Credit Risk Modelling Certification Training
This training provides a comprehensive overview of key credit risk modeling techniques and their practical applications, with detailed coverage of Basel III/IV frameworks and CECL requirements. The program also includes real-world examples and case studies to help participants understand how these concepts are applied in practice.
Overview
This Credit Risk Modelling certification training is a comprehensive 20-hour online BFSI program designed to equip professionals with in-depth knowledge and practical skills for building, evaluating and applying credit risk models used by banks, financial institutions and risk management teams. This course covers the fundamentals of credit risk measurement and modelling, including key risk parameters (PD, LGD, EAD), regulatory and supervisory expectations, modern modelling challenges and forward-looking elements such as IFRS 9 Expected Credit Loss (ECL).
Participants will learn how credit models support provisioning, capital frameworks (including ICAAP and Basel III/IV), and governance requirements, and they will get practical exposure to risk staging, data considerations, validation techniques and model governance. By the end of the training, learners will be prepared to contribute effectively to credit risk modelling and risk management decisions in real-world contexts.
Course Outline
Introduction: reserving for loan losses
- Purpose of loan-loss reserves in the risk management framework
- Interaction with lending, pricing, capital, and provisioning
Track record and evolution of credit-risk models
- Historical modelling limitations and lessons learned
- Risk concept foundations: expected loss (EL) and unexpected loss (UL)
Buffers against loss: reserves vs. capital
- Accounting reserves vs. economic capital
- How models feed into both
Economic and regulatory capital
- ICAAP, Basel III/IV considerations
- Relationship between capital, provisioning, and risk appetite
Modern modelling challenges (2025)
- Model instability, regime shifts, macro dependencies, data quality
- Introduction to modelling uncertainty and overlays (high-level)
Basel regulatory environment (2025)
- IRB / AIRB guidelines
- Revised definition of default
- Basel III/IV expectations for PD/LGD/EAD models
European regulatory guidance
- ECB guidance on nonperforming loans (NPLs)
- Supervisory reviews and TRIM-style expectations
Tightened definition of default
- 90 days past due, unlikeliness to pay, forbearance rules
- Impact on modelling, staging, and rating systems
- Data segmentation and governance implications
Global regulatory themes relevant in 2025
- Model Risk Management (MRM) expectations
- Validation and back-testing standards
- Increased scrutiny on forward-looking components
Probability of Default (PD)
- Rating systems, scoring models, calibration
- Long-run vs. point-in-time PDs
- Low-default portfolios and advanced estimation approaches
Loss Given Default (LGD)
- Workout models, recovery data, collateral valuation
- Downturn LGD and regulatory requirements
- Secured vs. unsecured, cure rates, write-offs
Exposure at Default (EAD)
- Credit conversion factors (CCFs)
- Limit utilisation modelling
- Behavioural and product-specific considerations
Validation and Governance
- Back-testing, benchmarking, discriminatory power tests
- Model monitoring and performance thresholds
- Documentation standards
- Supervisory expectations (SR 11-7-aligned)
Modern Modelling Trends
- Hybrid statistical + machine learning models (high-level)
- Model explainability and regulatory acceptance
IFRS 9 overview and conceptual foundation
- Expected Credit Loss (ECL) framework
- Reasonable and supportable forecasts
- Definition of default under IFRS 9
Three-stage approach
- Stage 1 (12-month ECL)
- Stage 2 (lifetime ECL)
- Stage 3 (credit-impaired assets)
Case examples
- Transition from Stage 1 → Stage 2
- Credit deterioration examples
- Impact of collateral and restructuring
Key differences between IFRS 9 and other risk measures
- CECL vs IFRS 9 (conceptual differences)
- Divergence between regulatory PD/LGD/EAD and accounting parameters
- Divergence between risk management vs financial reporting objectives
Criteria for Significant Increase in Credit Risk (SICR)
- Quantitative and qualitative measures
- Rebuttable presumption rules and risk-based thresholds
Lifetime loss estimation
- Methods for projecting lifetime PD/EAD/LGD
- Transition matrix approaches
- Survival and hazard models
Forward-looking scenarios
- Designing baseline, adverse, and severe
- Scenario weights and macroeconomic drivers
- Modelling non-linearity and regime changes
Judgment and governance
- Use of overlays and qualitative adjustments
- Avoiding double counting
- Documentation and audit expectations
Hands-on walkthrough
- Mini ECL calculation using sample data
Prerequisites
- Basic understanding of banking or finance
- Familiarity with credit concepts like loans and risk (PD, LGD, EAD)
- Comfort with simple quantitative concepts (basic statistics)
- No prior deep regulatory or modelling experience required
Who Should Attend
- Banking and finance professionals seeking credit risk knowledge
- Credit analysts, officers, and portfolio managers
- Risk management and compliance staff with limited prior exposure
- Finance or accounting professionals involved in loan monitoring
- Anyone interested in understanding IFRS 9 and credit risk modelling basics
Certification
On successful completion of the course and assessment, you will receive a Course Completion Certificate from GraspSkill.
