, payment activity, and forecast inputs. It can combine BAI2, MT940, CAMT.052, and CAMT.053 statements with treasury data to prepare a consolidated global cash position worksheet by entity, bank, and currency. Entity resolution maps accounts, legal entities, currencies, and transactions across systems.

Anomaly detection identifies unexplained movements, missing statements, duplicate transactions, and threshold breaches, while variance analysis compares expected and actual activity to prepare exception summaries for cash manager review.

What humans continue to own

Cash managers and treasurers continue to evaluate liquidity conditions, approve funding actions, review unusual account activity, and determine escalation requirements.

AI prepares cash-position analysis, identifies exceptions, and supports liquidity decisions, but authorized treasury personnel continue to approve funding decisions, accept liquidity risk, and release instructions.

Key artifacts

• BAI2 bank statements
• MT940 bank statements
• CAMT.052 intraday statements
• CAMT.053 end-of-day statements
• Global cash position worksheet
• TMS balance records
• Bankaccount master data
• Target balance policies
• Sweep instructions

Systems involved

• Treasury management systems (TMS)
• Enterprise resource planning (ERP) systems
• Bank portals
• Treasury reporting and analytics platforms
• SWIFT connectivity platforms
• Host-to-host banking connections
• Banking APIs

Regulatory and control considerations

Cash positioning requires controls over data integrity, bank connectivity, account access, and liquidity reporting accuracy.

Key controls include:

• Controlled bank connectivity
• Role-based access to banking systems
• Segregation of duties between preparation and approval activities
• Evidence retention for statement retrieval and position preparation
• Treasury policies governing target balances and liquidity thresholds

Applicable considerations include:

• ISO 20022 financial messaging standards (Globally used)
• Internal treasury policies (Internal policy based)
• SOX financial controls (US-specific, where applicable)

Accountable roles

Cash manager, treasurer, treasury analyst, treasury operations lead, treasury technology lead, corporate controller, and internal auditor.

Highest-value opportunities

• Global cash position worksheet preparation: High value because treasury teams must consolidate information across multiple banks, entities, accounts, and currencies before making liquidity decisions.
• Bank transaction anomaly detection: High value because unexplained movements and missing information can affect funding decisions and require timely investigation.
• Target balance and sweep monitoring: High value because treasury teams need visibility into excess cash, funding requirements, and liquidity structure effectiveness.

Example agentic workflow: Daily cash positioning and anomaly detection

Agent role: Prepare the daily global cash position packet and identify liquidity exceptions requiring cash manager review.

Starting artifacts:

• Prior-day BAI2 statements
• MT940 statements
• CAMT.052 intraday statements
• CAMT.053 statements
• TMS settlement records
• Scheduled AP payments
• Current liquidity forecast
• Target balance policies
• Sweep instructions

Workflow:

1. The workflow begins when prior-day bank statements arrive from approved banking connections.
2. The agent retrieves statement balances, transaction records, settlement information, payment schedules, and forecast data.
3. The agent retrieves target balance policies, sweep structures, and liquidity decision rules.
4. The agent prepares a consolidated cash position by entity, bank, and currency.
5. The agent identifies unusual transactions, balance exceptions, and liquidity variances.
6. The Cash Manager reviews findings and confirms required treasury actions.
7. Approved decisions proceed through existing treasury controls with approvals and evidence retained.

Exception handling:

The workflow routes missing statements, unmatched transactions, unexpected debits, and account-level inconsistencies to treasury reviewers.

Human checkpoint:

The cash manager confirms liquidity decisions before any funding action proceeds.

Output:

A reviewed daily cash position package containing balances, liquidity observations, exception analysis, recommendations, approvals, and audit evidence.

Function 2: Cash and liquidity forecasting

Turning operational cash flow inputs into forward-looking liquidity visibility.

Function overview

Cash and liquidity forecasting converts expected cash inflows, outflows, and treasury movements into forward-looking liquidity projections. The function combines direct forecasting inputs from AP, AR, payroll, tax flows, and treasury activity with longer-range assumptions to support funding decisions and liquidity planning.

Forecast accuracy directly affects treasury’s ability to manage borrowing requirements, investment opportunities, liquidity buffers, and financial resilience.

Treasury owns forecasting methodology, liquidity assumptions, and forecast governance. Operational functions provide business inputs that support treasury forecasting activities.

Teams involved

Treasury analysts, cash managers, treasurers, FP&A teams, accounts payable teams, accounts receivable teams, payroll teams, tax teams, business finance teams, and corporate controllers.

What AI helps with

AI can support treasury forecasting decisions by applying time-series forecasting to historical cash movements, approved forecast inputs, and operational cash-flow patterns. Multi-source aggregation can combine AP, AR, payroll, tax flows, treasury transactions, and historical forecast data to support the preparation of a 13-week direct cash forecast. Variance and driver analysis can identify why actual cash movements differ from forecast assumptions. Scenario simulation can evaluate liquidity outcomes under alternative assumptions such as delayed collections, increased payments, or changing funding requirements.

What humans continue to own

Treasury analysts and treasurers continue to approve forecast assumptions, evaluate liquidity scenarios, determine funding strategies, and communicate liquidity outlook.

AI prepares forecasts, identifies variance drivers, and supports scenario analysis, but authorized treasury personnel continue to make liquidity decisions and accept financial risk.

Key artifacts

• 13-week direct forecast
• AP payment schedules
• AR collection forecasts
• Payroll forecasts
• Tax flow forecasts
• Treasury cash-flow records
• Long-range indirect forecast
• Forecast-versus-actual variance reports
• Liquidity stress-testing models and scenarios

Systems involved

• Treasury management system
• ERP systems
• Accounts payable platforms
• Accounts receivable platforms
• Payroll systems
• Tax systems
• FP&A planning platforms
• Analytics platforms

Regulatory and control considerations

Liquidity forecasting requires controlled assumptions, documented methodologies, and evidence supporting treasury decisions.

Key controls include:

• Approved forecast methodologies
• Version-controlled assumptions
• Forecast ownership definitions
• Access controls over financial projections
• Evidence retention for treasury reporting

Applicable considerations include:

• SOX financial controls (US-specific, where applicable)
• Internal treasury forecasting policies (Internal-policy-based)

Accountable roles

Treasury analyst, cash manager, treasurer, FP&A lead, business finance leaders, corporate controller, and internal auditor.

Highest-value opportunities

• 13-week liquidity forecasting: High value because treasury teams frequently consolidate inputs from multiple business functions before making liquidity decisions.
• Forecast-versus-actual variance analysis: High value because identifying forecast drivers improves forecasting reliability.
• Stress liquidity scenario analysis: High value because treasury leadership can evaluate resilience before liquidity constraints occur.

Example agentic workflow: Liquidity forecasting preparation

Agent role: Prepare a liquidity forecast review package using operational cash-flow inputs.

Starting artifacts:

• 13-week direct forecast
• AP payment schedules
• AR collection forecasts
• Payroll forecasts
• Tax flow forecasts
• Treasury cash-flow records
• Forecast-versus-actual reports
• Scenario assumptions

Workflow:

1. The workflow begins during the scheduled treasury forecasting cycle.
2. The agent retrieves approved cash-flow inputs from ERP, planning, and treasury systems.
3. The agent retrieves forecasting methodologies, assumptions, and scenario requirements.
4. The agent prepares updated liquidity projections and variance analysis.
5. The agent identifies forecast drivers and prepares scenario comparisons.
6. Treasury analysts review assumptions and determine forecast adjustments.
7. Approved forecasts are recorded with supporting evidence.

Exception handling:

The workflow routes missing inputs, unsupported assumptions, unusual forecast changes, and material variances to responsible teams.

Human checkpoint:

Treasury analysts validate forecast drivers before updated forecasts support liquidity decisions.

Output:

A liquidity forecast package containing projections, variance analysis, scenario outputs, reviewer decisions, and supporting evidence.

Function 3: Payments and payment fraud controls

Turning payment instructions, transaction records, and control requirements into a controlled payment review and release process.

Function overview

Payments and payment fraud controls manage the preparation, validation, screening, exception handling, and controlled release of enterprise payment instructions. The function receives approved payment requests, payment files, beneficiary information, sanctions-screening results, funding availability data, and treasury control requirements, then prepares payment queues for authorized review and release.

