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Duration 14 hours
Course Outline
Introduction to AI in the Financial Sector
- Key applications: fraud identification, credit assessment, and compliance oversight
- Regulatory frameworks and risk management structures
- Fundamentals of fine-tuning within high-risk contexts
Preparing Financial Data for Model Optimization
- Data sources: transaction records, client demographics, and behavioral insights
- Ensuring data privacy, anonymization, and secure handling
- Feature engineering for both tabular and time-series formats
Techniques for Model Fine-Tuning
- Applying transfer learning and model adaptation to financial contexts
- Defining domain-specific loss functions and performance metrics
- Leveraging LoRA and adapter tuning for efficient model updates
Modeling for Risk Prediction
- Developing predictive models for loan defaults and credit scoring
- Striking a balance between interpretability and model performance
- Managing imbalanced datasets in risk-related scenarios
Applications in Fraud Detection
- Constructing anomaly detection pipelines using optimized models
- Strategies for real-time versus batch-based fraud forecasting
- Hybrid approaches: combining rule-based systems with AI-driven detection
Evaluation and Model Explainability
- Assessing models using precision, recall, F1, and AUC-ROC
- Utilizing explainability tools such as SHAP and LIME
- Conducting audits and generating compliance reports for optimized models
Production Deployment and Monitoring
- Embedding optimized models into financial platforms
- Establishing CI/CD pipelines for AI within banking systems
- Tracking model drift, retraining cycles, and lifecycle management
Recap and Future Directions
Requirements
- A solid grasp of supervised learning methods
- Proficiency with Python-based machine learning frameworks
- Knowledge of financial datasets, including transaction logs, credit scores, or KYC information
Target Audience
- Data scientists specializing in financial services
- AI engineers collaborating with fintech or banking entities
- Machine learning experts developing risk or fraud models
