🎓 International Credit Risk Modeling Certification

The International Certification in Credit Risk (ICCR) is a premier professional program designed for bankers, risk analysts, auditors, and quantitative modelers who want to master the full spectrum of modern credit risk management. This certification combines regulatory frameworks, advanced modeling, and cutting-edge AI/ML techniques.

📘 SECTION 1 — Duration & Workload

Duration: 10 weeks
Workload: 70–80 hours
Effort: 7–10 hours/week

📘 SECTION 2 — What’s Included

70+ hours of advanced IFRS 9 learning videos
Python, R, and Excel modeling exercises
Study Book (PDF, 200+ pages)
Mind maps for each module
IFRS 9/IRB presentations (PDF)
Credit Risk AI Assistant
Stress Testing IFRS 9 module
Climate Risk on Credit Risk module
Economic Capital Modeling and RAROC
Credit Scoring
Full ECL final project (PD, LGD, EAD, scenarios)
Certification exam
International diploma

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📘 SECTION 3 -- Syllabus

Module 1: Guidelines on PD, LGD & Defaulted Exposures

Topics

Reduction of parameter variability
Homogenization of PD/LGD calculation
Data quality and representativeness
Human judgment & margin of conservatism (MoC)
PD estimation (development, data, drivers, philosophy)
PD calibration (1-year DR, observed DR, LRA DR)
LGD estimation (recoveries, collaterals, costs, calibration)
LGD in-default & ELBE
Application of risk parameters
Review, documentation, impact assessment

(No exercises listed)

Module 2: Implementation of IFRS 9 in the EU

Topics

SICR & staging
Low credit risk exemption
PD as proxy for lifetime
ECL model types
Model limitations & overlays
ESG & climate risk influence
IFRS 9 PD variability
FLI incorporation (macroeconomic scenarios, non-linearity)
Staging, measurement, overlays
IFRS 9 LGD, IFRS 9 PD

(No exercises listed)

Module 3: Exploratory Analysis

Topics

Data sources, target definition
Sampling (random, stratified, rebalanced)
EDA: histograms, QQ plot, boxplot
Missing values & multivariate imputation
Outliers: winsorizing, trimming, Mahalanobis
Oversampling / SMOTE

Exercises

EDA Exploratory Analysis
Feature Engineering
Detection & treatment of Advanced Outliers
Multivariate imputation of missing values
Univariate analysis in percentiles (R)
Optimal univariate continuous-variable analysis (Excel)

Module 4: Feature Engineering

Topics

Standardization
Binning (equal interval, frequency, chi-square)
WOE coding
Variable selection
Continuous & categorical optimization
Information Value

(Exercises listed in Module 3)

Module 5: Machine Learning

Topics

K-Means
PCA & visualization
Ensemble Learning: Bagging, Random Forest, Boosting, AdaBoost, Gradient Boosting

Exercises

Segmentation using K-Means
Credit Scoring SVM
Credit Scoring Boosting
Credit Scoring Bagging
Credit Scoring Random Forest (R & Python)
Credit Scoring Gradient Boosting Trees

Module 6: Deep Learning

Topics

Feedforward NN, MLP
Activation functions
Backpropagation, gradients, Jacobians
CNNs (filters, pooling, FC layers)
CNN for credit scoring
RNN, LSTM
GANs for credit scoring

Exercises

Deep Learning Feedforward Credit Scoring
CNN for Credit Scoring
LSTM Credit Scoring
GANs Credit Scoring

Module 7: Hyperparameter Tuning

Topics

Grid, random & Bayesian search
Learning rate, optimization algorithms
Activation functions
Loss & cost functions
Hidden layers, units, epochs
Dropout, batch size
SHAP interpretation

Exercises

Tuning XGBoost, Random Forest & SVM
Tuning Deep Learning models

Module 8: Scorecard Development

Topics

Scorecard design (binary, continuous)
WOE classification
Reject inference
Cut-off optimization (ROC)

Exercises

Scorecard creation in Excel, R, Python

Module 9: Quantum Computing & Algorithms

Topics

Qubits, circuits, measurement
Quantum operations & entanglement
Deutsch algorithm
QFT
Hybrid quantum-classical algorithms
Quantum ML

