Question 1

IFRS 9: Credit Risk Modeling 2.0 40 hours

Accepted Answer

Module 1: Exploratory Analysis
 • Exercise 1: EDA Exploratory Analysis

Module 2: Feature engineering
 • Exercise 2: Feature Engineering
 • Exercise 3: Detection and   treatment of Advanced Outliers
 • Exercise 4: Multivariate model of imputation of missing values
 • Exercise 5: Univariate analysis in percentiles in R
 • Exercise 6: Continuous variable optimal univariate analysis in Excel

Module 3: Machine Learning
 • Exercise 7: Segmentation data  using K-Means
 • Exercise 8: Credit Scoring  Support Vector Machine
 • Exercise  9: Credit Scoring Boosting
 • Exercise 10: Credit Scoring Bagging
 • Exercise 11: Credit Scoring  Random Forest, R and Python
 • Exercise 12: Credit Scoring  Gradient Boosting Trees

Module 4: Deep Learning
 • Exercise 14: Credit Scoring  using Deep Learning Feed Forward
 • Exercise 15: Credit Scoring  using Deep Learning CNN
 • Exercise 16: Credit Scoring  using Deep Learning LSTM
 • Exercise  17: Credit Scoring using GANs

Module 5: Tuning Hyperparameters in Deep Learning
 • Exercise 18: Tuning  hyperparameters in Xboosting, Random Forest and SVM models for credit   scoring
 • Exercise 19: Tuning  hyperparameters in Deep Learning model for credit scoring

Module 6: The development process of the scorecard
 • Exercise 20: Creating a  scorecard using Excel, R, and Python

Module 7: Quantum Computing and Algorithms
 • Exercise  21: Quantum operations

Module 8: Development of Credit Scoring using Quantum Machine Learning
 • Exercise 22: Quantum Support  Vector Machine to develop credit scoring model
 • Exercise 23: Quantum Feed  Forward Neural Networks to develop a credit scoring model and PD  estimation
 • Exercise 24: Quantum Convoluted Neural Networks to develop a credit scoring model and PD estimation

Module 9: Tensor Networks for Machine Learning
 • Exercise 25: Construction of credit scoring and PD using tensor networks

Module 10: Probabilistic Machine Learning
 • Exercise 26: Gaussian process for regression
 • Exercise  27: Bayesian neural networks

Module 11: Advanced Validation of AI Models
 • Exercise 28: Validation and  diagnosis of advanced credit scoring models

Module 12: Probability of Default PD
 • Exercise 29: Modeling the Margin of Caution PD

Module 14: Econometric and AI Models of PD
 • Exercise 30: Using COX   Regression to estimate the PD
 • Exercise 31: Using logistic   regression with panel data to estimate PD
 • Exercise 33: Using Bayesian  Logistic Regression to estimate PD
 • Exercise 34: Using PD LASSO regression to estimate PD
 • Exercise 35: Using Random  Forest Survival to estimate PD
 • Exercise 36: Using Cox Xboost  to estimate PD
 • Exercise 37: Using Deep Learning survival to estimate PD
 • Exercise 38: Using Feed Forward NN to estimate PD
 • Exercise 39: Using Quantum  Neural Networks to estimate PD

Module 15: PD Calibration
 • Exercise 40: PD calibration  using Platt scaling and isotonic regression
 • Exercise 41: PD calibration  using Gaussian Process Regression
 • Exercise 42: Calibration of the  PD asymmetric Laplace distribution

Module 16: Bayesian PD and Gaussian Process
 • Exercise 43: Logistic Model  Bayesian PD in Python
 • Exercise 44: PD using MCMC in R
 • Exercise 45: PD using Process Gaussian Regression

Module 17: Low Default Portfolio PD (PD LDP)
 • Exercise 46: PD LDP confidence      interval approach in R
 • Exercise 47: Multiperiod confidence interval approach PD LDP
 • Exercise 48: Neutral Bayesian PD in R
 • Exercise 49: Conservative  Bayesian PD in R
 • Exercise 50: Generating  synthetic data with GAN for estimating PD

Module 18: Transition Matrices and Temporary Structure of PD
 • Exercise 51: Transition Matrix   analysis (cohort and duration) in Python
 • Exercise 52: Calibration of the temporal structure of the PD

Module 19: Lifetime PD Models
 • Exercise 53: Vintage EMV   Decomposition model for PD Lifetime
 • Exercise 54: Multinomial  regression for estimating PD Lifetime
 • Exercise 55: Multi-State Markov  Model for estimating PD Lifetime
 • Exercise 56: Matrix ASRF model  for estimating PD Lifetime
 • Exercise 57: SVM in Python for estimating PD Lifetime
 • Exercise 58: Neural Network in  Python for estimating PD Lifetime
 • Exercise 59: Quantum Neural  Network in Python for estimating PD Lifetime
 • Exercise 60: Quantum SVM in  Python for estimating PD Lifetime

