Upcoming Live Workshop | CRM 30 under 30 
Date: 16 May 2026 | 9:30 AM
30 Credit Risk Models covering all the famous methodologies on PD, LGD, and EAD.
Key Topics Covered: Statistical techniques · Actuarial techniques · Machine Learning techniques
Duration: 60 Hours

Master Credit Risk from Fundamentals to Model Deployment

Built for working professionals in banking, NBFCs, and financial analytics who want structured, application-driven expertise.

The Credit Risk Modeling (CRM) Program is a globally aligned, comprehensive course covering the full spectrum of business and regulatory credit risk models, including scorecards, Basel capital, IFRS 9, CECL, CCAR, PPNR, ICAAP, stress testing, pricing, and portfolio risk frameworks. The program covers both retail and wholesale portfolios, with hands-on development of all key risk components — Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).

The course also covers both lines of defense, including end-to-end model development and independent model validation aligned with global regulatory standards. All models are implemented step-by-step in Excel and Python, ensuring transparent and practical learning. The program is designed for freshers, working professionals, and senior leaders including CROs and partners seeking expertise in globally accepted credit risk modeling frameworks.

What You Get with ICRM

Key features that define the learning experience.

• 250+ Hours of Hybrid Learning Core concept sessions combined with live workshops and doubt-resolution classes.
• 100+ Excel Practice Models Purpose-built financial models covering the complete credit risk workflow.
• Python & Excel Dual Implementation Every topic is implemented step-by-step in both tools, matching industry practice.
• Exam-Based Certification Earn the ICRM certificate upon successfully completing the program assessment.
• Industry-Focused Curriculum Structured around how banks and financial institutions actually build credit risk models.
• Placement & Internship Support Resume guidance, model portfolio support, mock interviews, and referral-based placement assistance for eligible learners.

What You Will Learn

Our curriculum is meticulously designed to provide a comprehensive learning experience — combining theoretical depth with practical insights. Python basic to advanced is covered separately, and no coding background is presumed.

Module 1 — Basic Understanding

• 1.1 Understanding Loan Lifecycle
• 1.2 Scorecards vs Basel vs IFRS9 vs Stress testing models
• 1.3 Excel and Python hands-on – Data Preparation for Model development using MENTOS

Module 2 — Scorecards

• 2.1 Application Scorecard vs Behavioral Scorecard
• 2.2 Understanding Bad definition
• 2.3 Excel hands-on – Roll Rate Analysis (to incorporate bad flag) on Fannie Mae Mortgage data
• 2.4 Understanding concepts of Snapshot, Observation Period & Performance Period
• 2.5 Excel hands-on – Vintage analysis to identify Performance Window
• 2.6 Segmentation techniques, criteria and validation checklist (Excel)
• 2.7 Variable Selection using PCA clustering and Information value (Excel)
• 2.8 Fine Weight of Evidence Binning and Coarse Weight of Evidence Binning (Excel)
• 2.9 Excel and Python hands-on – Building Application Scorecards using Logistic Regression
• 2.10 Behavioral variables creation – utilization, payment and delinquency
• 2.11 Deciding cut off by maximizing Revenue or profit or minimizing risk (Excel)
• 2.12 Thinking beyond Statistics – Policy rules, Overrides, Reject Inferencing (Excel)

Module 3 — Loss Modelling

• 3.1 Excel hands-on – Modelling Losses through Age Period Cohort Analysis
• 3.2 Excel hands-on – Modelling Losses using Flow Rate Analysis

Module 4 — Modelling Probability of Default

• 4.1 Excel hands-on – Calculating PD using Logistic Regression
• 4.2 Calculating PD using Machine Learning Techniques (Excel)

Module 5 — Modelling Loss Given Default

• 5.1 Calculating workout LGD (Excel)
• 5.2 Handling incomplete workouts using Chain Ladder Method (Excel)
• 5.3 Tobit, Fractional Logit & Beta Regression for LGD Modelling (Excel)
• 5.4 LGD modelling using Survival analysis (Excel)
• 5.5 Component based approach for LGD modelling (Excel)