Exercises

Quantum operations

Module 10: Quantum Machine Learning for Credit Scoring

Topics

QSVM
VQC
Quantum NN, QGAN
Quantum ML for PD & scoring

Exercises

Quantum SVM Credit Scoring
Quantum FFNN for PD
Quantum CNN for PD

Module 11: Tensor Networks

Topics

Entanglement
Tensor networks for ML
Applications in NN & SVM
Credit scoring using tensors

Exercises

Credit Scoring & PD with Tensor Networks

Module 12: Probabilistic Machine Learning

Topics

Bayesian models
Gaussian processes
HMM
MCMC
Bayesian NN

Exercises

Gaussian Process Regression
Bayesian Neural Networks

Module 14: Advanced Validation of AI Models

Topics

Explainability, SHAP
Global & local explanations
Model diagnosis (accuracy, robustness, fairness)
Regression & classification comparison

Exercises

Validation & Diagnosis of Advanced Scoring Models

Module 15: IRB PD – Probability of Default

Topics

PD estimation
Data requirements
PD calibration
Technical defaults
PD PIT & TTC
ML & DL PD
Margin of conservatism

Exercises

Modeling Margin of Conservatism (PD)

Module 16: Econometric & AI PD Models

Topics

Logistic, Probit, Cox, Log-Log
Panel Models
Bayesian logistic
ML survival models
Random Survival Forest
Cox-XGBoost
Deep Learning Survival
Quantum NN

Exercises

Cox Regression PD
Panel Logistic PD
Bayesian Logistic Regression
LASSO PD
Random Forest Survival
Cox-XGBoost
Deep Learning Survival
Feedforward NN
Quantum NN PD

Module 17: PD Calibration IRB

Topics

Central tendency
Bayesian calibration
Scaled calibration
Smoothing PD curves
QMM, Beta, Laplace, isotonic regression
Gaussian Process Regression

Exercises

Platt Scaling & Isotonic Regression
Gaussian Process Regression Calibration
Asymmetric Laplace Calibration

Module 18: Bayesian PD & Gaussian Processes

Topics

Bayesian PD estimation
MCMC
Credibility intervals
Gaussian process regression

Exercises

Bayesian Logistic PD (Python)
PD using MCMC (R)
Gaussian Process Regression PD

Module 19: Low-Default Portfolio PD

Topics

PD in LDP
Bayesian neutral & conservative PD
GAN synthetic data for LDP

Exercises

LDP PD – Confidence Interval
Multi-period CI PD
Neutral Bayesian PD
Conservative Bayesian PD
Synthetic Data for PD via GAN

Module 20: IFRS 9 PD Forecasting

Topics

PIT PD
TTC PD
ARIMA, VAR, VARMAX
LSTM, Bayesian LSTM
Quantum LSTM

Exercises

PD Forecasting with VARMAX
PD Forecasting with Quantum LSTM

Module 21: Lifetime PD

Topics

Vintage & EMV models
Regression & survival models
Markov models
ML & DL

Exercises

Multinomial Regression – Lifetime PD
Multi-State Markov – Lifetime PD
Matrix ASRF – Lifetime PD
Quantum SVM – Lifetime PD
SVM – Lifetime PD
Deep Learning Lifetime PD
Quantum Deep Learning Lifetime PD

Module 22: IRB LGD (Retail)

(No exercises)

Module 23: Econometric & ML Models for LGD

Exercises

Econometric LGD Models
ML & Deep Learning LGD
Model Comparison Tests

Module 24: LGD IFRS 9

Exercises

RF LGD IFRS 9
Beta Regression + NN LGD IFRS 9
XGBoost LGD IFRS 9
SVM LGD IFRS 9
Quantum SVM LGD IFRS 9
Traditional NN LGD
Bayesian NN LGD

Module 25: IRB EAD & CCF

Exercises

NN CCF Model
SVR CCF (Python)
NN + Beta Regression (R)
Quantum SVM CCF

Module 26: IFRS 9 EAD – Contractual Options

Exercises

Prepayment Model (RF, R & Excel)

Module 27: Lifetime EAD for Credit Lines

Exercises

NN Model for Lines of Credit
Lifetime EAD Model

Module 28: PD Backtesting

Exercises

PD Backtesting in Excel

Module 29: LGD Backtesting

Exercises

LGD Model Performance Comparison

Module 30: EAD Backtesting

Exercises

EAD Model Performance Comparison

Module 31: Deep Learning for Macroeconomic Dynamics

Exercises

Neural Networks for Bellman Equation

Module 32: Deep Learning for Macroeconomic Projections

Exercises

Charge-off Model with VAR & VEC
PD Forecasting with Bayesian LSTM
Pandemic Forecasting with RNN LSTM
PD Forecasting with Quantum NN