As payment volumes increase and organizations operate across multiple banking networks, payment controls become increasingly important to treasury leaders. Treasury teams must balance payment efficiency with fraud prevention, sanctions compliance, approval controls, and operational continuity.

Treasury owns payment file control, payment release governance, funding availability review, and settlement visibility. Accounts payable owns invoice approval, liability validation, and procure-to-pay execution.

Teams involved

Payment operations leads, treasury operations teams, cash managers, treasurers, accounts payable teams, compliance teams, fraud monitoring teams, treasury technology teams, shared services teams, and internal audit teams manage payment-control activities.

What AI helps with

AI can support payment decisions by analyzing payment activity, identifying unusual patterns, and preparing review materials before authorized release.

Classification can organize payment queues by transaction type, approval status, risk indicators, and exception categories. Anomaly detection can identify unusual payment patterns by comparing payment files, beneficiary information, transaction history, and approved payment behavior. Entity resolution can compare beneficiary records, bank account details, and historical payment relationships to identify first-time beneficiary risks or bank-change anomalies. Policy-grounded retrieval can retrieve approved payment policies, authority matrices, and sanctions requirements to support review.

What humans continue to own

Payment operations leads and authorized treasury personnel continue to approve payment release, validate payment exceptions, confirm authority requirements, and determine whether suspicious activity requires escalation.

AI identifies payment patterns, prepares review information, and routes exceptions, but authorized personnel continue to approve transactions, accept payment risk, and release payment instructions.

Key artifacts

• ISO 20022 pain.001 payment files
• NACHA payment files
• Wire transfer instructions
• Payment queues
• Beneficiary master records
• Bank account master data
• Sanctions screening results
• OFAC screening records
• Payment approval matrices
• Payment repair records

Systems involved

• Treasury management system
• ERP payment modules
• Payment hubs
• Bank portals
• SWIFT connectivity platforms
• Host-to-host banking connections
• Sanctions screening platforms
• Fraud monitoring platforms
• Identity and access management systems
• Document repositories

Regulatory and control considerations

Payment operations require strong preventive controls because payment activity creates direct financial exposure. Governance must ensure that AI supports payment review without bypassing existing treasury controls.

Key controls include:

• Segregation of duties between payment preparation and release
• Dual approval requirements
• Payment authority limits
• Beneficiary change controls
• Sanctions screening requirements
• Payment cutoff monitoring
• Evidence retention for approvals and exceptions

Applicable standards and frameworks include:

• ISO 20022 financial messaging standards (Globally used)
• NACHA operating rules (US-specific)
• OFAC sanctions requirements (US-specific)
• Global sanctions regimes (Globally used)
• SOX financial control requirements (US-specific, where applicable)

AI must not independently release payments, modify beneficiary instructions, override sanctions controls, or bypass payment authority requirements.

Accountable roles

Payment operations lead, treasurer, treasury operations manager, cash manager, sanctions compliance officer, accounts payable lead, treasury technology lead, and internal auditor.

Highest-value opportunities

• First-time beneficiary anomaly detection: High value because new beneficiary payments require additional review before release.
• Bank-change anomaly detection: High value because unexpected beneficiary account changes can indicate payment fraud risk.
• Payment queue classification and repair management: High value because payment exceptions require coordination across treasury, banking, compliance, and operational teams.

Example agentic workflow: Payment fraud and release-readiness review

Agent role: Prepare a payment-review package by analyzing payment queues, beneficiary information, and control requirements before authorized release.

Starting artifacts:

• ISO 20022 pain.001 payment files
• NACHA payment files
• Wire transfer instructions
• Beneficiary master records
• Bankaccount information
• Sanctions screening results
• Payment approval matrices
• Treasury payment policies

Workflow:

1. The workflow begins when approved payment files enter the treasury payment queue.
2. The agent retrieves payment details, beneficiary records, screening results, approval status, and funding information from approved systems.
3. The agent retrieves payment policies, authority limits, and sanctions-control requirements.
4. The agent analyzes payment activity and prepares a review package highlighting first-time beneficiaries, bank-change anomalies, cutoff risks, and unresolved exceptions.
5. The agent classifies exceptions and routes cases to payment operations or compliance teams.
6. Authorized payment operations personnel review findings and confirm payment-release decisions.
7. Approved payments proceed through existing treasury controls with approvals and evidence retained.

Exception handling:

The workflow routes sanctions alerts, beneficiary inconsistencies, missing approvals, insufficient funding conditions, and paymentformat issues to responsible reviewers.

Human checkpoint:

Payment operations personnel confirm payment validity and release decisions before execution.

Output:

A paymentreview package containing payment validation results, risk indicators, exception cases, approvals, and audit evidence.

Function 4: Bank account management and administration

Turning fragmented bankaccount information and documentation into controlled account governance.

Function overview

Bank account management and administration maintains visibility and control over enterprise bank accounts, signatories, account documentation, and account lifecycle activities. The function manages bank account inventory, account opening and closing workflows, signatory records, regulatory documentation, and electronic bank-account management (eBAM) records.

As organizations expand across countries, banking partners, and legal entities, treasury teams often manage account information distributed across TMS platforms, ERP systems, bank portals, and document repositories. Maintaining accurate ownership, access, and documentation records is essential for operational continuity and financial control.

Treasury teams own bankaccount governance, documentation coordination, and account administration controls. Compliance and legal teams remain responsible for formal policy interpretation and approvals.

Teams involved

Treasury operations teams, treasury analysts, bank relationship managers, compliance teams, legal teams, tax teams, treasury technology teams, shared services teams, and internal audit teams.

What AI helps with

AI can support bankaccount governance by improving visibility across account records, documentation, and approval workflows. Document intelligence can extract account details, signatory information, and documentation requirements from bank forms, mandates, and agreements. Entity resolution can reconcile bank account records across treasury systems, ERP platforms, bank records, and documentation repositories. Classification can identify incomplete accountopening requests, outdated documentation, missing approvals, and signatory inconsistencies.

What humans continue to own

Treasury personnel continue to approve account opening and closing requests, authorize signatory changes, validate banking relationships, and confirm compliance with treasury policies.

AI identifies documentation gaps, reconciles records, and prepares accountmanagement materials, but authorized personnel continue to approve account changes and attest compliance.

Key artifacts

• Bank account inventory
• Signatory matrix
• Account opening forms
• Account closing documentation
• Bank mandates
• FBAR filings
• eBAM records
• Bankaccount master data
• Treasury approval records

Systems involved

• Treasury management system
• ERP master data systems
• Bank portals
• eBAM platforms
• Document-management repositories
• Identity-management systems
• Governance, risk, and compliance platforms

Regulatory and control considerations

Bankaccount administration requires controls over ownership, authorization, documentation, and access.

Key controls include:

• Authorized signatory management
• Account ownership validation
• Access reviews
• Documentation retention
• Accountopening approval workflows
• Periodic account inventory reviews

Applicable considerations include:

• FBAR reporting (US-specific)
• eBAM practices (Globally used)
• Internal treasury policies (Internal policy based)

Accountable roles

Treasury operations lead, treasurer, bank relationship manager, compliance officer, legal counsel, tax team, treasury analyst, and internal auditor.

Highest-value opportunities

• Bank-account inventory reconciliation: High value because organizations maintain account information across multiple systems and banking relationships.
• Signatory record validation: High value because outdated authorization records create operational and control risks.
• Account opening workflow review: High value because account administration involves recurring documentation and approval requirements.

Example agentic workflow: Bank account inventory reconciliation

Agent role: Prepare a bankaccount governance review package by reconciling account records across treasury systems and banking sources.

Starting artifacts:

• Bank account inventory
• TMS account records
• ERP master data
• Bank documentation
• Signatory matrix
• eBAM records

Workflow:

1. The workflow begins with a scheduled bank account governance review.
2. The agent retrieves account records from treasury systems, ERP platforms, and banking repositories.
3. The agent retrieves approved account management policies and signatory requirements.
4. The agent compares account attributes, ownership information, and authorization records.
5. The agent identifies missing documentation, inactive accounts, and inconsistent records.
6. Treasury operations reviews findings and approves required actions.
7. Approved updates are recorded through existing governance processes with evidence retained.

