Risk Assessment and Underwriting with Machine Learning Training Course

Module 1: Foundations of ML in Risk and Underwriting

• The evolution of risk: From Generalized Linear Models (GLMs) to Deep Learning
• Understanding the machine learning lifecycle in a regulated financial environment
• Identifying high-impact use cases: Credit default, mortality risk, and fraud detection
• Key performance metrics for risk: Gini Coefficient, AUC-ROC, and Lift charts
• Mapping the "Black Box" challenge in automated underwriting decisions
• Practical session: Designing a roadmap for migrating a traditional risk model to an ML-based framework

Module 2: Data Engineering for Risk Modeling

• Handling high-dimensional financial data: Missing values, outliers, and skewness
• Data cleaning protocols for sensitive PII (Personally Identifiable Information)
• Dealing with class imbalance: Handling rare events like defaults or large insurance losses
• Temporal data management: Ensuring no "Look-ahead" bias in historical risk data
• Data quality assessment for regulatory reporting (BCBS 239 standards)
• Practical session: Building a robust data preprocessing pipeline for an anonymized loan dataset

Module 3: Supervised Learning for Credit and Insurance Risk

• Implementing Logistic Regression vs. Random Forests for risk classification
• Probability of Default (PD) modeling using survival analysis and classification
• Loss Given Default (LGD) and Exposure at Default (EAD) estimation via regression
• Estimating claim frequency and severity in Property & Casualty (P&C) insurance
• Evaluating model calibration: Ensuring predicted probabilities match real-world outcomes
• Practical session: Training a classification model to predict the likelihood of credit default

Module 4: Feature Engineering for Financial Risk

• Transforming raw transactions into behavioral features (velocity, frequency, depth)
• Creating time-series features for longitudinal risk tracking
• Dimensionality reduction techniques for managing thousands of risk variables
• Target encoding and handling high-cardinality categorical data (e.g., ZIP codes)
• Automated feature selection: Identifying the most predictive risk drivers
• Practical session: Engineering behavioral features from raw transactional logs for a credit risk model

Module 5: Advanced Ensemble Methods for Underwriting

• Introduction to Gradient Boosting Machines: XGBoost, LightGBM, and CatBoost
• Hyperparameter tuning for risk models: Maximizing precision vs. recall
• Stacking and Blending models to improve predictive stability in volatile markets
• Managing the "Overfitting" risk in complex ensemble models
• Comparative analysis: When to use simple models vs. complex ensembles in underwriting
• Practical session: Building a high-performance XGBoost model for automated life insurance underwriting

Module 6: Alternative Data and Non-Traditional Risk Sources

• Integrating psychometric data and social media footprints in risk assessment
• Leveraging IoT and Telematics for real-time usage-based insurance (UBI)
• Using Natural Language Processing (NLP) to analyze credit applications and news
• Open Banking data: Extracting risk signals from real-time cash flow analysis
• Strategic considerations for data privacy and consumer consent for alternative data
• Practical session: Using NLP to extract risk indicators from unstructured loan application comments

Module 7: Explainable AI (XAI) for Regulatory Compliance

• The "Right to Explanation": Meeting GDPR and FCRA requirements
• Global vs. Local Interpretability: Understanding the model vs. a single decision
• Implementing SHAP and LIME to generate "Reason Codes" for applicants
• Feature Importance vs. Feature Contribution: Clarifying model mechanics to stakeholders
• Visualizing decision boundaries for non-technical risk committees
• Practical session: Generating and interpreting SHAP values for an automated rejection decision

Module 8: Model Validation and Backtesting Strategies

• Rigorous backtesting: Evaluating model performance on "Out-of-Time" datasets
• Stress Testing: Simulating model behavior during economic downturns
• Monitoring Model Drift: Detecting when risk populations change (Population Stability Index)
• Sensitivity Analysis: Testing the robustness of models to input perturbations
• Establishing a Model Risk Management (MRM) framework (SR 11-7 compliance)
• Practical session: Performing a backtest on a risk model using data from a different economic cycle

Module 9: Ethical AI, Bias Detection, and Fairness

• Identifying protected attributes and avoiding proxy discrimination
• Statistical measures of fairness: Demographic Parity, Equalized Odds, and Calibration
• Implementing "Fairness Constraints" during the model training process
• Mitigating historical bias in training data for equitable underwriting
• Establishing an Ethics Committee for AI in risk management
• Practical session: Conducting a bias audit on a credit scoring model and implementing mitigation strategies

Module 10: Operationalizing and Deploying Risk Engines

• Architectures for real-time underwriting: API-first design for instant decisions
• Integrating ML models with legacy Core Banking and Insurance systems
• Continuous Integration/Continuous Deployment (CI/CD) for risk models
• Building monitoring dashboards for real-time risk exposure tracking
• The role of the "Human-in-the-Loop": When to escalate to a senior underwriter
• Practical session: Deploying a trained risk model as a scalable API and designing a monitoring dashboard

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Phoenix Training Center admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@phoenixtrainingcenter.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254725012095 / +254724452588