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Duration 14 hours
Course Outline
Introduction to AI Applications in Financial Services
- Key use cases: fraud identification, credit assessment, and regulatory monitoring
- Navigating regulatory landscapes and risk management frameworks
- Overview of fine-tuning strategies in high-stakes environments
Preparing Financial Data for Optimization
- Data sources: transaction histories, customer profiles, and behavioral metrics
- Ensuring data privacy, anonymization, and secure handling procedures
- Crafting features for both tabular and time-series datasets
Techniques for Model Fine-Tuning
- Leveraging transfer learning to adapt models to financial contexts
- Selecting domain-appropriate loss functions and evaluation metrics
- Employing LoRA and adapter tuning for resource-efficient updates
Building Risk Prediction Models
- Developing predictive models for loan defaults and credit risk
- Striking a balance between model interpretability and predictive power
- Managing class imbalance in high-risk scenarios
Applications in Fraud Detection
- Constructing anomaly detection workflows using fine-tuned models
- Comparing real-time versus batch processing strategies for fraud prediction
- Designing hybrid systems that combine rule-based logic with AI-driven insights
Evaluation and Model Interpretability
- Assessing model performance via precision, recall, F1 score, and AUC-ROC
- Utilizing SHAP, LIME, and other explainability frameworks
- Conducting audits and generating compliance reports for fine-tuned models
Production Deployment and Continuous Monitoring
- Embedding fine-tuned models into existing financial platforms
- Implementing CI/CD pipelines for AI systems in banking
- Tracking data drift, executing retraining cycles, and managing the model lifecycle
Conclusion and Future Directions
Requirements
- Proficiency in supervised learning methodologies
- Practical experience with Python-based machine learning frameworks
- Familiarity with financial data structures, including transaction records, credit metrics, or KYC information
Target Audience
- Data scientists specializing in financial services
- AI engineers collaborating with fintech or banking entities
- Machine learning experts focused on developing risk or fraud models