Exception handling:

The workflow routes missing documentation, unauthorized signatories, inactive accounts, and conflicting records to responsible reviewers.

Human checkpoint:

Treasury operations confirms account changes and signatory decisions before updates proceed.

Output:

A bank account governance package containing reconciled records, identified exceptions, required actions, approvals, and audit evidence.

Function 5: Liquidity structure management

Turning distributed cash balances and intercompany funding requirements into controlled liquidity structures.

Function overview

Liquidity structure management manage how organizations centralize and allocate cash across entities, currencies, and banking relationships. The function covers physical and notional cash pooling, in-house bank operations, intercompany funding, intercompany loan schedules, and related treasury controls.

For multinational organizations, effective liquidity structures improve cash accessibility while maintaining appropriate controls, documentation, and compliance. The function receives entity cash positions, liquidity requirements, funding needs, intercompany balances, and treasury policies, then produces liquidity analysis and funding recommendations for review.

Treasury teams own liquidity structure administration and funding governance. Tax teams own transfer-pricing requirements, while legal teams own contractual interpretation and documentation approval.

Teams involved

Treasury operations teams, regional treasury centers, cash management teams, treasury analysts, intercompany accounting teams, corporate accounting teams, tax teams, legal teams, treasury technology teams, and internal audit teams manage liquiditystructure activities.

What AI helps with

AI can support liquidity decisions by creating a connected view of entity cash positions, funding requirements, and treasury structures.

Multi-source aggregation can combine entity-level balances, bank information, intercompany positions, and treasury policies to prepare consolidated liquidity views. Entity resolution can map legal entities, currencies, bank accounts, and intercompany relationships across treasury and ERP systems. Scenario analysis can evaluate alternative liquidity structures, funding arrangements, and cash-allocation scenarios. Reconciliation analysis can compare intercompany loan schedules, funding movements, and accounting records to identify inconsistencies. Policy-grounded retrieval can retrieve approved liquidity policies, funding rules, and interest-setting requirements to support treasury review.

What humans continue to own

Treasury leaders continue to determine liquidity structures, approve funding strategies, evaluate intercompany funding arrangements, and manage liquidity risk. Tax and legal teams continue to own transfer-pricing interpretation, contractual review, and jurisdiction-specific requirements.

AI analyzes liquidity information, identifies inconsistencies, and prepares recommendations, but authorized personnel continue to approve funding decisions, accept financial risk, and attest compliance.

Key artifacts

• Physical cash pooling structures
• Notional cash pooling records
• In-house bank records
• Entity cash positions
• Intercompany funding requests
• Intercompany loan schedules
• Interest-rate policies
• Treasury liquidity policies
• Funding approval records

Systems involved

• Treasury management system
• ERP systems
• Intercompany accounting platforms
• Bank platforms
• Cash management platforms
• Transfer pricing repositories
• Document management systems
• Reporting platforms

Regulatory and control considerations

Liquidity structures require controls over intercompany funding, documentation, entity ownership, and financial reporting.

Key controls include:

• Approved cash-pooling structures
• Funding authorization limits
• Intercompany reconciliation procedures
• Transfer pricing documentation requirements
• Segregation of duties between funding preparation and approval
• Evidence retention for liquidity decisions

Applicable considerations include:

• Internal treasury policies (Internal policy based)
• Transfer-pricing requirements (Jurisdiction specific)
• Financial reporting controls (US specific where SOX applies)
• Intercompany agreements (Contractual)

Accountable roles

Treasurer, assistant treasurer, treasury analyst, regional treasury manager, intercompany accounting lead, corporate controller, tax lead, legal counsel, and internal auditor.

Highest-value opportunities

• Intercompany funding reconciliation: High value because liquidity structures require alignment between treasury records, ERP balances, and intercompany accounting records.
• Liquiditystructure scenario analysis: High value because treasury leaders must evaluate whether existing structures support changing liquidity requirements.
• Intercompany loan schedule monitoring: High value because incomplete or inconsistent funding records can create accounting and compliance issues.

Example agentic workflow: Intercompany funding review

Agent role: Prepare an intercompany funding review package by analyzing entity liquidity requirements and internal funding records.

Starting artifacts:

• Entity cash positions
• Intercompany loan schedules
• Funding requests
• Treasury liquidity policies
• Interest-rate policies
• ERP intercompany balances

Workflow:

1. The workflow begins when an entity funding requirement is submitted, or a scheduled liquidity review occurs.
2. The agent retrieves entity cash balances, intercompany positions, funding schedules, and treasury policies.
3. The agent retrieves approved liquidity-structure rules and internal funding requirements.
4. The agent compares entity liquidity requirements with available funding structures.
5. The agent identifies funding gaps, unmatched balances, documentation issues, and policy exceptions.
6. Treasury leadership reviews funding recommendations and determines approved actions.
7. Approved funding decisions are recorded through existing treasury and accounting processes with supporting evidence retained.

Exception handling:

The workflow routes unmatched intercompany balances, incomplete loan documentation, funding-limit exceptions, and policy conflicts to responsible reviewers.

Human checkpoint:

Treasury leadership confirms funding decisions before any intercompany funding action proceeds.

Output:

A liquiditystructure review package containing funding analysis, exceptions, recommendations, approvals, and audit evidence.

Function 6: Debt and investment management

Turning borrowing requirements, investment positions, and policy limits into controlled financial decisions.

Function overview

Debt and investment management supports the administration of financing facilities, debt obligations, short-term investments, covenant requirements, and counterparty exposure. The function receives debt agreements, facility information, investment positions, market information, and treasury policies, then prepares analysis required for financing and investment decisions.

Treasury leaders use this information to manage borrowing requirements, refinancing timelines, investment allocation, and financial exposure. The function directly supports CFO and treasurer priorities around liquidity resilience, funding costs, capital allocation, and risk management.

Treasury teams own debt and investment monitoring and decision support. Legal teams remain responsible for contractual interpretation, while accounting teams remain responsible for financial reporting treatment.

Teams involved

Treasury teams, assistant treasurers, treasurers, corporate finance teams, investment management teams, legal teams, corporate accounting teams, risk teams, and internal audit teams.

What AI helps with

AI can support debt and investment decisions by extracting information from financial documents, monitoring exposure, and preparing analysis.

Document intelligence can extract facility terms, maturity dates, covenant requirements, and contractual information from debt agreements. Classification can organize debt instruments, investment positions, and counterparty information. Predictive analysis can identify upcoming maturity events, refinancing timelines, and exposure trends. Constraintbased recommendation can compare investment options against approved policy limits, maturity requirements, and risk constraints. Policy-grounded retrieval can retrieve investment policies, debt requirements, and approval rules to support treasury review.

What humans continue to own

Treasurers and authorized finance leaders continue to approve borrowing decisions, refinancing strategies, investment placements, counterparty selections, and policy exceptions.

AI analyzes financial information, prepares monitoring outputs, and identifies potential issues, but authorized personnel continue to accept funding and investment risk.

Key artifacts

• Debt agreements
• Facility utilization reports
• Covenant certificates
• Debt maturity ladder
• Refinancing calendar
• Investment policy documents
• Investment position records
• Counterparty exposure reports
• Short-term investment records

Systems involved

• Treasury management system
• Debt management platforms
• Investment management platforms
• ERP systems
• Market data platforms
• Risk management platforms
• Document repositories
• Reporting platforms

Regulatory and control considerations

Debt and investment management requires controls over borrowing authority, investment limits, counterparty exposure, and financial reporting.

Key controls include:

• Facility approval requirements
• Covenant monitoring procedures
• Investment policy limits
• Counterparty exposure thresholds
• Documentation retention
• Approval workflows for financing and investment decisions

Applicable considerations include:

• Debt agreements (Contractual)
• Investment policies (Internal policy based)
• Financial reporting controls (US specific where SOX applies)

AI must not independently borrow funds, invest funds, select counterparties, or approve policy exceptions.

Accountable roles

Treasurer, assistant treasurer, treasury analyst, corporate finance lead, investment manager, legal counsel, corporate controller, risk manager, and internal auditor.

Highest-value opportunities

• Covenant monitoring preparation: High value because debt agreements contain complex requirements that require recurring review and evidence preparation.
• Debt maturity ladder analysis: High value because refinancing decisions depend on timely visibility into future obligations.
• Counterparty exposure monitoring: High value because treasury teams must continuously evaluate exposure against approved limits.

