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

• An understanding of supervised learning techniques
• Experience with Python-based machine learning frameworks
• Familiarity with financial datasets such as transaction logs, credit scores, or KYC data

Audience

• Data scientists in financial services
• AI engineers working with fintech or banking institutions
• Machine learning professionals building risk or fraud models

Fine-tuning is the process of adapting pre-trained AI models to specific domains and datasets.

This instructor-led, live training (online or onsite) is aimed at advanced-level data scientists and AI engineers in the financial sector who wish to fine-tune models for applications such as credit scoring, fraud detection, and risk modeling using domain-specific financial data.

By the end of this training, participants will be able to:

• Fine-tune AI models on financial datasets for improved fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularization to enhance model efficiency.
• Integrate financial compliance considerations into the AI modeling workflow.
• Deploy fine-tuned models for production use in financial services platforms.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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