COURSE OBJECTIVE
Intensive course on IFRS 9 credit risk methodologies applying traditional econometric and machine learning models as well as innovative probabilistic machine learning, generative AI and quantum computing models.
The COVID19 pandemic emerged just two years after the 2018 implementation of IFRS 9. The pandemic stressed and affected the predictive power of the models and methodologies, posing significant challenges for creating provisions for impaired assets. In the wake of the pandemic shock, subsequent regulatory and government actions, as well as the recent unprecedented set of risk events such as war, European energy supply insecurity and global inflationary pressures, banks have gradually planned recalibrate the IFRS 9 expected credit loss (ECL) models to improve their accuracy and incorporate lessons learned. However, although adjustments to the models are necessary, new macroeconomic shocks continue to appear, influenced by high uncertainty.
Entities have faced several challenges. The first was the significant increase in credit risk (SICR) that was based on inaccurate or incomplete information. Second, the probability of default (PD) was not sensitive enough to forwardlooking and nonlinear information. Third, banks applied overlays more frequently, but did not justify or quantify them.
Some prestigious consulting firms propose to automate more processes, develop challenging models of PDs and ECL expected credit losses.
Therefore, we have created a course with a greater number of lifetime PD estimation and calibration models, we have increased the artificial intelligence models and added models based on quantum algorithms that on the one hand can be challenging models of the traditional ones and that will help measure nonlinear relationships.
However, the core of the course is to explain in detail credit risk methodologies to estimate and calibrate the lifetime parameters of PD, LGD and EAD adjusted to the IFRS 9 standard using econometric models, Bayesian approach, traditional machine learning, quantum machine learning and quantum algorithms.
All models must quantify the uncertainty inherent in financial inferences and predictions to be useful in financial risk management and decision making. Model parameters and outputs can have a range of values with associated probabilities. Therefore, mathematically sound probabilistic models are needed that adapt to inaccuracies and that quantify uncertainties with logical consistency. Therefore, we have included probabilistic machine learning models, that is, machine learning algorithms together with probabilistic modeling and Bayesian decision theory. These algorithms offer modern and powerful solutions in today's complex financial and economic environment.
This course includes more than 12 methodologies and exercises to estimate PD Lifetime in retail, mortgage, SME and corporate portfolios, for example, the Exogenous Maturity Vintage EMV model, Markov models, survival models, transition matrices, Deep Learning, Monte Carlo simulation quantum algorithms among others.
Forecasting and stress testing methodologies have been incorporated to generate forward looking economic scenarios. Regarding the subject, there are several modules dedicated to the design of scenarios where the interaction between the macroeconomic variables and the Lifetime PD are exposed. In addition, stress testing methodologies for IFRS 9 credit risk provisions are explained.
Regarding the LGD Lifetime, machine learning models are shown to improve the accuracy of the parameters. And regarding the EAD Lifetime, vintage models for lines of credit are explained, as well as econometric models for prepayment.
A pricing tool is delivered, which includes the estimate of ECL 12m and ECL Lifetime, regulatory capital, Raroc and Hurdle rate.
Quantum Machine Learning is the integration of quantum algorithms within Machine Learning programs. Machine learning algorithms are used to compute vast amounts of data, quantum machine learning uses qubits and quantum operations or specialized quantum systems to improve the speed of computation and data storage performed by algorithms in a program. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning. A quantum neural network has computational capabilities to decrease the number of steps, the qubits used, and the computation time.
The aim of this course is to demonstrate how quantum computing and tensor networks can enhance the accuracy of machine learning algorithms.
We will showcase how quantum algorithms accelerate the calculation of Monte Carlo simulation, which is a powerful tool for developing credit risk models. This provides a significant advantage in calculating economic capital, lifetime PD, and creating stress testing scenarios.
In this course, we will compare classical models with quantum models and explain their scope, benefits, and opportunities.
To facilitate learning, we will deliver most scripts in Jupyter Notebook, which is an interactive web environment for running R and Python code. This includes videos, images, formulas, and other resources that will aid in the analysis and explanation of the methodologies.
WHO SHOULD ATTEND?
This program is designed for risk managers, analysts, and consultants who are involved in developing, validating, or auditing IFRS 9 credit risk models. It is also suitable for anyone who is interested in this topic. To fully comprehend the subject matter, it is recommended that participants have a background in statistics and mathematics. You can benefit from quantum computing technologies without needing to have knowledge of quantum physics.
Schedules:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Price: 7 900 €
Summer Price: 5 900 €
Modular Price *