Benefits
- Strong foundation in credit risk concepts (PD, LGD, EAD)
- Practical skills in building and validating risk models
- Understanding of Basel III and ICAAP frameworks
- In-depth knowledge of IFRS 9 and ECL modelling
- Ability to support lending, pricing, and capital decisions
- Hands-on exposure to risk staging, validation, and data analysis
- Improved decision-making and risk assessment skills
- High-demand skillset for banking, finance, and risk roles
- Enhanced career growth and job opportunities
- Awareness of modern challenges like model risk and macroeconomic impact
About Trainer
- He is a seasoned credit risk specialist with extensive experience in building and validating risk models for financial institutions and corporate credit portfolios. With deep expertise in statistical modelling, PD/LGD/EAD frameworks and regulatory expectations, he brings practical insights into designing robust, data‑driven credit risk solutions. Known for his clear and engaging training style, he breaks down complex quantitative concepts into actionable learning that professionals can apply immediately. His guidance equips learners with the skills and confidence to develop, evaluate and implement effective credit risk models.
Learning Outcomes
- Understand core credit risk concepts including PD, LGD, and EAD
- Explain expected loss (EL) and unexpected loss (UL) frameworks
- Apply credit risk models in lending, pricing, and provisioning decisions
- Interpret regulatory requirements under Basel III and ICAAP
- Implement IFRS 9 ECL methodology and staging approach
- Assess Significant Increase in Credit Risk (SICR) and staging transitions
- Develop and evaluate PD, LGD, and EAD estimation techniques
- Perform model validation using back-testing and benchmarking methods
- Analyze data quality, segmentation, and model governance practices
- Incorporate forward-looking macroeconomic scenarios into models
- Understand model risk, overlays, and uncertainty in credit modelling
- Apply credit risk knowledge to real-world banking and financial contexts
Student Reviews
"This credit risk modelling course transformed my understanding of risk frameworks and model development, the practical approach and real examples made complex concepts easy to grasp."
"The instructors were exceptional at explaining statistical techniques and risk metrics. I now confidently build and validate credit risk models that add real value to my organization."
"I took this training to strengthen my analytics skills and it delivered beyond my expectations. The hands-on exercises and case studies prepared me for real-world modelling challenges."
"Completing this course has boosted both my technical confidence and my credibility with stakeholders. It was exactly what I needed to progress in my risk management career."
Frequently Asked Questions
We offer live online sessions, self-paced modules and in-person workshops.
No, the certification is valid indefinitely, demonstrating credit risk modeling proficiency. While not mandatory, refresher training every 2-3 years is recommended to maintain skills and compliance.
Yes, you will be able to pay the course fees in instalments. Reach out to [email protected] to see the options available to you.
Yes, there will be an assessment of 20 questions based on the training topics at the end of the course, you will have to score 75% to pass.
You will get 2 attempts.
Sure, you can watch the recordings of the sessions you cannot attend and get back to us if you have any doubts to clear.
Group discounts are available to groups of more than three candidates. You can get up to 20% discount depending on the number of participants.
Yes, if you notify at least 24 hours in advance before the 1st class of the training and there is an availability in a different batch then you will be able to switch your start date.
Our courses are designed to provide high quality learning and outcomes that exceed expectations. If for some reason your expectations are not met. You will be given a refund in accordance with our 100% satisfaction policy.
You will receive meeting login for Zoom live classes and training materials.
All the participants will be added to WhatsApp/SMS group and email thread. You can clarify doubts at any time via WhatsApp, SMS or email.
Yes, we provide mentorship, doubt resolution, and guidance for assessment preparation.
Yes. The online training is accessible worldwide.
Self Paced Learning
Request a Quote
Corporate in-house programs or open events — write to us at info@graspskill.com
Email UsRequest more details
Fill in the form and we'll get back to you shortly.
Related Courses
Fraud Prevention and Detection Online Certification Training
5 Days, 4 hours a day. 20 hours in total.
Finance 4.0: Mastering AI for Risk and Compliance Certification Training
5 Days ( 16,17, 23, 24, and 30 May 2026 ) , Total :20 hours of training. 4 hours a day.
Advanced Debt Restructuring Certification Training
5 Days, 4 hours a day. 20 hours in total.Sat, ( May 16 ,17 , 23 , 24 and May 30)- Weekend Session
IFRS 9 and Expected Credit Loss (ECL) Modelling Certification Training
Total 15 hours. 3 hours a day. On Mar 16, 17, 18, 19 and 20. Via Zoom.
Upcoming Schedules
| Date | Time | Duration | Mode | Price | Action |
|---|---|---|---|---|---|
| 13:30 - 17:30 UTC (UTC+0) | On May 02, 03, 09, 10 and 16. 4 hours a day. 20 hours in total Via Zoom. | Online | $999.00 $749.00 |
Can't find a suitable schedule?
Suggest a Date & Location
Tell us when and where works best for you — we'll do our best to arrange a session that fits.