AI Training for Finance Professionals: Controls, Audit Trails and Numbers You Can Sign

This online course designed for finance professionals explores the essential aspects of artificial intelligence implementation in financial environments. The programme examines critical controls that must accompany AI systems, ensuring proper governance and risk management. Students learn about audit trails necessary for regulatory compliance and transparency. The curriculum covers numerical frameworks that finance specialists can verify and validate. Participants discover how to evaluate AI outputs using established financial metrics and reporting standards. The course addresses practical applications of AI within accounting, risk assessment, and financial planning processes. Learners examine real-world scenarios where artificial intelligence intersects with traditional finance operations. The training emphasizes the importance of maintaining accurate records and documentation throughout AI-driven financial activities.

The course structure includes fifteen detailed lessons, each followed by a graded quiz to reinforce learning objectives. A final examination assesses overall understanding of AI controls and financial validation processes. All course materials remain freely accessible to anyone interested in finance and artificial intelligence. Upon completion, participants gain practical skills for evaluating AI systems within financial contexts. They learn to identify potential risks and implement appropriate controls. The knowledge acquired enables finance professionals to make informed decisions about AI adoption. Graduates can confidently review AI-generated financial reports and maintain proper audit trails. This training prepares finance specialists to work effectively alongside artificial intelligence systems while maintaining regulatory compliance and data integrity.

Frequently asked questions

Can AI be used in financial reporting?

Yes, AI can be used in financial reporting, but with controls proportionate to materiality. AI is most appropriate for mechanical steps (data aggregation, extraction, calculation) and least appropriate for classification and valuation decisions that require accounting judgment. Any AI-processed figure in material financial statements must have documented controls showing that the AI was validated before use, is monitored in production and has undergone testing that the external auditor can review.

What controls are needed when AI processes invoices?

Invoice AI controls should include: rule-based validation of extracted amounts (checking line items sum correctly), weekly sample testing of at least 50 invoices to verify accuracy, automated matching to purchase orders with escalation for mismatches, vendor reconciliation at month end and defined thresholds for human review. The goal is to catch both false positives (incorrect matches) and false negatives (missed line items) before they affect payment.

How do auditors treat AI generated numbers?

External auditors treat AI-generated numbers as outputs of an automated process requiring documented controls. Before relying on an AI-generated figure, auditors expect to see evidence that the process was designed correctly, tested on representative data, deployed with appropriate oversight and monitored in production. Auditors will sample transactions and verify that AI output matches source documents. An AI number with no documented control environment is not auditable.

Is it safe to put financial data into a language model?

It depends on the data and the vendor. Putting customer pricing or confidential business information into a commercial language model API creates data exposure risk because vendors typically log and may retain your data. For sensitive information, use locally-run models (Llama, Mistral) that never leave your infrastructure, or use rule-based systems instead of language models. For non-sensitive data, negotiate appropriate Data Processing Agreements with the vendor before use.

How do you audit an AI forecasting model?

Forecasting model audits typically focus on: the data used for training (is it representative of your business?), the assumptions built into the model (are they still valid?), the model’s accuracy on held-out test data and its accuracy when applied to actual forecast periods. Also examine whether the model is reacting appropriately to known business changes. If a product launch occurred that the model’s training data could not anticipate, that is expected model degradation, not model failure.

What is segregation of duties for AI agents?

Segregation of duties for AI means ensuring that no AI system can both initiate and approve a transaction without human oversight. An AI agent might initiate a payment if all matching criteria are met, but a human must approve payments above a threshold or when the agent has applied exception rules. The audit trail must show what the agent decided and what the human approved.

Can AI approve payments?

AI can initiate payments or propose payments, but final approval of material payments should remain with humans. An AI system might process routine invoices that match all criteria and flag them for approval, but the human approver makes the final decision. This preserves segregation of duties and ensures that a qualified person takes responsibility for the payment decision. For very large or unusual transactions, the human approval should be at a senior level.

How do you evidence an audit trail for an automated process?

An audit trail for an automated process must record: the original source document or data, what AI tool or system processed it, what output the system produced, any human review or override and what was finally posted in the general ledger. Store all of these in sequence so that later you can reconstruct exactly how the number came into being. This trail is what allows your auditors to form an opinion and what allows you to investigate if something goes wrong.

Does the EU AI Act apply to finance functions?

The EU AI Act applies to certain finance functions. Article 5 identifies AI that determines creditworthiness or access to credit as prohibited if it relies on specifically protected characteristics. Articles 50 and 51 require transparency marking for generative AI. The Act’s high-risk Annex III duties (data quality, documentation, human oversight) are deferred to 2 December 2027 by Regulation (EU) 2026/1744. Finance functions using AI should review whether their systems fall within high-risk categories and should plan for the December 2027 Annex III compliance deadline.