Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection Training Course

Introduction to Artificial Intelligence in Financial Services

• Use cases: fraud detection, credit scoring, compliance monitoring
• Regulatory considerations and risk frameworks for government
• Overview of fine-tuning in high-risk environments for government operations

Preparing Financial Data for Fine-Tuning

• Sources: transaction logs, customer demographics, behavioral data
• Data privacy, anonymization, and secure processing for government agencies
• Feature engineering for tabular and time-series data in public sector applications

Model Fine-Tuning Techniques

• Transfer learning and model adaptation to financial data for government use
• Domain-specific loss functions and metrics for enhanced accuracy
• Using Low-Rank Adaptation (LoRA) and adapter tuning for efficient updates in public sector models

Risk Prediction Modeling

• Predictive modeling for loan default and credit scoring to support government financial services
• Balancing interpretability vs. performance for transparent decision-making
• Handling imbalanced datasets in risk scenarios to ensure fair outcomes

Fraud Detection Applications

• Building anomaly detection pipelines with fine-tuned models for government agencies
• Real-time vs. batch fraud prediction strategies for efficient monitoring
• Hybrid models: rule-based + AI-driven detection to enhance accuracy and reliability

Evaluation and Explainability

• Model evaluation: precision, recall, F1 score, AUC-ROC for government oversight
• SHAP, LIME, and other explainability tools to ensure transparency
• Auditing and compliance reporting with fine-tuned models for government accountability

Deployment and Monitoring in Production

• Integrating fine-tuned models into financial platforms for government use
• CI/CD pipelines for AI in banking systems to support continuous improvement
• Monitoring drift, retraining, and lifecycle management for sustained performance

Summary and Next Steps