Module 33: Stress Testing PD & LGD

Exercises

Credit Portfolio View – PD Stress Test
Bayesian LSTM PD Stress Test
VQR Quantum Regression PD Stress Test
MARS Stress Test PD
LASSO Stress Test

Module 34: Corporate Portfolio Stress Testing

Exercises

Corporate PD Stress Test (ASRF + Transition Matrix)

Module 35: ECL IFRS 9 Stress Testing

Exercises

Internal ECL Stress Test (R & Excel)

Module 36: Quantum Stress Testing

Exercises

DGSE Model with Deep Learning
Quantum vs Classical Monte Carlo

Module 37: Economic Capital Models

Exercises

Portfolio EL/UL/Correlation/EC
CreditRisk+ in SAS
CreditMetrics in Excel & R
Single-Factor EC Model

Module 38: Quantum EC Models

Exercises

Quantum EL, VaR & ES

Module 39: Climate Risk in Credit Risk

Exercises

PD & DD for Transition Risk
PD & DD for Physical Risk

Module 1: Economic Capital (EC)

(No exercises)

Module 2: Economic Capital Regulation

(No exercises)

Module 3: Available Capital vs. Economic Capital

(No exercises)

Module 4: Regulatory Capital IRB vs. Economic Capital

(No exercises)

Module 5: Asset Correlation & Default Correlation

Exercise 1: Asset correlation using financial time series
Exercise 2: Asset correlation using default time series
Exercise 3: Intercorrelation & intracorrelation
Exercise 4: Calibration of asset correlation & default correlation
Exercise 5: Correlation estimation using MCMC simulation

Module 6: Structural Models for Economic Capital

Exercise 6: Estimation of Distance to Default
Exercise 7: Merton model simulation, VaR & ES estimation

Module 7: Economic Capital – Covariance Approach

Exercise 8: Unexpected loss & economic capital – corporate portfolio
Exercise 9: Unexpected loss & economic capital – retail portfolio

Module 8: Economic Capital – ASRF Model

Exercise 10: ASRF analytical solution & Monte Carlo simulation
Exercise 11: Multifactor t-student model using Monte Carlo

Module 9: Multifactor Economic Capital Model

Exercise 12: Multifactor model using Monte Carlo simulation

Module 10: CreditMetrics for Economic Capital

Exercise 14: CreditMetrics model in Excel
Exercise 15: CreditMetrics using Monte Carlo simulation in Python

Module 11: Econometric Models for Economic Capital

Exercise 16: Credit Portfolio Views in SAS & Excel
Exercise 17: Multifactor model with macroeconomic variables in R

Module 12: CreditRisk+ for Economic Capital

Exercise 18: CreditRisk+ single-sector retail
Exercise 19: CreditRisk+ sector model – analytical solution
Exercise 20: CreditRisk+ sector model – Monte Carlo simulation
Exercise 21: CreditRisk+ with copula dependence
Exercise 22: CreditRisk+ single-sector with FFT
Exercise 23: Marginal risk contribution in CreditRisk+

Module 14: Mixture Models for Economic Capital

Exercise 24: EC Logit–Normal
Exercise 25: EC Probit–Normal
Exercise 26: EC Beta–Binomial
Exercise 27: EC Poisson–Gamma

Module 15: Dependency Modeling with Copulas

Exercise 28: Gaussian & t-copula estimation
Exercise 29: EC using Gaussian & t-copulas
Exercise 30: EC using Frank & Clayton copulas

Module 16: Concentration Risk

Exercise 31: EC with ASRF + granularity adjustment
Exercise 32: CDI, Beta & DF calibration

Module 17: Capital Allocation & Planning

Exercise 33: Capital allocation using ASRF; VaR & ES estimation

Module 18: Limits for Economic Capital & Concentration

Exercise 34: Individual & sector economic capital limits

Module 19: Profitability Measures & Strategic Planning

Exercise 35: Hurdle Rate, RAROC & RORWA estimation

Module 20: Optimization of Economic & Regulatory Capital

Exercise 36: Scenario creation
Exercise 37: Stochastic dynamic programming model (multiperiod) for optimizing financial margin under leverage, NSFR, LCR, regulatory capital & EC constraints