Module 20: Advanced Calibration Lifetime PD
 • Exercise 61: Nelson–Siegel   calibration for Lifetime PD
 • Exercise 62: Gamma Adjustment calibration
 • Exercise 63: Factor Fit calibration
 • Exercise  64: Vasicek model calibration

Module 21: Estimating Lifetime PD using Quantum Computing
 • Exercise 65: Lifetime PD estimation using quantum enhancements

Module 22: LGD IRB in Retail Portfolios
(No exercises listed)

Module 23: Econometric and Machine Learning LGD Models
 • Exercise 66: Logistic, Linear and Beta regression for LGD
 • Exercise 67: Random Forest, XGBoost and Deep Learning for LGD
 • Exercise 68: Comparison of LGD models via calibration and precision tests

Module 24: LGD models in IFRS 9
 • Exercise 69: Tobit regression in R for LGD IFRS 9
 • Exercise 70: Neural Networks for LGD IFRS 9
 • Exercise 71: Support Vector Machine for LGD IFRS 9

Module 25: Advanced EAD and CCF modeling in IFRS 9
 • Exercise 72: OLS Regression Model for CCF
 • Exercise 73: CCF Logistic Regression Model
 • Exercise 74: Neural Networks and SVM for CCF

Module 26: Prepayment Rate Modeling
 • Exercise 75: IFRS 9 prepayment model for mortgages (R and Excel)

Module 27: Lifetime EAD for credit lines
 • Exercise 76: Lifetime EAD model for individual lines of credit
 • Exercise 77: Vintage Lifetime EAD/ECL model for credit line pools

Module 28: PD Backtesting
 • Exercise  78: PD backtesting in Excel

Module 29: LGD Backtesting
 • Exercise 79: Comparison of LGD model performance using calibration and precision tests

Module 30: Deep Learning for Macro-Economic Dynamics Modeling
 • Exercise 80: Neural networks for Bellman equation macroeconomic model

Module 31: AI for Forecasting & Stress Testing PD and LGD
 • Exercise 81: Forecasting PD using GAN Neural Networks
 • Exercise      83: Forecasting PD using LSTM
 • Exercise 84: Forecasting PD using Quantum LSTM
 • Exercise 85: Stress testing Charge-off model using RNN LSTM
 • Exercise 86: Forecasting PD with Pandemic & Climate variables (RNN LSTM)
 • Exercise 87: Forecasting PD using Quantum GAN
 • Exercise      88: Forecasting PD using Transformers
 • Exercise 89: Bayesian LSTM for Stress Testing PD

Module 32: Classical Stress Testing of PD & LGD
 • Exercise      90: Multiyear stress test
 • Exercise 91: Stress Testing PD (Excel & SAS – CPV model)
 • Exercise 92: Stress Testing ECL using matrices & time series
 • Exercise 93: Stress Testing using linear regressions
 • Exercise 94: Stress Test LGD
 • Exercise 95: Joint Stress Test of PD & LGD
 • Exercise 96: Stress Testing Uncertainty in R

Module 33: Stress Testing in Corporate Portfolios
 • Exercise 97: Stress Testing PD & transition matrices using ASRF (SAS, R, Excel)

Module 34: Stress Testing under ECL IFRS 9
 • Exercise 98: Global ECL Stress Testing (R & Excel)

Module 35: Quantum Computing for Stress Testing
 • Exercise 99: DGSE model using deep learning
 • Exercise 100: Quantum Monte Carlo Simulation vs Classical Monte Carlo

Module 36: Significant Increase in Credit Risk SICR
 • Exercise 101: SICR estimation using ROC discriminant power test

Module 37: Models for Lifetime ECL
 • Exercise 102: Lifetime ECL estimation for a consumer credit portfolio (R, Excel, VBA)

Module 38: Validation of ECL
(No exercises listed)

Module 39: Generative AI & Generative AI in IFRS 9
 • Exercise 103: Embeddings for  Words, sentences, question answering
 • Exercise 104: Embedding Visualization
 • Exercise 105: Embeddings on Large Dataset
 • Exercise 106: Prompt engineering
 • Exercise 107: Advanced Prompting Techniques
 • Exercise  108: LLMs in Credit Rating
 • Exercise 109: Modeling Lifetime  PD using generative AI
 • Exercise 110: Transformers for   forecasting Lifetime PD
 • Exercise 111: Analysis of ECL   results using generative AI
 • Exercise 112: Diffusion LLMs   applied to ECL IFRS 9