10 Modules: 2 900 €

15 Modules: 3 900 €

20 Modules: 4 900 €

25 Modules: 5 900 €
* You get to choose the specific modules that interest you
Level: Advanced
Duration: 39 h
Material:

Presentations in PDF

Exercises in Excel, R, Python y Jupyterlab

The recorded video of the 39hour course is delivered.
Forecasting Default Rate with Long Short Term Memory LSTM and Quantum LSTM
AGENDA
IFRS 9: Credit Risk Modeling 2.0
IFRS 9
Module 0: Implementation of IFRS 9 in the EU

SICR assessment approaches

Approaches for determining stage transfers

Alignment between the Definition of Default and IFRS 9 exposures in Stage 3

Low credit risk exemption

12month PD as proxy for lifetime PD


Expected Credit Loss Models

Types of Expected Credit Loss models

Model limitations and use of overlays

Effects from the Russian/Ukrainian conflict

ESG including climate risks


IFRS 9 PD variability and robustness

Variability in the IFRS 9 PD

Differences in the use of IRB models for IFRS 9 estimates

Differences in the definition of default

Differences in risk differentiation

Differences in risk quantification

Treatment of 20202021 defaults


Incorporation of forwardlooking information

Macroeconomic scenarios

Variability of the methodological approach for incorporation of FLI and
reflection of nonlinearity
Incorporation of FLI at parameter level

List of macroeconomic variables used for FLI incorporation

Forecasting period and reversion to longterm average


Variability in the impact and different sensitivities from FLI

Effect of nonlinearity and probability framework


Focus on backtesting practices

Staging allocation

ECL measurement

IFRS 9 LGD estimates

IFRS 9 PD estimates

Overlays

Forwardlooking information

CREDIT SCORING
Module 1: Exploratory Analysis

Exploratory Data Analysis EDA

Data sources

Data review

Target definition

Time horizon of the target variable

Sampling

Random Sampling

Stratified Sampling

Rebalanced Sampling


Exploratory Analysis:

histograms

Q Q Plot

Moment analysis

boxplot


Treatment of Missing values

Multivariate Imputation Model


Advanced Outlier detection and treatment techniques

Univariate technique: winsorized and trimming

Multivariate Technique: Mahalanobis Distance


Over and Undersampling Techniques

Random oversampling

Synthetic minority oversampling technique (SMOTE)

Module 2: Feature engineering

Feature engineering

Data Standardization

Variable categorization

Equal Interval Binning

Equal Frequency Binning

ChiSquare Test


Binary coding

WOE Coding

WOE Definition

Univariate Analysis with Target variable

Variable Selection

Treatment of Continuous Variables

Treatment of Categorical Variables

Information Value

Optimization of continuous variables

Optimization of categorical variables


Exercise 1: EDA Exploratory Analysis

Exercise 2: Feauture 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
Machine Learning
Module 3: Machine Learning

Unsupervised models

K Means

Principal Component Analysis (PCA)

Advanced PCA Visualization

Supervised Models

Ensemble Learning

Bagging trees

Random Forest

Boosting

Adaboost

Gradient Boosting Trees

Exercise 7: Segmentation data using KMeans

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
DEEP LEARNING
Module 4: Deep Learning

Feed Forward Neural Networks

Single Layer Perceptron

Multiple Layer Perceptron

Neural network architectures

Activation function

Back propagation

Directional derivatives

gradients

Jacobians

Chain rule

Optimization and local and global minima


Deep Learning Convolutional Neural Networks CNN

CNN for pictures

Design and architectures

convolution operation

filters

strider

padding

Subsampling

pooling

fully connected

Credit Scoring using CNN

Recent CNN studies applied to credit risk and scoring


Deep Learning Recurrent Neural Networks RNN

Long Term Short Term Memory (LSTM)

Hopfield

Bidirectional associative memory

descending gradient

Global optimization methods

RNN and LSTM for credit scoring

Oneway and twoway models


Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

Fundamental components of the GANs

GAN architectures

Bidirectional GAN

Training generative models

Credit Scoring using GANs


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

Hyperparameterization

Grid search

Random search

Bayesian Optimization

Train test split ratio

Learning rate in optimization algorithms (e.g. gradient descent)

Selection of optimization algorithm (e.g., gradient descent, stochastic gradient descent, or Adam optimizer)

Activation function selection in a (nn) layer neural network (e.g. Sigmoid, ReLU, Tanh)

Selection of loss, cost and custom function

Number of hidden layers in an NN

Number of activation units in each layer

The dropout rate in nn (dropout probability)

Number of iterations (epochs) in training a nn

Number of clusters in a clustering task

Kernel or filter size in convolutional layers

Pooling size

Batch size

Interpretation of the Shap model

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

Scoring assignment

Scorecard Classification

Scorecard WOE

Binary Scorecard

Continuous Scorecard


Scorecard Rescaling

Factor and Offset Analysis

Scorecard WOE

Binary Scorecard


Reject Inference Techniques

cutoff

parceling

Fuzzy Augmentation

Machine Learning


Advanced Cut Point Techniques

Cutoff optimization using ROC curves


Exercise 20: Creating a scorecard using Excel, R, and Python
QUANTUM COMPUTING
Module 7: Quantum Computing and Algorithms

Future of quantum computing in banking

Is it necessary to know quantum mechanics?

