Machine Learning & AI for Finance Professionals Training Course

Introduction to Machine Learning in Finance

• The role of AI and ML in the modern financial industry.
• Distinguishing between supervised, unsupervised, and reinforcement learning.
• Practical case studies covering fraud detection, credit scoring, and risk modeling.

Python and Data Handling Fundamentals

• Leveraging Python for data manipulation and analytical tasks.
• Working with financial data using Pandas and NumPy libraries.
• Creating data visualizations with Matplotlib and Seaborn.

Supervised Learning for Financial Forecasting

• Applying linear and logistic regression techniques.
• Implementing decision trees and random forest algorithms.
• Assessing model performance through accuracy, precision, recall, and AUC metrics.

Unsupervised Learning and Anomaly Detection

• Utilizing clustering methods such as K-means and DBSCAN.
• Dimensionality reduction via Principal Component Analysis (PCA).
• Identifying outliers to enhance fraud prevention strategies.

Credit Scoring and Risk Modeling

• Constructing credit scoring models using logistic regression and tree-based approaches.
• Strategies for managing imbalanced datasets in risk assessment.
• Ensuring model transparency and fairness in financial decision-making.

Fraud Detection with Machine Learning

• Understanding prevalent forms of financial fraud.
• Employing classification algorithms for effective anomaly detection.
• Implementing real-time scoring and deployment strategies.

Model Deployment and Ethics in Financial AI

• Deploying models via Python, Flask, or cloud-based platforms.
• Navigating ethical considerations and regulatory compliance (including GDPR and explainability).
• Monitoring performance and retraining models in production environments.

Summary and Recommended Next Steps