Question 2

IFRS 9: Credit Risk Modeling 30 hours

Accepted Answer

Module 1: Directives on the Accounting of Expected Credit Losses of Basel III and IFRS 9
(No specific exercises listed.)
Module 2: Phases of IFRS 9
(No specific exercises listed.)
Module 3: Impact of COVID-19 on Credit Risk
 • Exercise 1: Modeling and projection of contagion curves in Python
Module 4: Impact of Covid-19 on IFRS 9 provisions
(No specific exercises listed.)
Module 5: Preparation of Econometric Models
 • Exercise 2: Detection of non-stationary series and cointegration
 • Exercise 3: Measurement of multivariate colinearity of logistic and linear regression model
Module 6: Advanced Projection Models
 • Exercise 4: GARCH volatility forecasting in Python
 • Exercise 5: Forecasting multivariate volatility GARCH in R
 • Exercise 6: Forecasting financial series with Machine Learning using Python
 • Exercise 7: Forecasting financial series and indices using Recurrent Neural Networks in Python
 • Exercise 8: Pandemic Forecasting using RNN LSTM in Python
 • Exercise 9: Charge-off model with VAR and VEC
 • Exercise 10: Charge-off model with RNN LSTM
Module 7: Impact of COVID-19 on ECL models
 • Exercise 11: Roll rates model and post Covid-19 migration matrices
Module 8: Analysis of Scenarios under COVID-19
 • Exercise 12: Macroeconomic scenarios of associated macroeconomic variables using DSGE
Module 9: Loss Forecasting Models
 • Exercise 14: Roll Rates in Excel
 • Exercise 15: Multivariate Time Series of Roll Rates in R
 • Exercise 16: Markov processes in SAS and Excel
 • Exercise 17: Charge-off Deep Learning Model
 • Exercise 18: Charge-off Model with macroeconomic variables linked to COVID-19
Module 10: Default IRB Probability
(No specific exercises listed.)
Module 11: PD Estimation using Credit Scoring
 • Exercise 19: Advanced Credit Scoring Construction in Python and R
 • Exercise 20: PD calibration with COX regression in R
 • Exercise 21: PD Calibration with Panel Data Regression in R
 • Exercise 22: Calibration of PD with LASSO logistic model in R
 • Exercise 23: Calibration of PD with Bayesian probit regression in R
 • Exercise 24: PD calibration with Bayesian COX regression in R
Module 12: PD Bayesiana
 • Exercise 25: Bayesian PD of logistic model in Python
 • Exercise 26: Bayesian PD of probit model in R
Module 14: Structural Models EDF and models of Reduced form
 • Exercise 27: Estimate of EDF and Distance to Default (DD) of companies and sovereigns
 • Exercise 28: Impact on PD curves due to the pandemic
Module 15: Low Default Portfolio PD (PD LDP)
 • Exercise 29: Confidence interval approach PD LDP in R
 • Exercise 30: Multiperiod confidence interval approach PD LDP
 • Exercise 31: Neutral Bayesian PD in R
 • Exercise 32: Conservative Bayesian PD in R
 • Exercise 33: Bayesian PD with Expert criterion in R
Module 16: Transition Matrices and PD Temporal Structure
 • Exercise 34: Transition Matrix analysis and error exercise using cohort and duration approach in Python
 • Exercise 35: Calibration of the PD temporal structure
Module 17: Forecasting of PD IFRS 9
 • Exercise 36: Forecasting the PD using VARMAX in R
 • Exercise 37: PD forecasting using ASRF in R and Excel
Module 18: Lifetime PD Models
 • Exercise 38: Lifetime PD using vintage EMV Decomposition with pandemic data
 • Exercise 39: Lifetime PD using multinomial regression in R
 • Exercise 40: Lifetime PD using Markov model
 • Exercise 41: Lifetime PD using matrix ASRF model
 • Exercise 42: Lifetime PD using extrapolation approach
 • Exercise 43: Lifetime PD using SVM in Python
 • Exercise 44: Lifetime PD using Deep Learning in Python and PD calibration
Module 19: PD IFRS 9 for LDP portfolios
 • Exercise 45: Lifetime PD model in LDP portfolio
Module 20: LGD in Retail portfolios and IRB companies
 • Exercise 46: Estimation and analysis of LGD and Exposure-Weighted Average LGD
Module 21: Econometric Models for LGD
 • Exercise 47: Logistic and linear regression LGD in SAS
 • Exercise 48: LGD Neural Networks
 • Exercise 49: Beta Regression Model LGD in R and SAS
 • Exercise 50: Comparison of the performance of the models using calibration and precision tests
Module 22: LGD for IFRS 9
 • Exercise 51: Estimation and adjustments for LGD IFRS 9 using Tobit regression in R
 • Exercise 52: Censored Regression Model LGD in R
 • Exercise 53: LGD IFRS 9 estimation using LASSO regression in Python
 • Exercise 54: Estimate LGD IFRS 9 using SVM
 • Exercise 55: Estimate LGD IFRS 9 using Neural Networks
Module 23: Advanced EAD and CCF modeling for IRB
 • Exercise 56: OLS regression model for CCF in Excel
 • Exercise 57: CCF logistic regression model in Python
 • Exercise 58: Neural Networks and SVM CCF in R
 • Exercise 59: Beta Regression Model CCF in R
 • Exercise 60: Comparison of the performance of EAD models
Module 24: Contractual Options
 • Exercise 61: IFRS 9 prepaid model for mortgages in R and Excel
Module 25: EAD Lifetime for Credit Lines
 • Exercise 62: Econometric model of credit line usage in R
 • Exercise 63: EAD Lifetime model for individual credit line
 • Exercise 64: EAD Lifetime Vintage Model for credit line pool in R and Excel
Module 26: Backtesting PD
 • Exercise 65: Backtesting of PD in Excel
Module 27: LGD Backtesting
 • Exercise 66: Comparison of the performance of the models using calibration and precision tests
Module 28: Backtesting EAD
 • Exercise 67: Comparison of the performance of EAD models
Module 29: Backtesting Lifetime PD
 • Exercise 68: Stability and accuracy test of Lifetime PD in Excel
 • Exercise 69: Backtesting Lifetime PD over time
Module 30: Consumer Credit Risk Stress Testing
 • Exercise 81: Stress Testing PD IRB in Excel and SAS multifactorial model Credit Portfolio View
 • Exercise 70: Stress Testing PD IRB Multiyear Approach
 • Exercise 71: Stress Test of the LGD econometric model in R
 • Exercise 72: Transition Matrix Stress Test and Lifetime PD
 • Exercise 73: Joint Stress Test of PD & LGD in SAS
(Nota: el orden de numeración de ejercicios en el texto original está mezclado; aquí lo he respetado tal cual.)
Module 31: Stress Testing Corporate Credit Risk
 • Exercise 74: Stress Testing of corporate portfolio provisions using transition matrix and ASRF model in SAS, R and Excel
Module 32: Stress Testing of ECL IFRS 9
 • Exercise 74 (second occurrence): ECL Stress Testing using matrices and time series in R and Excel
Module 33: Increased Risk SICR Credit
 • Exercise 75: Estimation of SICR credit risk increase using ROC discriminant power test in R and Excel
Module 34: Lifetime Expected Credit Losses Models
 • Global Exercise 76: Estimation of Lifetime Expected Loss Provisions of a consumer credit portfolio in R and Excel with VBA
Module 35: EL Validation
(No specific exercises listed.)
Module 36: Financial and capital impact by IFRS 9 endowments under COVID-19
(No specific exercises listed.)

