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Course Outline
Introduction to AI in Financial Services
- Use cases: fraud detection, credit scoring, compliance monitoring
- Regulatory considerations and risk frameworks
- Overview of refinement in high-risk environments
Preparing Financial Data for Refinement
- Sources: transaction logs, customer demographics, behavioral data
- Data privacy, anonymization, and secure processing
- Feature engineering for tabular and time-series data
Model Refinement Techniques
- Transfer learning and model adaptation to financial data
- Domain-specific loss functions and metrics
- Using LoRA and adapter tuning for efficient updates
Risk Prediction Modeling
- Predictive modeling for loan default and credit scoring
- Balancing interpretability vs. performance
- Handling imbalanced datasets in risk scenarios
Fraud Detection Applications
- Building anomaly detection pipelines with refined models
- Real-time vs. batch fraud prediction strategies
- Hybrid models: rule-based + AI-driven detection
Evaluation and Explainability
- Model evaluation: precision, recall, F1, AUC-ROC
- SHAP, LIME, and other explainability tools
- Auditing and compliance reporting with refined models
Deployment and Monitoring in Production
- Integrating refined models into financial platforms
- CI/CD pipelines for AI in banking systems
- Monitoring drift, retraining, and lifecycle management
Summary and Next Steps
Requirements
- Understanding of supervised learning techniques
- Experience with Python-based machine learning frameworks
- Familiarity with financial data sets, such as transaction logs, credit scores, or KYC data
Target Audience
- Data scientists working in financial services
- AI engineers employed by fintech or banking institutions
- Machine learning professionals developing risk or fraud models
14 Hours
