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

1. EDA Exploratory Analysis

2. Feature Engineering

3. Detection & treatment of Advanced Outliers

4. Multivariate imputation of missing values

5. Univariate analysis in percentiles (R)

6. 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

1. Segmentation using K-Means

2. Credit Scoring SVM

3. Credit Scoring Boosting

4. Credit Scoring Bagging

5. Credit Scoring Random Forest (R & Python)

6. 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

1. Deep Learning Feedforward Credit Scoring

2. CNN for Credit Scoring

3. LSTM Credit Scoring

4. 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

1. Tuning XGBoost, Random Forest & SVM

2. Tuning Deep Learning models

Module 8: Scorecard Development

Topics

• Scorecard design (binary, continuous)

• WOE classification

• Reject inference

• Cut-off optimization (ROC)

Exercises

1. 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

1. Quantum operations

Module 10: Quantum Machine Learning for Credit Scoring

Topics

• QSVM

• VQC

• Quantum NN, QGAN

• Quantum ML for PD & scoring

Exercises

1. Quantum SVM Credit Scoring

2. Quantum FFNN for PD

3. Quantum CNN for PD

Module 11: Tensor Networks

Topics

• Entanglement

• Tensor networks for ML

• Applications in NN & SVM

• Credit scoring using tensors

Exercises

1. Credit Scoring & PD with Tensor Networks

Module 12: Probabilistic Machine Learning

Topics

• Bayesian models

• Gaussian processes

• HMM

• MCMC

• Bayesian NN

Exercises

1. Gaussian Process Regression

2. 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

1. 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

1. 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

1. Cox Regression PD

2. Panel Logistic PD

3. Bayesian Logistic Regression

4. LASSO PD

5. Random Forest Survival

6. Cox-XGBoost

7. Deep Learning Survival

8. Feedforward NN

9. 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

1. Platt Scaling & Isotonic Regression

2. Gaussian Process Regression Calibration

3. Asymmetric Laplace Calibration

Module 18: Bayesian PD & Gaussian Processes

Topics

• Bayesian PD estimation

• MCMC

• Credibility intervals

• Gaussian process regression

Exercises

1. Bayesian Logistic PD (Python)

2. PD using MCMC (R)

3. Gaussian Process Regression PD

Module 19: Low-Default Portfolio PD

Topics

• PD in LDP

• Bayesian neutral & conservative PD

• GAN synthetic data for LDP

Exercises

1. LDP PD – Confidence Interval

2. Multi-period CI PD

3. Neutral Bayesian PD

4. Conservative Bayesian PD

5. 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

1. PD Forecasting with VARMAX

2. PD Forecasting with Quantum LSTM

Module 21: Lifetime PD

Topics

• Vintage & EMV models

• Regression & survival models

• Markov models

• ML & DL

Exercises

1. Multinomial Regression – Lifetime PD

2. Multi-State Markov – Lifetime PD

3. Matrix ASRF – Lifetime PD

4. Quantum SVM – Lifetime PD

5. SVM – Lifetime PD

6. Deep Learning Lifetime PD

7. Quantum Deep Learning Lifetime PD

Module 22: IRB LGD (Retail)

(No exercises)

Module 23: Econometric & ML Models for LGD

Exercises

1. Econometric LGD Models

2. ML & Deep Learning LGD

3. Model Comparison Tests

Module 24: LGD IFRS 9

Exercises

1. RF LGD IFRS 9

2. Beta Regression + NN LGD IFRS 9

3. XGBoost LGD IFRS 9

4. SVM LGD IFRS 9

5. Quantum SVM LGD IFRS 9

6. Traditional NN LGD

7. Bayesian NN LGD

Module 25: IRB EAD & CCF

Exercises

1. NN CCF Model

2. SVR CCF (Python)

3. NN + Beta Regression (R)

4. Quantum SVM CCF

Module 26: IFRS 9 EAD – Contractual Options

Exercises

1. Prepayment Model (RF, R & Excel)

Module 27: Lifetime EAD for Credit Lines

Exercises

1. NN Model for Lines of Credit

2. Lifetime EAD Model

Module 28: PD Backtesting

Exercises

1. PD Backtesting in Excel

Module 29: LGD Backtesting

Exercises

1. LGD Model Performance Comparison

Module 30: EAD Backtesting

Exercises

1. EAD Model Performance Comparison

Module 31: Deep Learning for Macroeconomic Dynamics

Exercises

1. Neural Networks for Bellman Equation

Module 32: Deep Learning for Macroeconomic Projections

Exercises

1. Charge-off Model with VAR & VEC

2. PD Forecasting with Bayesian LSTM

3. Pandemic Forecasting with RNN LSTM

4. PD Forecasting with Quantum NN

Module 33: Stress Testing PD & LGD

Exercises

1. Credit Portfolio View – PD Stress Test

2. Bayesian LSTM PD Stress Test

3. VQR Quantum Regression PD Stress Test

4. MARS Stress Test PD

5. LASSO Stress Test

Module 34: Corporate Portfolio Stress Testing

Exercises

1. Corporate PD Stress Test (ASRF + Transition Matrix)

Module 35: ECL IFRS 9 Stress Testing

Exercises

1. Internal ECL Stress Test (R & Excel)

Module 36: Quantum Stress Testing

Exercises

1. DGSE Model with Deep Learning

2. Quantum vs Classical Monte Carlo

Module 37: Economic Capital Models

Exercises

1. Portfolio EL/UL/Correlation/EC

2. CreditRisk+ in SAS

3. CreditMetrics in Excel & R

4. Single-Factor EC Model

Module 38: Quantum EC Models

Exercises

1. Quantum EL, VaR & ES

Module 39: Climate Risk in Credit Risk

Exercises

1. PD & DD for Transition Risk

2. 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

Module 21: Credit Risk Appetite

• Exercise 38: Credit Risk Appetite framework