Advanced Credit Risk Modelling and Financial Risk Analytics Course

Course Introduction

The Advanced Credit Risk Modelling and Financial Risk Analytics Course equips finance and risk professionals with advanced analytical skills to identify, measure, and manage credit risk effectively. Participants will gain hands-on expertise in credit modelling, portfolio risk analysis, and regulatory frameworks.
This course covers state-of-the-art techniques in credit scoring, probability of default, loss given default, and exposure at default. Participants will learn to build predictive models that enhance risk decision-making for banks, financial institutions, and investment portfolios.
Emphasis is placed on practical application of credit risk analytics using statistical, econometric, and machine learning tools. Case studies and simulations will illustrate how to optimize capital allocation, stress-test portfolios, and evaluate credit quality under various economic conditions.
Participants will also explore emerging topics such as regulatory compliance (Basel III/IV), IFRS 9 expected credit loss modelling, and integrated risk management frameworks. The course emphasizes leveraging technology and analytics for real-time monitoring and informed decision-making.
Beyond modelling, the program emphasizes risk reporting, governance, and internal control processes. Professionals will gain the ability to communicate risk insights effectively to management, regulators, and investors while ensuring transparency, accuracy, and accountability.
By the end of the program, participants will be fully equipped to design, implement, and validate advanced credit risk models, optimize risk-adjusted performance, and apply cutting-edge analytics to manage financial risk across portfolios and institutions globally.

Duration

10 days

Who Should Attend

• Credit risk analysts and financial risk managers
• Portfolio managers and investment analysts
• Banking professionals involved in credit approval and underwriting
• Risk officers in financial institutions and insurance companies
• Quantitative analysts and data scientists in finance
• Regulators and compliance professionals
• Auditors and internal control specialists for credit risk
• Financial consultants advising banks and corporates
• Students and professionals pursuing FRM or CFA certifications
• Treasury and capital management professionals
• Enterprise risk management specialists
• Fintech professionals in lending and credit platforms

Course Objectives

• Equip participants with practical skills to develop advanced credit risk models using statistical and quantitative techniques.
• Enhance understanding of probability of default, loss given default, and exposure at default calculations.
• Develop capabilities to apply portfolio credit risk modelling for banks, financial institutions, and investment portfolios.
• Strengthen knowledge in stress testing, scenario analysis, and macroeconomic impact assessment on credit risk.
• Teach participants how to integrate Basel III/IV regulations into credit risk management frameworks.
• Enable proficiency in IFRS 9 expected credit loss modelling and accounting implications.
• Build skills in risk reporting, dashboards, and communication of analytics insights to stakeholders.
• Provide techniques to leverage technology, machine learning, and big data in credit risk assessment.
• Develop expertise in credit scoring, rating models, and predictive analytics for improved risk decision-making.
• Enhance understanding of governance, internal controls, and validation processes in credit risk management.
• Prepare participants to optimize risk-adjusted capital allocation and portfolio performance.
• Equip professionals to implement integrated financial risk frameworks and enhance strategic decision-making.

Course Outline

Module 1: Introduction to Credit Risk Analytics

• Overview of credit risk types and measurement techniques
• Role of credit risk in financial institutions and portfolios
• Key regulatory and compliance considerations for credit risk
• Overview of risk-adjusted return and capital allocation metrics

Module 2: Probability of Default (PD) Modelling

• Fundamentals of PD calculation and modelling approaches
• Logistic regression, discriminant analysis, and machine learning applications
• Data preparation, cleaning, and feature selection for PD models
• Model validation and performance evaluation metrics

Module 3: Loss Given Default (LGD) Modelling

• Estimating recovery rates and loss severity for credit portfolios
• LGD modelling using statistical and econometric techniques
• Incorporating collateral and guarantee effects in LGD calculation
• Validation of LGD models under Basel and IFRS 9 standards

Module 4: Exposure at Default (EAD) Modelling

• Understanding credit exposure and off-balance-sheet items
• EAD calculation methodologies and predictive models
• Incorporating drawdowns, credit limits, and facility usage
• Model validation and backtesting for accuracy

Module 5: Credit Scoring Techniques

• Traditional credit scoring and rating methods
• Advanced scoring using machine learning and AI approaches
• Model calibration, performance monitoring, and implementation
• Use of credit scores in lending decisions and risk management

Module 6: Portfolio Credit Risk Measurement

• Aggregation of individual exposures to portfolio level
• Correlation modelling and diversification effects
• Portfolio loss distribution and tail risk assessment
• Expected and unexpected loss measurement techniques

Module 7: Stress Testing and Scenario Analysis

• Macro stress testing for credit portfolios
• Scenario design and sensitivity analysis for default probabilities
• Integration of market, interest rate, and economic scenarios
• Reporting and communication of stress test results

Module 8: Regulatory Frameworks and Basel Compliance

• Overview of Basel III/IV credit risk requirements
• Capital adequacy, risk-weighted assets, and leverage ratios
• Pillar 1, 2, and 3 compliance for credit institutions
• Implementation challenges and regulatory reporting practices

Module 9: IFRS 9 Expected Credit Loss Modelling

• Principles of IFRS 9 and accounting for credit losses
• Staging and lifetime expected loss calculations
• Forward-looking information and macroeconomic adjustments
• Integration of IFRS 9 with internal credit risk models

Module 10: Credit Risk Data Management

• Data governance and quality considerations in modelling
• Data extraction, cleaning, and transformation for analytics
• Leveraging internal and external datasets for model accuracy
• Documentation and audit trails for regulatory compliance

Module 11: Machine Learning in Credit Risk

• Application of supervised and unsupervised ML techniques
• Predictive modelling and feature engineering for credit risk
• Neural networks, decision trees, and ensemble methods
• Model interpretability, explainability, and validation

Module 12: Model Validation and Backtesting

• Statistical validation of PD, LGD, and EAD models
• Backtesting portfolio credit risk predictions
• Benchmarking against industry standards and regulatory requirements
• Reporting and documentation for model governance

Module 13: Integrated Risk Management

• Combining credit, market, and operational risk perspectives
• Risk aggregation and capital allocation strategies
• Enterprise risk management frameworks for financial institutions
• Using integrated analytics for strategic decision-making

Module 14: Risk Reporting and Communication

• Designing dashboards and reporting templates for management
• Communicating credit risk insights to stakeholders
• Regulatory and investor reporting requirements
• Enhancing transparency and decision-making through analytics

Module 15: Case Studies and Practical Exercises

• Real-world credit risk modelling scenarios and exercises
• Applying PD, LGD, EAD, and portfolio risk frameworks
• Simulation of stress tests and scenario analyses
• Group discussions on model limitations and solutions

Module 16: Capstone Project

• Develop a full-scale credit risk model for a sample portfolio
• Incorporate PD, LGD, EAD, and stress testing analytics
• Validate and present model findings and risk metrics
• Demonstrate insights for regulatory compliance and decision-making

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 a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, 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.