Example agentic workflow: Covenant monitoring preparation

Agent role: Prepare a debt covenant review package by comparing financing requirements with available financial information.

Starting artifacts:

• Debt agreements
• Covenant definitions
• Financial reports
• Facility utilization records
• Covenant certificates
• Treasury policies

Workflow:

1. The workflow begins when a scheduled covenant review cycle starts.
2. The agent retrieves debt agreements, covenant requirements, financial inputs, and facility information.
3. The agent retrieves approved covenant policies and reporting requirements.
4. The agent extracts covenant conditions and prepares comparison analysis.
5. The agent identifies missing information, potential covenant issues, and documentation gaps.
6. Treasury and finance reviewers evaluate results and determine required actions.
7. Approved covenant reporting materials are prepared and retained with supporting evidence.

Exception handling:

The workflow routes missing documentation, potential covenant concerns, outdated information, and policy exceptions to responsible reviewers.

Human checkpoint:

Treasury and finance leadership validate covenant assessments before certification or communication.

Output:

A covenant monitoring package containing extracted requirements, analysis results, exceptions, reviewer decisions, and audit evidence.

Function 7: FX and interest rate risk management

Turning financial exposures, market movements, and hedge documentation into controlled risk-management decisions.

Function overview

FX and interest rate risk management identifies and analyzes financial exposures arising from foreign currency movements and interestrate changes. The function uses forecast cash flows, balance-sheet exposures, debt information, derivative records, market data, and treasury policies to prepare analysis supporting risk decisions.

For treasurers and CFOs, effective risk management depends on timely exposure visibility, appropriate mitigation strategies, and supporting hedge documentation. The function covers exposure identification, aggregation and netting, hedge execution support, confirmation matching, and hedge-accounting documentation. Treasury teams own risk identification and hedge strategy decisions, while accounting teams own financial reporting treatment and hedgeaccounting entries.

Teams involved

FX risk managers, treasurers, treasury analysts, treasury accountants, corporate accounting teams, financial controllers, risk management teams, legal teams, and internal audit teams manage FX and interestrate risk activities.

What AI helps with

AI can support treasury risk decisions by improving exposure visibility, identifying inconsistencies, and preparing analysis from multiple financial sources.

Multi-source aggregation can combine forecast cash flows, balance-sheet exposures, debt records, derivative positions, and market data into a consolidated exposure view. Entity resolution can map exposures across legal entities, currencies, counterparties, and financial instruments. Classification can categorize exposures by currency, maturity, risk type, and hedge relationship. Reconciliation analysis can compare derivative confirmations, trade records, and treasury systems to identify mismatches. Scenario analysis can evaluate potential exposure changes under approved currency and interestrate assumptions. Document intelligence can support hedge documentation preparation by extracting relevant information from approved hedge records, confirmations, and designation documents.

What humans continue to own

FX risk managers and treasurers continue to determine hedge strategies, approve hedge transactions, evaluate risk tolerance, and select appropriate mitigation approaches. Treasury accountants and controllers continue to determine accounting treatment, approve hedgeaccounting documentation, and complete financial reporting requirements.

AI identifies exposures, prepares analysis, and supports documentation, but authorized personnel continue to approve hedge decisions, accept market risk, and attest accounting compliance.

Key artifacts

• FX exposure reports
• Balance sheet exposure records
• Cash flow forecasts
• Hedge policy documents
• Derivative trade confirmations
• ISDA agreements
• Hedge designation memos
• Hedge effectiveness documentation
• Market rate data
• Debt schedules

Systems involved

• Treasury management system
• ERP systems
• Riskmanagement platforms
• Trading platforms
• Market-data platforms
• Bank and broker platforms
• Accounting systems
• Document repositories
• Reporting platforms

Regulatory and control considerations

FX and interestrate risk management requires controls over derivative authorization, counterparty exposure, hedge documentation, and accounting evidence.

Key controls include:

• Approved hedge policies
• Counterparty authorization controls
• Trade approval workflows
• Confirmation matching controls
• Hedge documentation retention
• Separation between trade execution and accounting review

Applicable standards include:

• ASC 815 hedge accounting (US-specific)
• IFRS 9 hedge accounting (IFRS-related)
• Dodd-Frank derivatives reporting (US-specific)
• EMIR derivatives reporting (EU-specific)
• Internal hedge policies (Internal policy based)

AI must not independently execute hedges, select counterparties, or determine hedge-accounting treatment.

Accountable roles

FX risk manager, treasurer, assistant treasurer, treasury analyst, treasury accountant, corporate controller, legal counsel, risk manager, and internal auditor.

Highest-value opportunities

• Exposure identification and aggregation: High value because treasury decisions depend on accurate visibility into currency and interest rate exposures across entities and systems.
• Derivative confirmation matching: High value because mismatches between trade records and confirmations can create operational and accounting risks.
• Hedge documentation preparation: High value because hedgeaccounting requirements involve recurring evidence preparation and documentation controls.

Example agentic workflow: FX exposure analysis and hedge-support preparation

Agent role: Prepare an FX exposure review package to support treasury risk analysis.

Starting artifacts:

• Cash flow forecasts
• Balance sheet exposure records
• Debt schedules
• Derivative trade confirmations
• Hedge policies
• Marketrate data

Workflow:

1. The workflow begins during a scheduled FX exposure review cycle.
2. The agent retrieves exposure data, forecast information, derivative records, and approved hedge policies.
3. The agent retrieves hedge thresholds, risk limits, and reporting requirements.
4. The agent consolidates exposures by entity, currency, maturity, and risk category.
5. The agent identifies exposure changes, unmatched records, and potential hedge requirements.
6. The FX risk manager reviews findings and determines appropriate actions.
7. Approved decisions are handed off through existing treasury processes with exposure analysis, approvals, and evidence retained.

Exception handling:

The workflow routes incomplete exposure records, confirmation mismatches, policylimit breaches, and inconsistent hedge documentation to responsible reviewers.

Human checkpoint:

The FX risk manager confirms exposure interpretation and approves hedgerelated decisions before execution.

Output:

An FX exposure review package containing exposure analysis, hedgesupport materials, reviewer decisions, and audit evidence.

Function 8: Bank relationship and fee management

Turning banking activity, service information, and fee data into controlled relationshipmanagement decisions.

Function overview

Bank relationship and fee management supports treasury’s interaction with banking partners by analyzing banking services, fee structures, service performance, and relationship information. The function receives bank fee statements, service records, banking agreements, account activity information, and treasury requirements, then prepares analysis used for banking decisions.

As organizations operate across multiple banking partners, treasury teams need visibility into service utilization, pricing accuracy, and relationship performance. Effective bank relationship management can support better service decisions, improved cost visibility, and stronger alignment between banking structures and treasury strategy.

Treasury teams own banking relationship management, service evaluation, and fee analysis. Procurement remains responsible for sourcing processes and commercial negotiation activities.

Teams involved

Treasury relationship managers, treasurers, treasury analysts, procurement teams, bank relationship managers, treasury technology teams, finance teams, and internal audit teams manage bank relationship activities.

What AI helps with

AI can support banking decisions by analyzing fee information, service records, and contractual data.

Document intelligence can extract information from bank fee statements, service agreements, and banking documentation. Reconciliation analysis can compare EDI 822 and camt.086 fee statements against approved fee schedules to identify discrepancies. Classification can categorize banking services, fee types, and service issues. Natural-language generation can prepare relationship-review materials, treasury committee summaries, and bankingservice assessments.

What humans continue to own

Treasurers and bank relationship managers continue to evaluate banking relationships, approve commercial decisions, select banking partners, and determine relationship strategies.

AI analyzes banking information, prepares comparisons, and identifies exceptions, but authorized treasury personnel continue to make relationship and commercial decisions.

Key artifacts

• Bank fee statement analysis
• EDI 822 fee records
• camt.086 fee reports
• Fee schedules
• Banking agreements
• Service level agreements
• Bank performance reports
• Banking RFP documents
• Relationship review materials

Systems involved

• Treasury management system
• Bank portals
• Banking analytics platforms
• Contract repositories
• Procurement platforms
• Document management systems
• Reporting platforms

Regulatory and control considerations

Bank relationship management requires controls over fee validation, contractual information, and banking access.

