Overview

This short course provides a practical introduction to the use of artificial intelligence in finance. It explains how AI, machine learning, generative AI, natural-language processing, robotic process automation, and predictive analytics can improve financial decision-making, reporting, risk management, customer service, fraud detection, treasury, audit, and compliance.

The course is designed for finance professionals who need to understand both the opportunity and the responsibility that come with AI adoption. Participants will explore how AI can automate repetitive finance activities, extract insights from large volumes of structured and unstructured data, improve forecasts, detect unusual transactions, support management reporting, and assist with scenario analysis.

The program also addresses the key risks of using AI in finance, including poor data quality, bias, explainability, privacy, cybersecurity, model error, third-party dependency, intellectual-property concerns, and regulatory compliance. Participants will leave with a practical framework for selecting, governing, testing, and implementing AI use cases within a finance function.

Target Participants

This course is intended for:

• Chief financial officers, finance directors, and finance managers.
• Accountants, management accountants, and financial controllers.
• Financial planning and analysis professionals.
• Treasury, tax, procurement, and shared-services professionals.
• Risk managers, internal auditors, and compliance officers.
• Financial analysts and business-intelligence professionals.
• Banking, insurance, investment, and fintech professionals.
• Data, technology, and transformation teams supporting finance.
• Senior managers and board or audit-committee members overseeing AI initiatives.
• Professionals seeking a practical, non-technical introduction to AI in finance.

Course Objectives

By the end of the course, participants will be able to:

1. Explain the main AI concepts relevant to finance, including machine learning, generative AI, natural-language processing, and automation.
2. Identify high-value AI use cases across accounting, planning, reporting, risk, audit, treasury, and compliance.
3. Distinguish between rule-based automation, analytics, machine learning, and generative AI.
4. Assess the quality, availability, security, and suitability of data for AI use.
5. Recognize the benefits and limitations of AI-supported financial decisions.
6. Use AI responsibly for drafting, analysis, forecasting, reconciliation, and research tasks.
7. Identify AI risks, including bias, hallucinations, privacy breaches, model failure, and cyber threats.
8. Apply human oversight, validation, documentation, and approval controls to AI outputs.
9. Develop basic governance principles for AI use within the finance function.
10. Prioritize AI initiatives using business value, feasibility, risk, cost, and control criteria.
11. Prepare a practical AI-in-finance implementation roadmap.

Course Outline

Module 1: Introduction to AI in Finance

• What artificial intelligence means in a finance context.
• The evolution from spreadsheets and automation to predictive and generative AI.
• Key terms: AI, machine learning, deep learning, natural-language processing, generative AI, large language models, robotic process automation, and predictive analytics.
• AI versus traditional analytics and business-intelligence tools.
• How AI creates value in the finance function.
• Common misconceptions about AI adoption.
• The continuing role of finance professionals and human judgment.

Module 2: AI Technologies and Capabilities

• Rule-based automation and robotic process automation.
• Machine learning for classification, prediction, and anomaly detection.
• Natural-language processing for document review and text analysis.
• Generative AI for drafting, summarization, explanation, and knowledge retrieval.
• Computer vision and intelligent document processing.
• Process mining and workflow intelligence.
• Forecasting and optimization models.
• AI agents and workflow orchestration.
• Strengths, limitations, and appropriate use cases for each technology.

Module 3: AI Use Cases in Accounting and Financial Reporting

• Automated invoice capture, coding, and approval routing.
• Intelligent reconciliations and exception management.
• Journal-entry review and unusual-transaction detection.
• Financial-close management and variance explanations.
• Drafting management reports and financial-commentary narratives.
• Document extraction from contracts, invoices, and supporting schedules.
• Consolidation and intercompany transaction support.
• Disclosure-checking and reporting-quality review.
• Human review requirements for financial statements and disclosures.

Module 4: AI for Planning, Forecasting, and Performance Management

• Forecasting revenue, expenses, cash flow, and working capital.
• Driver-based planning and predictive forecasting.
• Scenario planning and sensitivity analysis.
• Detecting trends, anomalies, and performance drivers.
• Budgeting support and forecasting-cycle automation.
• Forecast accuracy, back-testing, and model performance monitoring.
• Combining quantitative forecasts with management judgment.
• Avoiding false precision and unsupported projections.

Module 5: AI in Risk Management, Fraud, and Compliance

• AI-supported credit, market, liquidity, and operational-risk monitoring.
• Anomaly detection for fraud and suspicious transactions.
• Transaction screening and alert prioritization.
• Regulatory-change monitoring and policy review.
• Anti-money-laundering and know-your-customer support.
• Control monitoring and early-warning indicators.
• False positives, false negatives, and model-threshold calibration.
• Explainability and evidence requirements for high-impact decisions.

Module 6: AI in Treasury, Audit, Tax, and Procurement

• Cash-flow forecasting and liquidity monitoring.
• Foreign-exchange and interest-rate exposure analytics.
• Payment optimization and bank-reconciliation support.
• Audit planning, document analysis, and continuous controls monitoring.
• Tax-data extraction, classification, and compliance support.
• Spend analytics, supplier-risk monitoring, and contract review.
• Segregation-of-duties considerations.
• Appropriate boundaries for AI recommendations and approvals.

Module 7: Data Foundations for AI

• Data types: structured, unstructured, internal, external, and synthetic data.
• Data quality, completeness, timeliness, accuracy, and consistency.
• Data governance, ownership, lineage, and retention.
• Master data and chart-of-accounts consistency.
• Data security, access controls, and classification.
• Privacy and confidential financial information.
• Preparing data for model development and AI-enabled workflows.
• Monitoring for data drift and changing business conditions.

Module 8: Responsible AI, Controls, and Governance

• Responsible-AI principles: fairness, transparency, accountability, privacy, security, and reliability.
• Human-in-the-loop review and final accountability.
• Bias in data, models, and business processes.
• Hallucinations and inaccurate outputs from generative AI.
• Model validation, testing, documentation, and approval.
• Prompting practices and secure use of generative-AI tools.
• Intellectual-property, confidentiality, and data-leakage risks.
• Vendor due diligence and third-party model risk.
• AI policies, acceptable-use guidelines, and staff training.

Module 9: AI Implementation and Change Management

• Defining the business problem before selecting technology.
• Selecting and prioritizing AI opportunities.
• Developing a business case: value, cost, feasibility, risk, and control requirements.
• Pilot projects, proof of concept, and phased deployment.
• Establishing success measures and key performance indicators.
• Stakeholder engagement and cross-functional collaboration.
• Skills development and change-management planning.
• Integrating AI into existing finance processes and systems.
• Managing adoption, resistance, and continuous improvement.

Module 10: Integrated Case Study

Participants analyze a finance department experiencing:

• Slow and manual month-end reporting.
• Inconsistent forecasting and frequent budget variances.
• Growing volumes of invoices and payment exceptions.
• Concerns about fraudulent transactions.
• Fragmented data sources and weak data governance.
• Staff interest in using public generative-AI tools for financial analysis.