Predictive Analytics in Banking and Risk Management Training Course

Module 1: The Predictive Analytics Landscape in Modern Banking

• Overview of the shift from descriptive to prescriptive analytics in finance
• Key differences between statistical models and machine learning in banking
• High-value use cases across retail, corporate, and investment banking
• Understanding the impact of Fintech and Big Tech on traditional risk models
• Aligning analytics strategy with business goals and ROI
• Practical session: Mapping a banking value chain to identify high-priority predictive opportunities

Module 2: Data Engineering and Quality for Financial Models

• Handling missing values and outliers in high-volume transactional data
• Feature engineering for banking: Creating behavioral and temporal variables
• Addressing data imbalance in risk events and default scenarios
• Integrating traditional credit bureau data with alternative data sources
• Ensuring data lineage and integrity for regulatory reporting
• Practical session: Cleaning and transforming a raw retail banking dataset for model readiness

Module 3: Probability of Default (PD) Modeling for Credit Risk

• Logistic Regression vs. Gradient Boosting for default prediction
• The Weight of Evidence (WoE) and Information Value (IV) approach
• Behavioral vs. Application scoring: Distinguishing short-term and long-term risk
• Model calibration and backtesting of PD estimates
• Interpreting scorecards for loan approval and rejection
• Practical session: Building and validating a credit scorecard using a historical loan dataset

Module 4: Loss Given Default (LGD) and Exposure at Default (EAD)

• Modeling recovery rates and hair-cuts on collateralized loans
• Predictive techniques for EAD: Handling credit limit utilization
• Using Tobit and Beta regression for bounded LGD distributions
• Macroeconomic adjustments: Modeling LGD across the credit cycle
• Identifying key drivers of severity in defaulted portfolios
• Practical session: Developing an LGD estimation model for a secured lending portfolio

Module 5: Market Risk and Value at Risk (VaR) Analytics

• Foundations of Value at Risk (VaR) and Expected Shortfall (ES)
• Implementing Historical Simulation and Parametric VaR models
• Using Monte Carlo simulations for complex portfolio risk estimation
• Volatility modeling: Moving from simple standard deviation to GARCH models
• Backtesting market risk models against actual P&L outcomes
• Practical session: Calculating VaR for a multi-asset investment portfolio using Python

Module 6: Operational Risk and Fraud Prediction

• Identifying and quantifying operational "Rare Events" and tail risks
• Machine learning for real-time transaction monitoring and fraud detection
• Anomaly detection techniques for identifying internal and external threats
• Modeling the impact of cyber risk and system failures on capital
• Sentiment analysis for reputational risk monitoring
• Practical session: Implementing an isolation forest for detecting fraudulent credit card transactions

Module 7: Stress Testing and Regulatory Capital Modeling

• Principles of the Internal Ratings-Based (IRB) approach for capital
• Designing macroeconomic scenarios: Baseline, Adverse, and Severely Adverse
• Projecting Balance Sheet and P&L under stressed conditions
• Sensitivity analysis: Assessing the impact of interest rate and currency shifts
• Communicating stress test results to senior management and regulators
• Practical session: Simulating a portfolio loss under a "Global Financial Crisis" scenario

Module 8: Customer Analytics: Churn, Cross-sell, and CLV

• Predictive modeling for customer churn: Identifying "At-Risk" depositors
• Next Best Action (NBA) models for cross-selling financial products
• Estimating Customer Lifetime Value (CLV) to prioritize high-value segments
• Market Basket Analysis for identifying product bundles and affinities
• Segmenting customers using unsupervised learning for targeted marketing
• Practical session: Building a churn prediction model to improve retail banking retention

Module 9: IFRS 9 and Basel III/IV Analytical Requirements

• Transitioning from "Incurred Loss" to "Expected Credit Loss" (ECL) frameworks
• Calculating Stage 1, Stage 2, and Stage 3 impairments under IFRS 9
• Integrating macroeconomic "Forward-Looking" information into risk models
• Understanding the "Output Floor" and risk-weighted asset (RWA) optimization
• Reporting and disclosure requirements for automated risk models
• Practical session: Calculating ECL for a sample portfolio following IFRS 9 guidelines

Module 10: Model Governance, Ethics, and Explainable AI

• The Model Risk Management (MRM) lifecycle: Development, Validation, and Monitoring
• Implementing Explainable AI (XAI) to justify credit decisions (SHAP and LIME)
• Detecting and mitigating algorithmic bias in lending decisions
• Ethical considerations of using alternative data in credit scoring
• Managing "Black Box" risk in deep learning models for finance
• Practical session: Conducting a bias audit and generating model explanations for a denied credit application

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