Training course on AI (Artificial Intelligence) and Machine Learning in Finance

Module 1: Introduction to AI and ML in Finance

What is AI and ML?

Key differences from traditional statistical models

Current and emerging applications in finance

Module 2: Data in AI/ML Applications

Structured vs. unstructured financial data

Data preprocessing and feature engineering

Handling missing values and data imbalances

Module 3: Supervised Learning in Finance

Regression and classification models

Applications: credit scoring, loan default prediction

Hands-on: logistic regression and decision trees

Module 4: Unsupervised Learning in Finance

Clustering and dimensionality reduction

Applications: customer segmentation, anomaly detection

Hands-on: k-means clustering on financial transactions

Module 5: Deep Learning and Neural Networks

Basics of neural networks and deep learning

Applications in image, text, and speech analysis for finance

Fraud detection and sentiment analysis with deep learning

Module 6: Natural Language Processing (NLP) in Finance

Text mining of financial reports and news

Sentiment analysis of market data

Chatbots and AI assistants in banking

Module 7: Reinforcement Learning in Finance

Basics of reinforcement learning

Applications in algorithmic trading and robo-advisory

Limitations and risks of reinforcement learning

Module 8: Risk Management with AI/ML

Credit, market, and liquidity risk models

Stress testing with AI models

Early warning systems for systemic risk

Module 9: AI in Investment and Trading

Portfolio optimization with ML

High-frequency and algorithmic trading strategies

Predictive analytics for asset pricing

Module 10: Ethical, Legal, and Regulatory Issues

Model transparency and interpretability (explainable AI)

Algorithmic bias and fairness in finance

Regulatory and supervisory perspectives on AI in finance

Module 11: Case Studies and Global Practices

AI use cases in global banks, fintechs, and central banks

Case studies: JPMorgan COiN, BlackRock Aladdin, ECB SupTech initiatives

Lessons learned from early adopters

Module 12: Practical Workshop and Capstone Project

Building and testing an ML model using financial data (e.g., fraud detection, risk modeling, or credit scoring)

Model evaluation and presentation of results

Policy and business implications of findings