Chart Your Path to Financial Compliance in the IFRS 9 Era

Embark on a transformative journey into the multifaceted realm of IFRS 9 modeling, guided by our esteemed cadre of industry experts. Immerse yourself in the intricate landscape of Credit Risk Modeling, where every nuance holds the potential for insightful discovery. Against the backdrop of monumental financial upheavals such as the Great Recession and the unprecedented challenges posed by the COVID-19 pandemic, the significance of IFRS 9 modeling shines brighter than ever.

IFRS 9 is complex. Stage classification rules confuse credit teams. Expected Credit Loss (ECL) calculations intimidate CFOs. Regulatory audits stress even experienced risk managers.

You’re not alone. In 2020, when COVID-19 hit, banks struggled to recalculate ECL for moratoriums. Teams that understood IFRS 9 forwards and backwards adapted in days. Others scrambled for weeks, burning through compliance budgets.

Here’s what you need to know: IFRS 9 is now the global standard for recognizing credit losses. India adopted it. Your bank must implement it correctly—or face regulatory penalties, auditor criticism, and balance sheet surprises.

DexLab’s 30-hour Advanced Certificate in IFRS 9 Modeling takes you from confused to confident. You’ll learn stage classification, ECL calculation, macroeconomic scenario modeling, and regulatory-compliant documentation. Real case studies. Python implementation. Production-ready models.

The IFRS 9 Challenge

Traditional provisioning (IAS 39) was backward-looking: “How much did we lose last year?” IFRS 9 is forward-looking: “How much are we likely to lose over the lifetime of this loan?”

This shift requires:
1. Stage assessment: Is the loan Stage 1 (low risk), Stage 2 (rising risk), or Stage 3 (default)?
2. Forward-looking variables: GDP, unemployment, interest rates, industry growth rates
3. Scenario weighting: Base, upside, downside scenarios with probabilities
4. Complex formula: PD × LGD × EAD × (1 + IR)^-t for different stages

Banks need professionals who understand both the regulatory intent and the mathematical implementation. That’s what this course delivers.

What You’ll Learn (5 Core Outcomes)

• Classify loans into Stage 1, Stage 2, and Stage 3: Using quantitative thresholds (e.g., > 30 days past due = Stage 2) and qualitative indicators (e.g., watch list). Implement RBI guidelines. Understand Significant Increase in Credit Risk (SICR) criteria.
• Calculate Expected Credit Loss (ECL) for all three stages: Stage 1: 12-month PD × LGD × EAD. Stage 2 & 3: Lifetime PD × LGD × EAD. Understand the impact of stage migration on ECL.
• Build ECL models in Python: From anonymized real banking data. Calculate stage-specific ECL. Incorporate macroeconomic scenarios. Validate against actual losses.
• Validate models to pass Regulatory audits: Back-testing. Sensitivity analysis. Ensure compliance with regulatory guidelines. Document assumptions for auditor review.
• Communicate ECL results to auditors, CFO, and the board: Present findings clearly. Justify assumptions. Show impact on balance sheet reserves.

Real-World Examples: Why IFRS 9 Matters

COVID-19 Moratorium Impact (March 2020): When India announced a moratorium on loan payments, banks had to recalculate ECL overnight. Portfolios shifted from Stage 1 to Stage 2 en masse. Teams trained in IFRS 9 modeling adjusted models in days. Others, unfamiliar with stage logic, panicked.

IL&FS Contagion Effect (September 2018): When IL&FS defaulted, connected borrowers’ credit risk changed. SICR (Significant Increase in Credit Risk) indicators triggered stage migration. Banks with robust SICR assessment moved loans to Stage 2 proactively, avoiding surprise defaults.

HDFC Implementation (Anonymized Case): HDFC implemented a sophisticated IFRS 9 model using macroeconomic scenario weighting. Result: “ECL calculations now reflect economic reality. Reserve policies adapted to GDP forecasts. Auditors approved the framework first time.”

RBI Regulatory Response: In 2021, RBI issued guidelines requiring banks to validate IFRS 9 models quarterly. Banks without trained teams struggle with validation. Those with DexLab-trained professionals adapt quickly.

Who This Course Is For

• Credit Analysts at national and global banks
• Risk Professionals in NBFC, HFC, and fintech companies
• Finance professionals seeking promotion into risk teams
• MBA students specializing in Finance or Risk Management
• FRM (Financial Risk Manager) candidates needing practical foundation
• Data Analysts wanting to transition into risk analytics

Why DexLab vs. Other Options

Dexlab Analytics has more than a decade of experience providing training on risk analytics.
DexLab teaches Python automation—you learn how to scale models from 1,000 to 1 million borrowers.
DexLab assumes you start from basics and teaches IFRS9 specifically.
DexLab is integrated, practical, project-based. You walk out with a portfolio piece.
DexLab offers—premium quality at affordable pricing.
DexLab brings fresh perspectives, latest ML techniques, and external credibility that helps with promotions.

Salary Impact & Career Path

The data is clear. Credit risk professionals earn well in India:

Post-Training Salary Jump: Alumni report average salary increase of INR2L – INR5L within 12 months of completing this certification. That’s a 100-250x return on your INR25,599 investment in Year 1 alone.

Career Progression: Risk modelers become Risk Managers, then Risk Heads. The path to leadership in financial institutions runs through risk. Banks prioritize risk professionals for executive roles because risk management is existential—get it wrong and the institution fails.

Meet Your Instructor

Your instructor brings over 10+ years of hands-on experience in Credit Risk, Analytics, and Predictive Modeling. Previous worked at Moody’s, GE Capital, Standard Chartered and SBI — four of the world’s most risk-sophisticated institutions.

