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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 fine-tuning in high-risk environments.
Preparing Financial Data for Fine-Tuning
- Sources: transaction logs, customer demographics, behavioral data.
- Data privacy, anonymization, and secure processing.
- Feature engineering for tabular and time-series data.
Model Fine-Tuning 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 fine-tuned 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 fine-tuned models.
Deployment and Monitoring in Production
- Integrating fine-tuned models into financial platforms.
- CI/CD pipelines for AI in banking systems.
- Monitoring drift, retraining, and lifecycle management.
Summary and Next Steps
Requirements
- A solid understanding of supervised learning techniques.
- Practical experience with Python-based machine learning frameworks.
- Familiarity with financial datasets, such as transaction logs, credit scores, or KYC data.
Audience
- Data scientists working in financial services.
- AI engineers collaborating with fintech or banking institutions.
- Machine learning professionals developing risk or fraud models.
14 Hours