Key controls include:

• Fee approval controls
• Contract access restrictions
• Banking service review processes
• Documentation retention
• Separation between analysis and commercial approval

Applicable considerations include:

• Internal treasury policies (Internal policy based)
• Banking agreements (Contractual)
• Procurement policies (Internal-policy-based)

Accountable roles

Treasurer, assistant treasurer, treasury relationship manager, treasury analyst, procurement lead, legal counsel, finance lead, and internal auditor.

Highest-value opportunities

• Bank fee statement reconciliation: High value because treasury teams need visibility into banking costs and pricing discrepancies across complex service structures.
• Service-quality review preparation: High value because treasury leadership needs consistent visibility into banking performance.
• RFP support preparation: High value because banking evaluations require analysis of complex service requirements and commercial information.

Example agentic workflow: Bank fee analysis and relationship review preparation

Agent role: Prepare a bank relationship review package by analyzing banking fees, services, and performance information.

Starting artifacts:

• EDI 822 fee statements
• camt.086 fee reports
• Bank fee schedules
• Banking agreements
• Serviceperformance records
• Account activity data

Workflow:

1. The workflow begins during a scheduled bankfee review cycle.
2. The agent retrieves fee statements, account activity information, service records, and approved fee schedules.
3. The agent retrieves treasury banking policies and relationship-review criteria.
4. The agent compares charged fees against agreed pricing and identifies discrepancies.
5. The agent classifies service issues and prepares relationship-review materials.
6. Treasury teams review findings and determine whether escalation or commercial discussion is required.
7. Approved review outputs are retained with supporting evidence under treasury governance controls.

Exception handling:

The workflow routes fee discrepancies, contract conflicts, unresolved service issues, and missing documentation to responsible reviewers.

Human checkpoint:

The treasury relationship manager reviews findings before engaging banks or making relationship decisions.

Output:

A bank relationship review package containing fee analysis, service observations, exceptions, decisions, and supporting evidence.

Function 9: Treasury accounting and compliance management

Turning treasury transactions, valuations, and regulatory requirements into controlled accounting and compliance evidence.

Function overview

Treasury accounting and compliance management connects treasury activities with financial reporting, accounting processes, regulatory obligations, and internal control frameworks. The function receives derivative transactions, investment positions, debt information, valuation data, treasury policies, and supporting documentation, then prepares accounting entries and reconciliations, hedge-accounting documentation, compliance evidence, and exception reports.

For treasurers and CFOs, this function is critical because treasury decisions directly influence financial reporting, risk disclosure, and control effectiveness. As treasury operations become more complex, organizations require consistent evidence showing how transactions were analyzed, documented, and reviewed.

The function covers mark-to-market activities, hedge-accounting support, derivative regulatory reporting, treasury policy compliance monitoring, and exception management. Treasury teams provide transaction information and supporting documentation, while corporate accounting and controllers remain responsible for accounting treatment, financial reporting, and formal attestations.

Teams involved

Treasury accountants, treasurers, treasury analysts, corporate controllers, financial reporting teams, accounting teams, compliance teams, risk teams, legal teams, and internal audit teams manage treasury accounting and compliance activities.

What AI helps with

AI can support treasury accounting decisions by improving how transaction records, documentation, and compliance requirements are analyzed.

Document intelligence can extract information from derivative agreements, trade confirmations, hedge documentation, and accounting support records to prepare review materials. Reconciliation analysis can compare treasury transactions, accounting records, valuations, and reporting outputs to identify inconsistencies requiring investigation. Classification can organize treasury policy exceptions, compliance events, and reporting requirements. Policy-grounded retrieval can retrieve approved accounting policies, hedge-accounting guidance, and treasury controls to support review activities. Natural-language generation can prepare accounting support summaries, compliance explanations, and exception reports from approved treasury information.

What humans continue to own

Treasury accountants and corporate controllers continue to determine accounting treatment, approve accounting entries, complete financial reporting activities, and attest to compliance requirements. Treasury leadership continues to approve policy exceptions and risk-related decisions.

AI analyzes treasury records, prepares documentation, and identifies exceptions, but authorized personnel continue to approve accounting treatment, attest compliance, and accept financial reporting responsibility.

Key artifacts

• Mark-to-market reports
• Hedge accounting entries
• Derivative trade records
• Derivative confirmations
• Hedge designation memos
• Hedge effectiveness documentation
• Treasury policy documents
• Compliance reports
• Exception reports
• Regulatory reporting records

Systems involved

• Treasury management system
• ERP and accounting platforms
• Derivative management platforms
• Risk management platforms
• Market-data platforms
• Document repositories
• Compliance and GRC platforms
• Reporting platforms

Regulatory and control considerations

Treasury accounting and compliance require controls over transaction documentation, accounting treatment, regulatory reporting, and evidence retention.

Key controls include:

• Approval controls for accounting entries
• Hedge documentation requirements
• Reconciliation procedures
• Policy exception management
• Regulatory reporting validation
• Audit evidence retention
• Segregation of duties between transaction execution and accounting review

Applicable standards include:

• ASC 815 hedge accounting (US-specific)
• IFRS 9 hedge accounting (IFRS-related)
• EMIR derivative reporting (EU-specific)
• Dodd-Frank derivative reporting (US-specific)
• SOX financial control requirements (US-specific, where applicable)
• Treasury policy compliance requirements (Internal-policy-based)

AI must not independently:

• Post treasury accounting entries
• Certify compliance
• Submit regulatory reports
• Approve treasury policy exceptions

Accountable roles

Treasury accountant, corporate controller, treasurer, treasury analyst, compliance officer, financial reporting lead, risk manager, and internal auditor.

Highest-value opportunities

• Hedge documentation preparation: High value because hedge-accounting requirements involve recurring documentation, evidence collection, and accounting review.
• Treasury policy compliance monitoring: High value because treasury teams must continuously evaluate activity against approved limits and controls.
• Derivative reporting preparation: High value because regulatory reporting requires accurate aggregation of complex transaction data.

Example agentic workflow: Hedge accounting documentation preparation

Agent role: Prepare a hedgeaccounting documentation package by consolidating derivative records and supporting evidence.

Starting artifacts:

• Derivative trade confirmations
• Hedge designation memos
• Hedge policies
• Exposure records
• Accounting guidance documents
• Valuation reports

Workflow:

1. The workflow begins when a hedgeaccounting review cycle is initiated.
2. The agent retrieves derivative records, hedge documentation, exposure information, and accounting requirements.
3. The agent retrieves approved hedgeaccounting policies and documentation standards.
4. The agent compares hedge records against required documentation fields and prepares a review package.
5. The agent identifies missing evidence, documentation inconsistencies, and potential exceptions.
6. Treasury accounting and controller teams review documentation completeness and determine required actions.
7. Approved documentation is retained with supporting evidence under treasury accounting controls.

Exception handling:

The workflow routes missing designation information, incomplete confirmations, unsupported documentation, and policy exceptions to responsible accounting and treasury reviewers.

Human checkpoint:

Treasury accountants and controllers validate hedge documentation before accounting treatment or reporting activities proceed.

Output:

A hedgeaccounting review package containing supporting records, identified gaps, reviewer decisions, and audit evidence.

Function 10: Treasury reporting and governance

Treasury reporting and governance consolidates treasury data into standardized reports, management insights, and oversight mechanisms that support informed decision-making, accountability, and control.

Function overview

Treasury reporting and governance provides visibility into liquidity position, funding activities, investment compliance, debt obligations, risk exposure, and treasury performance. The function receives information from treasury operations, financial systems, banking platforms, and risk-management processes, then develops and maintains reporting dashboards, treasury committee materials, compliance reports, and governance documentation.

For treasurers and CFOs, treasury reporting provides the connection between operational treasury activity and strategic financial decisions. Reliable reporting helps leadership evaluate liquidity resilience, funding requirements, investment decisions, financial risk, and control effectiveness.

The function covers daily liquidity dashboards, treasury committee packs, investment policy compliance reports, debt covenant certificates, and governance reporting activities.

Teams involved

Treasurers, assistant treasurers, treasury analysts, treasury reporting teams, treasury operations teams, corporate controllers, CFO organizations, risk teams, compliance teams, and internal audit teams.