🎓 International Certification in IFRS 9 Credit Risk Modeling

📘 SECTION 1 — Duration & Workload

Duration: 10 weeks (self-paced)

Workload: 50-70 hours

Effort: 5–7 hours per week

Access: 12 months

IFRS 9: Credit Risk Modeling 30 hours

IFRS 9: Credit Risk Modeling 2.0 40 hours

The International Certification in IFRS 9 Credit Risk Modeling equips professionals with cutting-edge skills in expected credit loss (ECL) modeling. Covering PD, LGD, EAD, and advanced AI techniques, this program aligns with global standards like Basel IV and EBA guidelines.

📘 SECTION 2 — What’s Included

📘 SECTION 4 — Videos with information about the material

IFRS 9 Promotions

IFRS 9 Promotions

IFRS 9 Credit Risk Modeling 2.0 Info

Agent AI for Credit Scoring

IFRS Credit Risk Modeling Sample 1

IFRS 9 Credit Risk Modeling Sample 2

🎓 International Certification in IFRS 9 Credit Risk Modeling

📘 SECTION 1 — Duration & Workload

​

Duration: 10 weeks (self-paced)

Workload: 50-70 hours

Effort: 5–7 hours per week

Access: 12 months

IFRS 9: Credit Risk Modeling 30 hours

IFRS 9: Credit Risk Modeling 2.0 40 hours

The International Certification in IFRS 9 Credit Risk Modeling equips professionals with cutting-edge skills in expected credit loss (ECL) modeling. Covering PD, LGD, EAD, and advanced AI techniques, this program aligns with global standards like Basel IV and EBA guidelines.

📘 SECTION 2 — What’s Included

​

📘 SECTION 4 — Videos with information about the material

IFRS 9 Promotions

IFRS 9 Credit Risk Modeling 2.0 Info

Agent AI for Credit Scoring

IFRS Credit Risk Modeling Sample 1

IFRS 9 Credit Risk Modeling Sample 2