AI in the Finance Function: Close Automation, Forecasting and Controls Training Course

Programme Summary

AI in the finance function is a five-day training course for CFOs, controllers and finance transformation leaders at advanced level, culminating in a finance AI roadmap with a control design for their own organisation. Finance teams adopt machine learning and generative AI faster than their controls adapt. The approach moves from use-case selection and data readiness to automation, prediction and controls over AI outputs. Mars Training Centre UK delivers this programme in AI for finance and accounting leaders.

The Business Case

Much finance capacity is still absorbed by matching, chasing and reformatting, leaving little time for forecasting and anomaly review. AI can remove manual effort and surface exceptions earlier, but each model and generated report must then be controlled, explained and evidenced like any other reporting process.

• Close Cycle Time: Shortens as reconciliations, accruals and journal reviews are automated with exception routing.
• Forecast Reliability: Improves as machine learning cash and revenue forecasts are back-tested and monitored.
• Control and Audit Exposure: Reduced as AI outputs are covered by documented controls, review evidence and audit trails.

Principles and Evidence Base

• COSO Internal Control - Integrated Framework: Its components structure the control design for AI-enabled finance processes in Session 4.
• COSO publication on internal controls over generative AI: Its application of the framework to generative AI guides the review and approval rules in Session 4.
• Machine Learning Forecasting and Back-Testing: Model comparison and error measurement underpin the cash and revenue forecasting work in Session 3.
• Anomaly Detection: Statistical and machine learning outlier methods frame the fraud and error analytics in Session 3.

Intended Participants

• Leaders accountable for finance cost and reporting quality.
• Controllers who own the financial close, reconciliations and internal control over reporting.
• Heads of financial planning and analysis responsible for forecasts presented to boards.
• Transformation leaders who select finance automation and AI platforms.

The programme serves banking, energy, retail and government entities.

Learning Outcomes

1. Participants will be able to evaluate finance processes for AI suitability, value and data readiness.
2. Participants will be able to design intelligent automation for accounts payable, reconciliations and close tasks.
3. Participants will be able to construct and validate machine learning forecasts for cash flow and revenue.
4. Participants will be able to diagnose fraud and error risks using anomaly detection outputs.
5. Participants will be able to justify controls, review evidence and model risk limits for AI outputs in financial reporting.
6. Participants will be able to prioritise a finance AI roadmap with business cases and accountabilities.

Programme Structure

Day 1: AI in the Finance Function and Use-Case Selection

Where does AI create value in finance, and what must be true of the data first?

• Machine Learning, Intelligent Automation and Generative AI Compared
• Finance Process Map and AI Use-Case Inventory
• Value, Feasibility and Risk Scoring of Finance Use Cases
• Master Data, Chart of Accounts and Data Quality Assessment
• Finance Data Architecture for AI Platforms

Day 2: Intelligent Automation of Transaction Processing

Which accounting tasks can be automated, and how are exceptions handled?

• Invoice Capture and Three-Way Matching with Machine Learning
• AI-Assisted Bank and Intercompany Reconciliations
• Automated Accruals and Journal Entry Suggestions
• Exception Routing, Confidence Thresholds and Human Review
• Close Task Orchestration and Automation Metrics

Day 3: Machine Learning Forecasting and Anomaly Detection

How reliable are AI forecasts and anomaly signals for finance decisions?

• Machine Learning Cash Flow Forecasting and Driver Selection
• Revenue Forecasting Models and Back-Testing
• Anomaly Detection for Duplicate Payments, Fraud and Errors
• Generative AI for Variance Commentary and Management Reports
• Explaining Model Outputs to Executives and Auditors

Day 4: Controls, Model Risk and Auditability

How are AI outputs controlled so that financial reporting remains reliable?

• COSO Internal Control - Integrated Framework Applied to AI Processes
• COSO Guidance on Controls over Generative AI
• Model Inventory, Validation and Performance Monitoring
• Audit Trails, Review Evidence and Segregation of Duties
• Data Privacy, Access Rights and Vendor Due Diligence

Day 5: Building the Finance AI Roadmap

Which AI initiatives should finance fund first, and how will they be governed?

• Prioritising Day 1 Use Cases by Value and Readiness
• Automation Pilots Selected from Day 2 Processes
• Day 4 Control Design Applied to Selected Pilots
• Skills, Operating Model and Change Plan for Finance Teams
• Finance AI Roadmap and Control Design Completion

Methodology

The programme uses a data workshop format with anonymised ledgers, payment files and forecast histories from banking, energy and retail organisations. Each session moves from a short briefing to hands-on analysis with AI tools, then tests results from a controller's and an auditor's viewpoint.

• Suggested activity: Back-testing a machine learning cash forecast against actual receipts.
• Suggested activity: Reviewing anomaly flags on a payment file and deciding which to investigate.
• Suggested activity: Designing controls for AI-drafted management commentary.

Capstone Output

Each participant completes a finance AI roadmap with a control design. It contains a scored use-case portfolio, automation and forecasting pilots, a model inventory, control and review requirements, and a skills plan. It can be presented to the audit committee to approve investment.

Frequently Asked Questions

Is AI in the finance function training suitable for CFOs without data science skills?

Yes. It is set at advanced level for finance leaders and assumes experience of reporting and controls; no programming is required.

Which frameworks does AI in finance and accounting training rest on?

It rests on the COSO Internal Control - Integrated Framework, COSO guidance on controls over generative AI, and established forecasting and anomaly detection methods.

How is the finance AI roadmap used at work?

You can use it to select and fund AI pilots, agree controls with auditors and track adoption.

How does AI in the finance function differ from AI-enabled internal audit courses?

It addresses how finance leaders design and control AI inside finance processes, rather than how auditors use or audit AI.