What AI helps with

AI can support treasury reporting by connecting financial information across treasury systems and preparing decisionsupport materials.

Multi-source aggregation can combine liquidity data, debt information, investment records, risk information, and compliance results into structured reporting packages. Variance and driver analysis can identify changes in liquidity position, funding requirements, investment exposure, and treasury performance indicators. Natural-language generation can prepare treasury committee summaries, liquidity commentary, and governance updates using approved financial information. Policy-grounded retrieval can compare treasury activity against investment policies, authority limits, and governance requirements.

What humans continue to own

Treasurers and treasury leadership continue to interpret financial conditions, communicate treasury strategy, approve governance decisions, and determine responses to liquidity or risk events. CFOs and executive leaders continue to make strategic financial decisions based on treasury insights.

AI aggregates information, identifies trends, and prepares reporting materials, but authorized personnel continue to provide governance oversight and make financial decisions.

Key artifacts

• Daily liquidity dashboard
• Treasury committee pack
• Investment policy compliance report
• Debt covenant certificate
• Counterparty exposure report
• Treasury performance reports
• Liquidity summaries
• Treasury policy documents
• Governance meeting records

Systems involved

• Treasury management system
• ERP systems
• Reporting and analytics platforms
• Investment management systems
• Debt management platforms
• Risk management platforms
• Document repositories
• Governance, risk, and compliance platforms

Regulatory and control considerations

Treasury reporting and governance requires controls over reporting accuracy, evidence retention, policy compliance, and executive decision support.

Key controls include:

• Reportingsource validation
• Approval of governance materials
• Policy compliance monitoring
• Controlled distribution of treasury reports
• Evidence retention
• Version management of reporting packages

Applicable considerations include:

• Investment policies (Internal policy based)
• Debt agreements (Contractual)
• SOX financial controls (US-specific, where applicable)
• Treasury governance requirements (Internal-policy-based)

Accountable roles

Treasurer, assistant treasurer, treasury reporting lead, treasury analyst, CFO, corporate controller, risk manager, compliance officer, and internal auditor.

Highest-value opportunities

• Daily liquidity dashboard preparation: High value because treasury leaders require timely visibility into enterprise cash positions and liquidity conditions.
• Treasury committee pack preparation: High value because governance reporting requires consolidation of information from multiple treasury activities.
• Investment policy compliance reporting: High value because investment decisions must remain within approved limits and require documented evidence.

Example agentic workflow: Treasury committee reporting preparation

Agent role: Prepare a treasury committee reporting package by consolidating liquidity, risk, and governance information.

Starting artifacts:

• Daily liquidity dashboard
• Cash position reports
• Forecast reports
• Investment positions
• Debt reports
• Policy compliance reports
• Counterparty exposure reports

Workflow:

1. The workflow begins when the treasury committee reporting cycle starts.
2. The agent retrieves approved treasury reports, financial data, risk information, and policy documents.
3. The agent retrieves committee templates, reporting requirements, and governance guidelines.
4. The agent consolidates treasury information and prepares reporting summaries.
5. The agent identifies material changes, policy exceptions, and emerging risks.
6. Treasury leadership reviews the committee package and determines required actions.
7. Approved reporting materials are distributed through existing governance processes with supporting evidence retained.

Exception handling:

The workflow routes missing reports, inconsistent metrics, policy breaches, and unresolved data-quality issues to responsible treasury teams.

Human checkpoint:

The treasurer reviews and approves treasury committee materials before executive distribution.

Output:

A treasury committee package containing liquidity reporting, risk summaries, compliance observations, decisions, and audit evidence.

High-value AI use cases in treasury management

AI opportunities in treasury management should be prioritized based on their ability to improve financial visibility, reduce manual preparation effort, strengthen controls, and support better treasury decisions. The objective is not to identify the largest number of possible AI applications. It is to identify workflows where AI can improve the quality and speed of treasury analysis while preserving accountability for financial decisions.

For treasury managers, assistant treasurers, cash managers, and TMS owners, the strongest AI opportunities are typically those that extend existing treasury technology investments. The most valuable workflows are not isolated AI applications, but connected capabilities that improve how treasury teams use existing systems of record. By combining information across TMS platforms, ERP systems, banking channels, payment systems, and policy repositories, AI can help treasury teams improve visibility, identify exceptions earlier, and support better financial decisions.

A high-value treasury AI opportunity typically has:

• A clearly defined treasury decision or review activity.
• Reliable source artifacts available in treasury or enterprise systems.
• A measurable operational baseline.
• A defined human review boundary.
• A controlled impact if the AI output requires correction.

The following use cases represent examples of high-value AI opportunities across the treasury management operating model.

How agentic AI works in treasury management workflows

Agentic AI can support treasury teams by coordinating multiple workflow steps around a defined financial objective. Unlike traditional automation that follows fixed rules and predefined sequences, agentic AI can retrieve information from approved systems, analyze financial records, apply treasury policies, prepare review materials, identify exceptions, and route decisions to accountable reviewers.

For treasury organizations, this capability is particularly relevant because critical decisions rarely depend on a single system. A cash manager reviewing liquidity may need bank statements, TMS balances, payment activity, forecast information, and liquidity policies. A payment operations lead reviewing payment release may need payment files, beneficiary records, sanctions results, approval matrices, and funding information. A treasurer evaluating financial risk may need exposure data, debt information, derivative records, market data, and policy requirements.

Agentic workflows help connect these activities while maintaining clear decision boundaries. AI can prepare analysis, identify exceptions, and assemble evidence. Treasury professionals continue to approve funding actions, payment releases, hedge decisions, investment actions, accounting treatments, and regulatory obligations.

Here are some examples:

Example 1: Daily cash positioning and liquidity review

Agent role: Prepare the daily global cash position package and identify liquidity exceptions requiring cash manager review.

Starting artifacts:

• BAI2 bank statements
• MT940 bank statements
• CAMT.052 intraday statements
• CAMT.053 end-of-day statements
• TMS settlement records
• Scheduled AP payment files
• Current liquidity forecast
• Target balance policies
• Sweep instructions

Workflow:

1. The workflow begins when prior-day bank statements arrive from approved banking connections.
2. The agent retrieves statement balances, transaction activity, expected settlements, scheduled payments, and liquidity forecast inputs from approved systems.
3. The agent retrieves target balance policies, sweep structures, and liquidity decision rules.
4. The agent prepares a consolidated global cash position by entity, bank, and currency.
5. The agent identifies:
   • Unexpected bank movements
   • Accounts below target balances
   • Missing statements
   • Unmatched transactions
   • Forecast variances
6. The cash manager reviews the liquidity analysis, validates funding requirements, and determines required treasury actions.
7. Approved decisions proceed through existing treasury controls, with position data, exceptions, approvals, and review evidence retained.

Exception handling:

The workflow routes:

• Missing bank statements
• Unexpected debits
• Balance inconsistencies
• Unmatched transactions
• Sweep exceptions

to responsible treasury reviewers.

Human checkpoint:

The cash manager confirms liquidity decisions before any funding transfer or treasury action proceeds.

Output:

A reviewed daily cash position package containing:

• Consolidated cash balances
• Liquidity observations
• Exception analysis
• Funding recommendations
• Reviewer decisions
• Audit evidence

Example 2: Liquidity forecasting and scenario analysis

Agent role: Prepare a liquidity forecast review package using operational cash-flow inputs.

Starting artifacts:

• 13-week direct forecast
• AP payment schedules
• AR collection forecasts
• Payroll forecasts
• Tax flow forecasts
• Treasury cash flow records
• Forecast versus actual variance reports
• Liquidity scenario assumptions

Workflow:

1. The workflow begins during the scheduled treasury forecasting cycle.
2. The agent retrieves approved cashflow inputs from ERP systems, planning platforms, and treasury systems.
3. The agent retrieves forecasting methodologies, approved assumptions, and scenario requirements.
4. The agent analyzes:
   • Historical cash movements
   • Forecast assumptions
   • Actual-versus-forecast variance
   • Liquidity drivers
5. The agent prepares:
   • Updated liquidity projections
   • Forecast variance analysis
   • Scenario comparisons
6. Treasury analysts review assumptions, validate forecast drivers, and determine whether adjustments are required.
7. Approved forecast updates are recorded with supporting evidence.

