Advanced Credit Risk Modelling and Portfolio Analytics Training Course

Introduction

The Advanced Credit Risk Modelling and Portfolio Analytics Training Course provides an in-depth exploration of modern quantitative techniques used to assess, manage, and mitigate credit risk in today’s dynamic financial environment. As financial institutions face increasing market volatility and regulatory scrutiny, the ability to build robust, data-driven credit risk models has become critical for sustainability and profitability.

This course equips participants with a strong understanding of both theoretical and practical aspects of credit risk modelling. It delves into advanced concepts such as probability of default (PD), loss given default (LGD), and exposure at default (EAD), as well as portfolio credit risk measurement and stress testing. Participants will learn to develop, validate, and implement models using real-world datasets and analytical tools.

A major focus is placed on integrating machine learning and artificial intelligence techniques into credit risk analytics. The course explores predictive modelling, feature engineering, and algorithmic decision-making, providing participants with the tools to enhance model accuracy and improve portfolio management outcomes.

The program also covers regulatory frameworks such as Basel III and IFRS 9, ensuring that participants understand compliance requirements and can apply advanced models that meet supervisory expectations. This regulatory perspective is crucial for aligning credit risk modelling with institutional governance and capital adequacy standards.

Through practical exercises, simulations, and case studies, participants will gain the skills to identify, quantify, and manage credit exposures across diverse portfolios. They will also learn how to interpret model outputs to make informed decisions that balance risk and reward in lending operations.

Ultimately, this training prepares financial professionals to design sophisticated credit risk systems, optimize portfolio performance, and lead institutional risk management transformation through evidence-based decision-making and innovation.

Who Should Attend

• Credit risk analysts and managers
• Portfolio managers and investment officers
• Risk management professionals in banks and MFIs
• Data scientists and quantitative modelers in finance
• Financial regulators and compliance officers
• Treasury and finance department executives
• Credit rating and evaluation specialists
• Academics and researchers in finance and economics
• Actuaries and financial engineers
• Internal auditors and risk control professionals
• Consultants in risk management and analytics
• Professionals involved in Basel and IFRS 9 implementation

Duration

10 Days

Course Objectives

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

• Master the fundamentals and advanced concepts of credit risk modelling.
• Apply quantitative methods to estimate PD, LGD, and EAD parameters.
• Utilize statistical and machine learning techniques in risk prediction.
• Build and validate credit scoring and portfolio risk models.
• Apply portfolio credit risk measurement and stress testing techniques.
• Understand regulatory frameworks including Basel III and IFRS 9.
• Enhance decision-making using model-based risk analytics.
• Identify and mitigate model risk and validation challenges.
• Use data visualization tools for credit risk reporting.
• Apply scenario analysis and economic capital modelling.
• Develop strategies to optimize credit portfolios and minimize losses.
• Integrate AI-driven automation in credit risk management processes.

Comprehensive Course Outline

Module 1: Introduction to Credit Risk Management

• Overview of credit risk concepts and classification
• Evolution of credit risk modelling and analytics
• Key drivers of credit risk in modern finance
• Role of credit risk management in institutional performance

Module 2: Quantitative Foundations for Risk Modelling

• Probability theory and statistics in risk analysis
• Regression models for credit scoring
• Time-series modelling and forecasting techniques
• Introduction to stochastic modelling and Monte Carlo simulations

Module 3: Credit Scoring Models and Techniques

• Logistic regression and discriminant analysis
• Development and validation of credit scoring models
• Feature selection and data preparation
• Model performance metrics and validation

Module 4: Estimation of Risk Parameters (PD, LGD, EAD)

• Probability of default modelling methods
• Loss given default estimation techniques
• Exposure at default modelling approaches
• Application of credit conversion factors

Module 5: Portfolio Credit Risk Measurement

• Understanding portfolio-level credit risk
• Correlation and concentration risk modelling
• Credit Value-at-Risk (Credit VaR) computation
• Economic capital estimation for credit portfolios

Module 6: Stress Testing and Scenario Analysis

• Designing macroeconomic stress scenarios
• Linking stress variables to credit parameters
• Reverse stress testing approaches
• Stress testing frameworks under Basel III

Module 7: Default Correlation and Credit Portfolio Models

• Structural models: Merton and KMV
• Reduced-form models for credit events
• Copula-based portfolio models
• Application of Gaussian and t-copulas

Module 8: Model Validation and Back-Testing

• Principles of model governance and validation
• Out-of-sample testing and benchmarking
• Calibration and discrimination tests
• Model risk management frameworks

Module 9: Machine Learning for Credit Risk Analytics

• Machine learning algorithms for credit scoring
• Feature engineering and data preprocessing
• Model explainability and fairness
• Hybrid models combining traditional and ML methods

Module 10: Credit Risk Modelling under IFRS 9

• Expected credit loss (ECL) calculation
• Staging criteria and impairment modelling
• Lifetime PD and macroeconomic overlays
• IFRS 9 implementation challenges and solutions

Module 11: Basel III Framework and Capital Requirements

• Regulatory capital for credit risk
• Standardized vs. IRB approaches
• Credit risk mitigation and collateral treatment
• Supervisory review process and reporting

Module 12: Credit Portfolio Optimization

• Diversification and risk-return trade-offs
• Portfolio rebalancing and credit allocation
• Optimization techniques and algorithms
• Strategic credit portfolio management

Module 13: Data Management and Risk Infrastructure

• Data quality, governance, and lineage
• Building robust data architectures for risk analytics
• Integration of credit data across systems
• Emerging trends in risk data management

Module 14: Model Risk and Governance

• Identifying and managing model risk
• Model documentation and audit trails
• Governance frameworks for model oversight
• Best practices for regulatory compliance

Module 15: Emerging Trends in Credit Risk Analytics

• Use of big data and alternative credit scoring sources
• ESG integration in credit risk modelling
• Climate-related financial risk assessments
• Real-time credit risk analytics and AI-powered systems

Module 16: Capstone Project and Practical Applications

• Developing an advanced credit risk model
• Conducting portfolio risk analysis using case data
• Presenting risk analytics dashboards
• Group project: Designing a risk modelling framework for an institution

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.