Finance Applications

Finance Applications

ML applications in financial services and risk management.

Learning Outcomes

By completing this topic, you will:

• Apply ML to risk measurement (VaR, CVaR)
• Understand portfolio optimization approaches
• Implement credit risk and fraud detection models
• Navigate regulatory requirements

Visual Guides

Risk Metrics

Portfolio Allocation

Prediction Models

Prerequisites

• Supervised and Unsupervised Learning
• Basic financial concepts
• Understanding of probability distributions

Key Concepts

Risk Management

• Value at Risk (VaR): Maximum expected loss at confidence level
• Conditional VaR (CVaR): Expected loss beyond VaR
• Stress Testing: Scenario-based risk assessment

Portfolio Optimization

• Mean-variance optimization (Markowitz)
• Hierarchical Risk Parity (HRP)
• Factor-based approaches

Credit & Fraud

• Credit scoring models
• Default probability estimation
• Anomaly detection for fraud
• Transaction monitoring

Regulatory Framework

• SR 11-7 model risk management
• MiFID II compliance
• Explainability requirements

When to Use

ML in finance is appropriate for:

• Pattern recognition at scale
• Real-time decision support
• Risk quantification
• Process automation

Use caution when:

• Full transparency required
• Small sample sizes
• Regime changes expected

Common Pitfalls

• Overfitting to historical patterns
• Ignoring non-stationarity
• Underestimating tail risks
• Model risk from complexity
• Regulatory non-compliance

(c) Joerg Osterrieder 2025