Module 6 — Modelling Exposure at Default

• 6.1 Modelling EAD using CCF (Excel)
• 6.2 CCF data preparation using Fixed & Variable Horizon, Cohort approach (Excel)
• 6.3 CCF Regression (Excel)

Module 7 — Cure Modelling

• 7.1 Instant Cure vs Probationary Cure (Model design)
• 7.2 Loss given Cure modelling (Excel)

Module 8 — Basel Capital Charge

• 8.1 RWA & Capital Adequacy Ratio calculations (Excel)
• 8.2 Using Vasicek formula to convert TTC PD to Worst Case PD
• 8.3 Excel and Python hands-on – Calculating Capital as per Basel IRB Approach

Module 9 — IFRS 9 Introduction

• 9.1 TTC PD in Basel vs PIT PD in IFRS
• 9.2 12 months PD calculation vs lifetime PD calculation
• 9.3 Understanding Concepts of Staging – Stage 1 | Stage 2 | Stage 3

Module 10 — IFRS 9 PD Calculation

• 10.1 Understanding Conditional PD Vs Unconditional PD
• 10.2 Excel and Python hands-on – Converting TTC PD to PIT PD using Scalar approach
• 10.3 Excel and Python hands-on – Converting TTC PD to PIT PD using Log Odds shift
• 10.4 Calibration & Smoothening techniques (Excel)
• 10.5 Excel and Python hands-on – Converting TTC PD to PIT PD using z score
• 10.6 Excel and Python hands-on – Converting TTC PD to PIT PD using multi state Transition matrices
• 10.7 Building PD term structure for lifetime under 2 states and multi state framework (Excel)

Module 11 — CECL Techniques

• 11.1 Discrete Time Hazard Models (Excel)
• 11.2 Snapshot/Open Pool Method (Excel)
• 11.3 WARM Model (Excel)
• 11.4 Vintage analysis (Excel)

Module 12 — Actuarial Credit Risk Models

• 12.1 Survival analysis (Excel)
• 12.2 Cox Regression, Accelerated Failure Time models (Excel)
• 12.3 Age Period Cohort Analysis (Excel)

Module 13 — APC Extensions

• 13.1 Validating APC – Alternating Vintage Diagrams, Moran's D (Excel)
• 13.2 Bayesian APC (Excel)
• 13.3 Quantifying Adverse Selection by Vintage (Excel)
• 13.4 Adverse Selection through Fixed and Random effects (Excel)

Module 14 — IFRS 9 LGD & EAD Calculation

• 14.1 PIT forward looking term structure of LGD as a function of Collateral value (Excel)
• 14.2 PIT forward looking term structure of LGD using Regression (Excel)
• 14.3 Calculating PIT LGD using Jacob Frye model (Excel)
• 14.4 EAD Term structure for credit cards using PIT CCF Modelling (Excel)
• 14.5 EAD Term structure for loans using amortisation schedule (Excel)
• 14.6 Modelling prepayments and incorporating in amortisation schedules (Excel)

Module 15 — IFRS 9 Staging Criteria

• 15.1 Staging decision tree using quantitative and qualitative criteria
• 15.2 Staging Validation (Excel)

Module 16 — Wholesale Models

• 16.1 Understanding Transition Matrices
• 16.2 Building Transition Matrix using Cohort Approach (Excel and Python)
• 16.3 Building Transition Matrix using Duration Approach (Excel and Python)
• 16.4 Excel and Python hands-on – Converting TTC Transition Matrix to PIT Transition matrix
• 16.5 Validating Transition Matrices (Excel)
• 16.6 Building Wholesale scorecards using Quantitative and Qualitative scores

Module 17 — Low Default Portfolios

• 17.1 Bayesian approach to handle LDP (Excel)
• 17.2 Pluto Tasche Approach (Excel)
• 17.3 Van Der Burgt Method (Excel)
• 17.4 QMM Method (Excel)

