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Duration 14 hours
Course Outline
Foundations of AI in Financial Services
- Key application areas: fraud identification, credit assessment, and regulatory monitoring
- Navigating regulatory landscapes and established risk frameworks
- Strategic overview of model fine-tuning in high-stakes environments
Preparing Financial Data for Optimization
- Data sources: transaction histories, customer profiles, and behavioral patterns
- Ensuring data privacy, anonymity, and secure handling protocols
- Feature engineering strategies for tabular and temporal data
Advanced Model Fine-Tuning Methods
- Adapting models to financial contexts through transfer learning
- Designing domain-specific loss functions and evaluation metrics
- Implementing LoRA and adapter tuning for resource-efficient model updates
Developing Risk Prediction Models
- Building predictive models for loan defaults and credit scoring
- Striking a balance between model interpretability and predictive performance
- Addressing class imbalance challenges in risk assessment scenarios
Applications in Fraud Detection
- Constructing anomaly detection pipelines utilizing fine-tuned models
- Evaluating real-time versus batch processing strategies for fraud prediction
- Combining rule-based systems with AI-driven detection for hybrid approaches
Model Evaluation and Transparency
- Assessing model performance using precision, recall, F1 scores, and AUC-ROC
- Utilizing SHAP, LIME, and other explainability frameworks
- Conducting audits and generating compliance reports for fine-tuned models
Production Deployment and Ongoing Monitoring
- Integrating optimized models into existing financial platforms
- Establishing CI/CD pipelines for AI solutions within banking systems
- Managing model drift, continuous retraining, and lifecycle governance
Recap and Future Directions
Requirements
- Proficiency in supervised learning concepts.
- Practical experience with Python-centric machine learning libraries.
- Working knowledge of financial data structures, including transaction records, credit ratings, and KYC information.
Target Audience
- Data scientists specializing in financial services.
- AI engineers collaborating with fintech companies or banking institutions.
- Machine learning experts focused on developing risk assessment or fraud detection models.
