Artificial Intelligence (AI) in Accounting and Finance

The programme

Programme overview

AI is reshaping finance and accounting—automating routine work, improving accuracy, and enabling faster, data-driven decisions. This Artificial Intelligence (AI) in Accounting and Finance practical, case-led programme shows how to apply AI across bookkeeping, reporting, audit, tax, forecasting, fraud detection, investment analysis, and risk management.

By the Artificial Intelligence (AI) in Accounting and Finance end, participants will know which tools to use, how to integrate them into existing systems and controls, and how to govern AI responsibly for measurable business impact.

Who should attend

Who is this programme for?

• Finance leaders and managers (CFOs, FDs, Controllers)
• Accountants, auditors, and management accountants
• FP&A and business analysts
• Risk, compliance, and tax professionals
• Investment and treasury teams
• IT professionals supporting finance functions

Targeted Competencies

• AI/ML literacy for finance
• Data preparation and interpretation
• Automated reporting and reconciliations
• Anomaly/fraud analytics
• Predictive planning & forecasting
• Investment and risk analytics
• AI system integration & controls
• Ethical, compliant AI governance

Learning outcomes

What you will learn

By the end of this Artificial Intelligence (AI) in Accounting and Finance Training, delegates will be able to:

• Explain core AI/ML concepts and real finance use cases
• Automate bookkeeping, close, and reporting workflows
• Detect anomalies and fraud with AI techniques
• Build predictive forecasts for revenue, cash flow, and costs
• Apply AI to investment screening, portfolio, and risk modelling
• Integrate AI with existing finance systems and data pipelines
• Govern AI ethically (bias, transparency, privacy) and meet regulatory requirements
• Lead change, measure ROI, and scale successful pilots

Explore the programme

Detailed programme outline

Unit 1: Foundations of AI in Finance

• What AI/ML is (and isn’t) for finance
• Priority use cases and quick wins
• Benefits, risks, and success factors
• Data quality, lineage, and control considerations

Unit 2: AI for Accounting & Audit

• Automated ledgering, reconciliations, and close
• Real-time reporting and narrative generation
• Tax data preparation and compliance support
• Fraud and anomaly detection patterns (rules vs ML)
• Short case studies from accounting processes

Unit 3: Analysis, Planning & Investment

• Predictive forecasting (AR, ML, ensembles)
• Driver-based budgeting and scenario modelling
• Credit, market, and operational risk scoring
• AI-assisted investment screening and portfolio optimisation
• Case studies from FP&A and treasury

Unit 4: Ethics, Compliance & Controls

• Fairness, bias, explainability, and accountability
• Data privacy and security in financial datasets
• Regulatory expectations and auditability
• Model monitoring, drift, and performance KPIs

Unit 5: Implementing AI in Your Organisation

• Selecting problems, tools, and vendors
• Integration patterns (APIs, RPA, ERP/EPM connectors)
• Upskilling finance teams; operating models for AI
• Change management, benefits tracking, and scaling
• Case examples of successful roll-outs

Learning approach

How you will learn

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.– Course Material: Access well-crafted supporting resources.– Group Work: Collaborate on discussions and case studies for practical insights.– Workshops & Role-Play: Participate in immersive, scenario-based activities.– Practical Application: Focus on applying theoretical knowledge in real situations.– Post-Training Support: Receive extensive support after training for skill implementation.

Discuss your requirements

Dates, duration & arrangements

Contact Ratomac to confirm dates, duration, delivery format, entry requirements and any applicable assessment or certification before booking.