1. Program Overview

This training program introduces participants to the practical application of Artificial Intelligence (AI) in financial analysis, forecasting, risk assessment, and decision-making. As financial data becomes increasingly complex and voluminous, AI technologies provide powerful tools to uncover insights, automate tasks, and enhance strategic financial planning. Participants will explore use cases, tools, and ethical implications of deploying AI in finance.

2. Training Objectives

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

• Understand key AI concepts relevant to financial analysis.
• Apply AI tools for automating financial reporting and data analysis.
• Use machine learning for financial forecasting, credit scoring, and anomaly detection.
• Interpret AI-generated insights for investment and budgeting decisions.
• Evaluate the risks, limitations, and governance requirements of AI in finance.
• Explore how AI is transforming roles in finance and accounting functions.

3. Target Trainees

This training targets professionals in:

• Financial analysis and accounting
• Investment management and advisory
• Finance and strategy departments
• Risk and compliance units
• Financial technology (FinTech) teams
• Corporate and institutional finance
• Banking, SACCOs, microfinance, insurance, and public finance organizations

4. Main Discussion Items / Modules

• Module 1: Introduction to AI in Finance
• AI vs. traditional financial analysis
• Types of AI: Machine Learning, NLP, RPA
• The evolving role of finance professionals
• Module 2: Data-Driven Financial Analysis
• Financial data sources
• Data cleansing and preparation
• Visualization and dashboarding
• Module 3: Machine Learning Applications in Finance
• Forecasting models
• Credit scoring and profiling
• Fraud and anomaly detection
• Sentiment analysis
• Module 4: AI Tools and Technologies
• Power BI, Tableau, Excel AI add-ins
• Python and R for financial modeling
• Chatbots and AI platforms
• Module 5: Automation in Financial Reporting
• Robotic Process Automation (RPA)
• Auto-reconciliation and reporting
• AI in audit and compliance
• Module 6: Risk, Ethics, and Governance
• Algorithmic bias
• Data privacy and AI regulation
• AI governance frameworks
• Module 7: Case Studies and Emerging Trends
• Local and global case studies
• FinTech and InsurTech innovations
• Generative AI in finance

5. Training Methodology

• Facilitator-led presentations
• Real-life exercises and demos
• Case study analysis
• Group discussions and peer learning
• Scenario-based simulations
• Action planning

6. Tasks

• Build a simple predictive model
• Conduct anomaly detection
• Perform data cleansing and preparation
• Design a financial dashboard
• Group pitch: AI integration plan