Module 18 — Stress Testing

• 18.1 Top Down vs Bottom Up stress Testing (Excel)
• 18.2 Understanding CCAR vs DFAST requirements
• 18.3 Modelling ARIMA & ARIMAX (Excel)
• 18.4 Regression modelling and assumption handling (Excel)
• 18.5 Variable selection pipeline for macro-economic models
• 18.6 Excel and Python hands-on – Building CCAR models using multiple regression and VECM
• 18.7 Excel hands-on – Perform 9 quarter In Sample & Out of Sample Back testing

Module 19 — Model Validation

• 19.1 Evaluating Discriminatory Power of Model (Excel)
• 19.2 Evaluating Accuracy of Model and Calibration (Excel)
• 19.3 Performing Stability analysis (Excel)
• 19.4 Margin of Conservatism (Excel)
• 19.5 Validating Scorecards and Basel Capital Models (Excel)
• 19.6 Validating Transition Matrices (Excel)
• 19.7 Validating PIT IFRS 9 models including staging criteria (Excel)
• 19.8 Validating Stress Testing Models (Excel)
• 19.9 Validating LGD and EAD models (Excel)
• 19.10 Model Risk Management using SR 11-07 checklist

Module 20 — Pricing Loans

• 20.1 Optimizing Yields using Solver (Excel)
• 20.2 RAROC based pricing (Excel)

Module 21 — Corporate Credit Models

• 21.1 Merton & KMV Models (Excel)
• 21.2 Credit Plus Models (Excel)
• 21.3 Credit Portfolio View (Excel)
• 21.4 Credit Metrics Model (Excel)

Module 22 — Machine Learning for Credit Risk

• 22.1 Supervised Learning – LDA, SVM, Decision trees, XG Boost, Neural Network (Excel)
• 22.2 Unsupervised Learning – PCA, Clustering (Excel)

What Do We Offer in Live Sessions?

Our live sessions provide real-time mentor guidance, interactive Q&A, and hands-on problem-solving to ensure strong conceptual clarity and practical understanding.

New Topics When a new topic starts, we keep you informed in advance so that you can join from 1st class onwards.

Doubt Solving Sessions 1-hour live sessions conducted every 15 days, offering interactive, two-way doubt resolution.

Practitioners Series We have tied up with practitioners to provide some extra sessions for continuous industry updations.

Masterclass Workshops Corporate-style weekend workshops (12 hours across Saturday and Sunday) designed for live, two-way interaction and hands-on learning. Workshops are conducted regularly and repeated every 45–75 days to ensure continuous learning opportunities.

Connected Topics Joining mid topic? No worries – we'll point you to 2–3 recordings before your next live class.

Included Deliverables

Concept Lectures · Maths & Statistics Primers · Excel Models (100+) · Python Code Files · Presentation Decks (PPTs) · Reading Materials · Practice Questions · Excel Animations & Illustrations · Assignments & Graded Projects

Your Learning Journey

From enrollment to certification, here's how the program unfolds.

1. Discover the Program
2.  Register & Enroll
3.  Start Learning
4. Practice with Models
5. Join Live Workshops
6. Schedule the Exam
7. Earn Certification
8. Career & Placement Support

Integrated Credit Risk Modeling — ICRM Certificate

The exam-based ICRM certification validates your ability to understand credit risk concepts in depth, build real-world models independently, and demonstrate hands-on implementation skills in Excel and Python.

Roles This Program Prepares You For

The ICRM curriculum is aligned with roles in banking, NBFCs, fintech, rating agencies, and global financial institutions.

• Credit Risk Analyst
• Risk Modeller
• IFRS 9 Specialist
• Quantitative Analyst
• Model Validation Analyst
• Credit Analytics Professional
• Retail / Corporate Lending Analyst
• Model Monitoring Analyst