Exception handling:

The workflow routes:

• Missing forecast inputs
• Unsupported assumptions
• Significant forecast deviations
• Material liquidity changes

to responsible business and treasury teams.

Human checkpoint:

Treasury analysts validate forecast drivers before updated projections are used for liquidity decisions.

Output:

A liquidity forecasting package containing:

• Updated projections
• Variance analysis
• Scenario outputs
• Reviewer decisions
• Supporting evidence

Example 3: Payment fraud and release-readiness review

Agent role: Prepare a payment review package by analyzing payment queues, beneficiary information, and treasury control requirements before authorized release.

Starting artifacts:

• ISO 20022 pain.001 payment files
• NACHA payment files
• Wire transfer instructions
• Beneficiary master records
• Bank-account information
• Sanctions screening results
• Payment approval matrices
• Treasury payment policies

Workflow:

1. The workflow begins when approved payment files enter the treasury payment queue.
2. The agent retrieves:
   • Payment details
   • Beneficiary information
   • Approval status
   • Screening results
   • Funding availability
3. The agent retrieves payment authority rules, sanctions requirements, and treasury policies.
4. The agent analyzes payment activity for:
   • First-time beneficiaries
   • Bankchange anomalies
   • Unusual payment patterns
   • Cutoff risks
   • Missing approvals
5. The agent prepares a payment-review package and routes exceptions to appropriate teams.
6. Payment operations personnel review findings and confirm paymentrelease decisions.
7. Approved payments proceed through existing dual-control payment processes with evidence retained.

Exception handling:

The workflow routes:

• Sanctions alerts
• Beneficiary inconsistencies
• Missing approvals
• Paymentformat errors
• Funding issues

to responsible reviewers.

Human checkpoint:

Authorized payment operations personnel approve payment release before execution.

Output:

A paymentreview package containing:

• Payment validation results
• Risk indicators
• Exception cases
• Approval decisions
• Audit evidence

Agentic AI creates value in treasury operations when workflows are designed around financial decisions rather than isolated automation tasks. The most effective workflows connect treasury artifacts, systems, policies, and accountable reviewers while maintaining clear boundaries around financial authority.

The review boundary is the safety property. AI can connect systems, assemble evidence, identify exceptions, and prepare recommendations. Treasury professionals continue to approve financial actions and remain accountable for treasury outcomes.

How to prioritize AI use cases in treasury management

Treasury organizations should prioritize AI use cases according to their business impact, implementation feasibility, data readiness, and governance requirements. The aim is not to pursue the greatest number of AI applications, but to focus on workflows where AI can deliver measurable value by improving visibility, reducing manual preparation, strengthening controls, and enabling better-informed financial decisions.

A strong treasury AI investment case connects:

• The treasury outcome the organization wants to improve.
• The sub-process where AI can provide support.
• The controls required for deployment.

For example, a payment anomaly detection workflow should not be evaluated only by detection accuracy. Treasury leaders should also consider payment volume, availability of beneficiary history, integration requirements with payment systems, existing fraud controls, and whether authorized personnel can validate the output before release.

Treasury AI prioritization criteria

Treasury AI maturity journey

Treasury organizations should approach AI adoption as a progression from improving information visibility to scaling governed workflows.

Stage 1: Improve treasury visibility

Objective: Establish a trusted treasury information foundation by improving connectivity across treasury systems, banking platforms, and enterprise financial data sources.

Typical use cases:

• Cash position preparation
• Bank statement reconciliation
• Liquidity dashboards
• Treasury reporting preparation

Key outcomes:

• Faster liquidity visibility
• Reduced manual consolidation effort
• More consistent treasury reporting
• Improved enterprise cash visibility
• Reduced manual consolidation effort
• Stronger foundation for treasury transformation

Stage 2: Improve treasury decision support

Objective: Improve liquidity, risk, and funding decisions by applying AI to connected treasury data, forecasts, exposures, and financial scenarios.

Typical use cases:

• Cash forecasting
• Forecast variance analysis
• Liquidity scenario analysis
• FX exposure analysis
• Counterparty monitoring

Key outcomes:

• Better forecast accuracy
• Earlier risk identification
• Improved treasury planning
• Better support for treasurer and CFO decisions around liquidity, funding, and financial resilience

Stage 3: Scale governed treasury workflows

Objective: Scale connected treasury workflows across existing technology ecosystems while maintaining financial controls, approval boundaries, and audit evidence.

Typical use cases:

• Payment review workflows
• Exception management
• Policy validation
• Cross-system treasury analysis
• Governance reporting

Key outcomes:

• Faster exception resolution
• Stronger control execution
• Scalable treasury operations
• Greater scalability across global treasury operations
• More consistent execution of treasury processes across regions and entities

The strongest AI investments in treasury management typically begin with high-volume, artifact-rich, clearly governed sub-processes. These workflows allow organizations to establish measurable baselines, validate AI performance, maintain human accountability, and expand toward broader treasury transformation.

Governance, risk, and responsible AI in treasury management

AI in treasury management operates across financial records, payment workflows, banking systems, investment activities, derivative processes, and regulatory obligations. Because treasury decisions directly affect liquidity, financial reporting, and enterprise risk, governance must be designed into AI workflows from the beginning rather than added after deployment.

For treasury leaders, responsible AI is not only about controlling model outputs. It is about establishing clear accountability for decisions, defining permitted system actions, protecting sensitive financial information, maintaining audit evidence, and ensuring that AI workflows operate within existing treasury control frameworks.

A governed treasury AI operating model should answer five questions:

• What information can AI access?
• What analysis or recommendations can AI prepare?
• Which treasury role reviews the output?
• Which actions require human approval?
• What evidence is retained for audit and compliance?

Human-in-the-loop oversight

Every treasury AI workflow should define what AI may analyze, classify, retrieve, prepare, or recommend and which authorized role must confirm the result.

Treasury decisions involving financial risk, payment execution, funding, investments, derivatives, accounting treatment, or regulatory obligations require human accountability.

Examples include:

• A cash manager reviews liquidity recommendations before approving funding actions.
• A payment operations lead confirms payment validity before release.
• An FX risk manager reviews exposure analysis before approving hedge actions.
• A treasury accountant and controller validate hedge-accounting documentation and accounting treatment.
• A treasurer approves policy exceptions and strategic treasury decisions.

AI can support treasury professionals by preparing information, identifying patterns, and highlighting exceptions. It does not replace the individuals responsible for accepting financial risk or approving treasury actions.

AI detects, forecasts, compares, and prepares recommendations, but authorized personnel continue to approve transactions, accept risk, attest compliance, and release instructions.

Regulatory and standards alignment

Treasury AI governance must align workflow controls with applicable financial regulations, accounting standards, payment requirements, and internal treasury policies.

Because treasury operates across multiple jurisdictions, regulatory requirements should be evaluated based on their applicable scope.

AI governance should complement existing treasury frameworks rather than introduce separate control processes.

For example:

• A payment review workflow should align with existing payment approval matrices, sanctions requirements, and segregation-of-duties controls.
• A hedge support workflow should align with approved hedge policies and accounting documentation requirements.
• A liquidity forecasting workflow should align with approved forecast methodologies and treasury reporting practices.

Bias mitigation and evidence retention

Treasury AI workflows should maintain traceability between recommendations and the financial information used to produce them.

For example:

A liquidity recommendation should identify:

• Source bank balances
• Forecast inputs
• Treasury assumptions
• Policy rules applied
• Exceptions identified

A paymentrisk recommendation should identify:

• Payment records analyzed
• Beneficiary information reviewed
• Historical transaction patterns
• Screening results
• Approval requirements

A hedgesupport workflow should identify:

• Exposure records
• Derivative information
• Hedge documentation
• Accounting requirements

Organizations should retain evidence supporting AI-generated outputs, including:

• Source artifacts
• Data sources accessed
• Retrieved policies
• Workflow version
• Generated analysis
• Reviewer decisions
• Approvals
• Overrides
• Resulting system updates

This evidence enables treasury teams, controllers, auditors, and compliance functions to validate how decisions were prepared and reviewed.

Key governance requirements

Treasury AI workflows should be inventoried and classified based on their operational and financial risk. A reporting summary workflow should not have the same governance requirements as a payment review workflow or a hedge support workflow.

