Artificial Intelligence in Treasury Management Training Course

Participants can assess whether treasury data is suitable for AI analysis and model development.

• Data lineage from ERP, bank statements, TMS and market feeds
• ISO 20022 camt and pain messages assessed for cash-data usability
• Master-data controls for entities, bank accounts, currencies and counterparties
• Missing values, duplicates and timing breaks diagnosed before model training
• Feature definitions document calendar effects, payment terms and cash drivers
• Data quality scorecards assign owners, thresholds, exceptions and remediation dates

Participants can evaluate machine-learning cash forecasts against treasury forecasting requirements and baseline methods.

• 13-week rolling cash forecasts structured by entity, currency and horizon
• Baseline forecast accuracy measured with MAE, RMSE, MAPE and bias
• Regression, random forest and gradient boosting compared against baseline methods
• Feature engineering captures seasonality, payroll cycles, invoices and collections
• Backtesting separates training, validation and holdout periods without data leakage
• Prediction intervals translate uncertainty into liquidity buffers and funding actions

Participants can interpret AI-supported liquidity recommendations within funding, pooling and policy constraints.

• Daily cash positioning combines bank balances, intraday flows and forecast receipts
• Cash concentration recommendations respect pooling structures, trapped cash and limits
• Working-capital signals identify late collections, payment runs and unusual outflows
• Scenario trees test rate shocks, delayed receipts and supplier disruptions
• Liquidity-at-risk estimates quantify adverse cash positions at stated confidence levels
• Funding decisions compare overdrafts, revolvers, deposits and money-market alternatives

Participants can design AI-assisted payment monitoring with practical investigation and escalation controls.

• Payment anomaly detection scores beneficiaries, amounts, timing and approval patterns
• Supervised classification distinguishes confirmed fraud cases from legitimate exceptions
• Unsupervised clustering identifies novel payment behaviour without labelled outcomes
• False-positive rates calibrated against investigation capacity and payment urgency
• Alert triage routes cases using thresholds, evidence and escalation rules
• Segregation-of-duties controls remain effective when AI flags or recommends payments

Participants can assess AI-supported FX and risk signals without exceeding approved risk limits.

• FX exposure data aggregates forecast, committed and balance-sheet currency positions
• Time-series signals support hedge timing while preserving approved hedge policy
• Value at Risk compares parametric, historical simulation and Monte Carlo approaches
• Stress tests model currency gaps, basis movements and counterparty deterioration
• Hedge recommendations document objective, instrument, limits and residual exposure
• Explainability reports show drivers, sensitivities and confidence behind risk signals

Participants can review AI-supported investment and counterparty recommendations against treasury policy.

• Short-term investment allocation considers liquidity tiers, duration, yield and concentration
• Credit-scoring inputs combine ratings, spreads, financials and payment behaviour
• Counterparty limits monitored against exposure, collateral, wrong-way risk and concentration
• Optimisation constraints encode investment policy, eligible instruments and maturity limits
• Expected shortfall supplements Value at Risk for tail-loss assessment
• Recommendation logs preserve market data, assumptions, approvals and execution evidence

Participants can apply generative AI to treasury analysis while protecting data and maintaining review controls.

• Retrieval-augmented generation grounds treasury assistants in approved policy documents
• Prompt patterns extract covenant terms, bank fees and cash-report exceptions
• Source citations and confidence cues support review of generated outputs
• Sensitive data redaction protects account details, payroll and counterparty information
• Human review gates control drafting, analysis and external communications
• Prompt-injection tests expose unsafe instructions and unauthorised data requests

Participants can establish governance requirements for treasury AI models and automated recommendations.

• Model inventory records purpose, owner, inputs, outputs and decision impact
• Model risk tiering considers materiality, autonomy, complexity and data sensitivity
• Validation plans test performance, stability, bias and conceptual soundness
• Drift monitoring tracks feature shifts, accuracy decline and alert volumes
• NIST AI RMF functions organise governance, mapping, measurement and management
• Audit trails retain versions, approvals, overrides and exceptions for assurance

Participants can prepare a controlled treasury AI pilot and a practical 90-day implementation plan.

• Target operating model assigns treasury, technology, risk and audit responsibilities
• Pilot charters define decision scope, success metrics, controls and exit criteria
• Integration architecture connects TMS, ERP, banks, data platforms and dashboards
• Vendor due diligence examines data rights, security, support and model transparency
• Change plans address analyst adoption, procedure updates and control ownership
• 90-day application plan sequences quick wins, dependencies, funding and governance