AI-powered analytics enable smarter loan portfolio oversight by identifying early signs of borrower stress and credit deterioration. This course explores how machine learning, time-series modeling, and anomaly detection create robust early warning systems. Participants will learn to integrate automated monitoring pipelines into credit workflows, establish risk thresholds, and deploy explainable alerts—ensuring proactive, scalable portfolio risk management aligned with industry best practices.
- ML-based credit deterioration detection (PD, LGD modeling)
- Time‑series monitoring of loan performance and risk drift
- Anomaly detection and alerting frameworks in loan data
- Borrower stress scoring using alternative data (behavioral, macro)
- Ensemble models: gradient boosting, LSTM for early warning
- Explainable AI for alert validation and audit compliance
- Rule-based and ML hybrid decision pipelines
- Real-time pipelines via APIs and streaming credit data
- Portfolio-level risk dashboards and visualization
- Governance, recalibration, and regulatory reporting
- Build ML models to detect early signs of borrower distress
- Implement time-series monitoring and alert triggers
- Combine alternative data for enhanced borrower risk scores
- Use ensemble and deep learning models for early warning
- Interpret AI alerts with explainable models (SHAP, LIME)
- Deploy real-time detection pipelines and thresholding
- Visualize loan risk dynamics through AI dashboards
- Establish governance and revalidation processes
- Credit portfolio managers and risk analysts
- Loan servicing and collection teams
- Risk modelers and data scientists in financial institutions
- Credit operations and compliance officers
- Fintech teams in lending platform development
- Internal audit and regulatory reporting personnel
- Expert-led demos: ML analytics in loan portfolios
- Hands-on labs: time-series scoring and anomaly detection
- Ensemble & LSTM modeling workshops
- Real-time detection pipeline setup with APIs
- Explainability sessions using SHAP/LIME frameworks
- Risk visualization using Power BI/Tableau
- Peer reviews for threshold tuning and governance
- Introduction to portfolio risk monitoring strategies
- Modeling risk drift and borrower behavior
- Lab: build a simple PD model with logistic regression
- Case study: stressed borrower detection in retail finance
- Techniques: moving average, ARIMA, and control charts
- Unsupervised anomaly detection: Isolation Forest, Autoencoders
- Lab: flag outlier loan performance metrics
- Scenario walk-through: missed payments and alerting
- Gradient Boosting and LSTM model architectures
- Handling sequential borrower data for stress prediction
- Lab: train an LSTM to forecast delinquencies
- Combine ensemble and deep models for hybrid systems
- XAI tools (SHAP/LIME) for alert justification
- Real-time detection with stream processing
- Lab: build real-time loan alerting via FastAPI
- Governance: thresholds, exceptions, and manager review
- Dashboard design: heatmaps, trend indicators, alert logs
- Model retraining, validation cycles, and governance checks
- Lab: create AI-driven loan risk dashboard
- Final group presentations: EWS rollout and credit policy integration
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.