Treasury governance should define:

• AI workflow inventory
• Risk classification
• Approved data sources
• User access requirements
• Permitted system actions
• Approval gates
• Escalation paths
• Monitoring requirements
• Exception-handling procedures
• Audit evidence requirements

For treasury workflows involving financial authority, governance should explicitly include:

Segregation of duties

AI workflows should not remove separation between:

• Preparation and approval
• Payment creation and payment release
• Trade execution and accounting review
• Data preparation and financial attestation

Dual-control requirements

Payment-related workflows should preserve required dual approvals and authorization controls.

Transaction and authority limits

AI workflows should operate within defined:

• Payment thresholds
• Funding limits
• Investment limits
• Counterparty limits
• User permissions

Sanctions and payment controls

Payment workflows should continue to enforce:

• Sanctions screening
• Beneficiary validation
• Bank detail verification
• Exception escalation

Emergency suspension controls

Organizations should define conditions under which treasury AI workflows can be paused or disabled.

Examples:

• Unexpected system behavior
• Incorrect data access
• Policy violations
• Excessive exception volume
• Unauthorized actions

Actions AI must never perform autonomously

For treasury management, AI should not independently:

• Release a payment
• Change beneficiary instructions
• Execute a hedge
• Borrow funds
• Invest funds
• Open or close a bank account
• Modify a signatory
• Approve a treasury policy exception
• Certify a debt covenant
• File a regulatory report
• Post a treasury accounting entry

AI can prepare analysis, identify exceptions, and assemble evidence. Authorized treasury personnel remain responsible for consequential financial actions.

Design principles

Treasury AI workflows should be designed around approved information sources, least-privilege access, and controlled system interactions.

Key design principles include:

• Ground AI outputs in approved treasury information

AI recommendations should rely on:

• Approved financial records
• Treasury policies
• Banking information
• Contractual documents
• Accounting guidance
• Risk management records

Apply least-privilege access

AI workflows should only access:

• Required systems
• Required data
• Approved entities
• Approved accounts
• Approved currencies

Separate read and write permissions

Many treasury AI workflows should begin with read-only access.

Examples:

AI may:

• Retrieve bank statements
• Analyze payment queues
• Compare transactions
• Prepare reports
• Identify exceptions

AI should not:

• Release payments
• Change banking instructions
• Modify financial records without approval

Maintain explicit review boundaries

Each workflow should define:

• What AI prepares
• What humans review
• What humans approve
• What actions remain unavailable to AI

Traceability and data security

Treasury AI requires complete visibility into how outputs were generated.

Audit trails should capture:

• Input artifacts used
• Systems accessed
• Policies retrieved
• Workflow version
• AI-generated output
• Reviewer decisions
• Approvals
• Exceptions
• System updates

Because treasury workflows involve sensitive financial information, organizations should apply appropriate controls for:

• Authentication
• Authorization
• Data protection
• Secure transmission
• Access monitoring
• Retention requirements

For example, a governed payment-review workflow should provide evidence of:

• Payment file analyzed
• Beneficiary information reviewed
• Screening checks performed
• Exceptions identified
• Reviewer approval recorded
• Final payment release completed through authorized processes

A governed treasury AI operating model enables organizations to scale AI while maintaining financial accountability. The goal is not autonomous treasury execution. It is stronger liquidity visibility, faster analysis, better exception management, and more consistent decision support while preserving the controls that treasury functions require.

How ZBrain operationalizes AI use cases in treasury management

Identifying AI opportunities in treasury management is only the first step. Treasury teams need a controlled way to analyze current workflows, define requirements, design integrations and review boundaries, build and validate solutions, deploy them, and govern them in operation.

ZBrain supports this lifecycle through four connected stages: ZBrain Analyzer, ZBrain Design, ZBrain Solution Builder, and ZBrain Governance. The platform provides a governed path from use-case analysis to deployed agentic workflows while maintaining policies, permissions, approval points, monitoring, and runtime evidence.

ZBrain Analyzer

ZBrain Analyzer helps teams examine treasury management processes, identify AI opportunities, and document the business context, systems, data, roles, controls, and review requirements needed to evaluate each use case.

ZBrain Design

ZBrain Design creates a build-ready technical design for the selected use case. It generates the BRD, functional requirements, user journeys, architecture, workflow logic, data details, integration context and governance considerations needed before development begins.

ZBrain Solution Builder

ZBrain Solution Builder enables teams to create, configure, and validate governed AI workflows for treasury management based on the technical designs developed in ZBrain Design. It supports testing across routine, exception, and control scenarios before deployment.

ZBrain Governance

ZBrain Governance applies policies, access controls, human approval requirements, monitoring, and traceability throughout workflow execution. It provides guardrails, approval gates, escalation controls, kill switches, and audit trails to maintain oversight of AI outputs, user actions, exceptions, and authorized system updates.

Future of AI in treasury management

The future of treasury AI will move beyond isolated automation toward connected financial intelligence platforms that combine orchestration, governance, identity, evidence, and observability across treasury ecosystems.

Traditional treasury operations often depend on scheduled reporting cycles, manual spreadsheet preparation, and analyst-driven exception review. As treasury organizations progress toward more mature operating models, AI will increasingly support connected workflows that combine TMS data, banking information, ERP records, market data, and policy requirements into more proactive decision-support environments. Future AI-enabled treasury environments will increasingly support event-driven visibility by connecting bank information, payment activity, forecasts, market data, and treasury policies into continuously updated decision support views.

This shift will become increasingly important as treasury organizations operate in more complex environments characterized by expanding banking relationships, larger numbers of legal entities and currencies, higher payment volumes, greater regulatory expectations, and growing demand for real-time liquidity visibility.

Longer-horizon agentic workflows can support treasury objectives across multiple processes. For example, an AI workflow may continuously monitor liquidity conditions, compare changes in forecasts, identify potential funding requirements, prepare supporting analysis, and route recommendations to authorized treasury reviewers.

However, these workflows should pause before any financial decision that requires professional judgment or formal authorization. Treasurers and other authorized treasury professionals must continue to approve funding decisions, payment releases, investment decisions, hedge actions, policy exceptions, and accounting attestations.

The greatest advantage will not come solely from selecting a more advanced AI model. It will come from designing treasury workflows around clearly defined decisions by identifying authoritative financial artifacts, connecting relevant systems, establishing permissions and review boundaries, testing exception scenarios, and maintaining evidence of decisions and approvals.

Organizations that successfully implement treasury AI will not be those that simply automate the greatest number of tasks. They will be those that build governed workflows that improve visibility, strengthen controls, and help treasury professionals make better decisions.

The future of AI in treasury management depends on better workflow design, connected enterprise context, and enforceable governance, not only better models.

Endnote

Treasury management is not a single financial process. It is a connected enterprise function spanning cash positioning, liquidity forecasting, payment controls, banking administration, liquidity structures, funding, investment management, financial risk, treasury accounting, and governance reporting.

AI can support treasury activities where work involves financial data aggregation, transaction analysis, exception identification, policy interpretation, documentation preparation, and reporting support. These capabilities can reduce manual preparation effort and help treasury professionals focus on decisions requiring financial judgment. For treasurers and CFOs, the broader opportunity is improved confidence in liquidity management, funding decisions, financial risk oversight and enterprise cash visibility.

The implementation challenge is precision: broad objectives such as “AI for treasury,” “AI for payments,” or “AI for forecasting” do not specify the financial artifacts, systems, controls, exception categories, authority requirements, or accountable reviewers needed for deployment. A practical treasury AI approach therefore begins with a bounded sub-process, identifies the required artifacts and systems, defines the human review boundary, validates the workflow against real exceptions, and expands only after accuracy, control effectiveness, and governance have been demonstrated.

The strongest treasury AI opportunities are those where AI improves financial visibility, prepares analysis, identifies exceptions, and assembles evidence while treasury professionals retain ownership of funding decisions, payment approvals, financial risk, and governance responsibilities. For organizations evaluating AI in treasury management, the priority is not replacing existing treasury platforms or financial expertise. It is extending the value of existing investments by applying AI where treasury teams spend significant effort preparing information before making decisions.

To explore how ZBrain can help analyze, design, build, and govern AI workflows across treasury management operations, contact the ZBrain team today.