Currently working with a leading global bank, he has extensive expertise in:
– Scorecard development and regulatory validation
– Model deployment in production environments
– Advanced machine learning techniques using Python (Random Forest, XGBoost, Neural Networks)
– Basel III and IFRS 9 compliance

This isn’t theory from textbooks. This is battle-tested knowledge from someone who built models that approve or deny millions of rupees in credit every single day.

How the Course Works

Weekend Batches – Saturday & Sunday
Live Online Instruction with video, screen sharing, and real-time Q&A
Use-case Project-Based: Build real models with actual datasets (anonymized)
Peer Learning: Learn alongside 10-15 professionals
Hands-On Tools: Python (Jupyter Notebooks), Excel, SQL, real banking data
Certificate of Completion: Recognized by banks for professional development

Timeline: 16 weekend sessions = 30 hours total. You can attend while working your day job.

Frequently Asked Questions

• What if I don’t know IFRS 9 basics? Module 1 covers everything from zero. We assume no prior knowledge.
• Can I use the models immediately at work? Build your bank’s ECL model in the course and deploy it.
• Should I use Excel or Python? Excel for quick analysis and reporting. Python for scalable production models.
• How long does implementation take? 2-3 months with support, depending on portfolio complexity.
• Is there job support? Career board, resume review, interview prep included.

Ready to Master Credit Risk Modeling?

Enrollment for Next Batch: Sunday, 12th July 2026
Limited Seats Available: Maximum 10 participants per batch to ensure quality
Price: INR25,599 + GST (18%)

Enroll Now: Click Here
Download Full Syllabus: Click Here

Questions? Contact us:
Email: hello@dexlabanalytics.com
Phone: +91 9903662244

What is IFRS 9?

PD, LGD, and EAD in IFRS 9

IFRS 9 requires banks to estimate the same three credit risk components used in capital calculations – Probability of Default, Loss Given Default, and Exposure at Default – but with some important differences from Basel III approaches.

Probability of Default in IFRS 9 must be forward-looking and incorporate current information about macroeconomic conditions. Unlike Basel III Point-in-Time (PIT) estimates that may fluctuate with economic cycles, IFRS 9 typically uses point-in-time PD estimates that reflect current expectations. Banks must consider whether economic conditions are expected to improve or deteriorate over the loan’s lifetime.

Loss Given Default in IFRS 9 must reflect current collateral valuations and realistic recovery expectations. During an economic downturn, collateral values fall and recovery rates decline, so LGD increases. Banks cannot simply use historical average LGD from better economic times – they must adjust for current conditions. This creates a procyclical effect where provisions rise during downturns precisely when banks have less capital.

Exposure at Default in IFRS 9 must estimate the actual amount outstanding at the time of default, accounting for expected drawdowns on undrawn facilities. For credit cards and lines of credit, this is particularly important because borrowers often draw the full undrawn amount before defaulting. Banks use Credit Conversion Factors (CCF) to estimate this drawdown behavior.

Macroeconomic Scenarios and SICR Assessment

IFRS 9 requires banks to incorporate macroeconomic variables into their Expected Credit Loss calculations. Banks must consider how factors like GDP growth, unemployment rates, interest rates, real estate prices, and industry-specific variables affect default probabilities and recovery rates.

The standard approach is to develop multiple scenarios – a base case reflecting the bank’s central economic forecast, an upside scenario with more favorable conditions, and a downside scenario with recession or adverse conditions. Each scenario gets a probability weight, and the bank calculates ECL under each scenario, then takes the probability-weighted average. This ensures ECL captures both optimistic and pessimistic outcomes.

ECL Model Development and Implementation

Building an IFRS 9 ECL model is one of the most complex credit risk modeling challenges banks face. The model must estimate PD, LGD, and EAD for thousands or millions of loans across multiple product types, then aggregate these estimates to calculate provisions.

The ECL modeling process starts with data collection and quality assurance. Banks need clean historical data on originations, defaults, recoveries, and borrower characteristics. The data must be sufficiently detailed to build statistical models, typically spanning 5+ years so that models capture behavior across different economic cycles.

Regulatory Requirements and RBI Guidelines

The IASB requires financial institutions to recognize Expected Credit Loss (ECL) for all financial instruments subject to impairment. The core principle is forward-looking – banks must estimate losses based on current conditions and reasonable forward-looking expectations, not wait for losses to occur. The ECL calculation follows the formula: Expected Credit Loss = Probability of Default (PD) × Loss Given Default (LGD) × Exposure at Default (EAD).

IASB’s Three-Stage Classification Framework:

The IASB classifies loans into three stages based on changes in credit risk since origination. Stage 1 comprises normal loans with no significant increase in credit risk – these are provisioned for 12-month ECL. Stage 2 includes loans with significant increase in credit risk but not yet in default – these are provisioned for lifetime ECL. Stage 3 comprises defaulted loans also provisioned for lifetime ECL. The transition between stages is based on whether a Significant Increase in Credit Risk (SICR) has occurred since origination.

Why Choose DexLab’s IFRS 9 Course?

DexLab’s Advanced Certificate in IFRS 9 Modeling is designed for banking and financial professionals who need to understand IFRS 9 Expected Credit Loss models at a deep technical level. The course covers the full spectrum from accounting principles through complex model implementation.

The course is taught by industry experts who have built ECL models at major Indian and international banks. Instructors bring real-world experience implementing IFRS 9 in complex banking environments, dealing with regulatory requirements, and managing model governance. This practical experience supplements theoretical knowledge so you understand not just how models work but how they work in practice.