Cursusaanbod

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

Vereisten

  • 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
 14 Uren

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