QIS Hardware and Apps

Quantum operations

Qubit representation

Measurement

Overlap

Matrix multiplication

Qubit operations

Multiple Quantum Circuits

Entanglement

Deutsch Algorithm

Quantum Fourier transform and search algorithms

Hybrid quantumclassical algorithms

Quantum annealing, simulation and optimization of algorithms

Quantum machine learning algorithms

Exercise 21: Quantum operations
QUANTUM MACHINE LEARNING
Module 8: Development of Credit Scoring using Quantum Machine Learning

What is quantum machine learning?

Qubit and Quantum States

Quantum Automatic Machine Algorithms

Quantum circuits

K means quantum

Support Vector Machine

Support Vector Quantum Machine

Variational quantum classifier

Training quantum machine learning models

Quantum Neural Networks

Quantum GAN

Quantum Boltzmann machines

Quantum machine learning in Credit Risk

Quantum machine learning in credit scoring

Quantum software

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

What are tensor networks?

Quantum Entanglement

Tensor networks in machine learning

Tensor networks in unsupervised models

Tensor networks in SVM

Tensor networks in NN

NN tensioning

Application of tensor networks in credit scoring models

Exercise 25: Construction of credit scoring and PD using tensor networks
PROBABILISTIC MACHINE LEARNING
Module 10: Probabilistic Machine Learning

Probability

Gaussian models

Bayesian Statistics

Bayesian logistic regression

Kernel family

Gaussian processes

Gaussian processes for regression


Hidden Markov Model

Markov chain Monte Carlo (MCMC)

Metropolis Hastings algorithm


Machine Learning Probabilistic Model

Bayesian Boosting

Bayesian Neural Networks

Exercise 26: Gaussian process for regression

Exercise 27: Bayesian neural networks
ADVANCED VALIDATION
Module 11: Advanced Validation of AI Models

Integration of stateoftheart methods in interpretable machine learning and model diagnosis.

Data Pipeline

Feature Selection

Blackbox Models

Posthoc Explainability

Global Explainability

Local Explainability

Model Interpretability

Diagnosis: Accuracy, WeakSpot, Overfit, Reliability, Robustness, Resilience, Fairness

Model comparison

Comparative for Regression and Classification

Fairness Comparison


Exercise 28: Validation and diagnosis of advanced credit scoring models
IFRS 9 : EXPECTED CREDIT LOSSES
Probability of Default
Module 12: Probability of Default PD

PD estimation

econometric models

Machine Learning Models

Data requirement

Risk drivers and credit scoring criteria

Rating philosophy

Pool Treatment


PD Calibration

Default Definition

Long run average for PD

Technical defaults and technical default filters

Data requirement

One Year Default Rate Calculation

LongTerm Default Rate Calculation


PD Model Risk

Conservatism Margin


PD Calibration Techniques

Anchor Point Estimate

Mapping from Score to PD

Adjustment to the PD Economic Cycle

Rating Philosophy

PD Trough The Cycle (PD TTC) models

PD Point in Time PD (PD PIT ) models



PD Calibration of Models Using Machine and Deep Learning

Margin of Caution

Exercise 29: Modeling the Margin of Caution PD
Module 14: Econometric and AI Models of PD

PD estimation

Treatment of Panel data

Econometric models to estimate PD

PD Logistic Regression

PD Probit Regression

PD COX regression of survival

PD Loglog Complementary

PD Regression Data Panel

PD Bayesian Logistic Regression


Machine Learning models to estimate PD

Cox XGBoost

Survival Tree

Random Survival Forest

Deep Learning Survival

PD Neural Networks

PD Quantum Neural Networks


PD Calibration

Calibration of econometric models

Anchor Point Estimate

PD calibration by vintages or vintages

Vintage analysis

PD Marginal

PD Forward

Cumulative PD


Econometric Models

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

Machine Learning and Quantum Machine Learning

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

Concept of adjustment to central tendency

Bayesian approach

PD calibration in developed countries

PD calibration in emerging countries

Scaled PD Calibration

Scaled Likelihood ratio calibration

Smoothing of PD curves

Quasi moment matching

Approximation methods

Scaled beta distribution

Asymmetric Laplace distribution


Rubber function

Platt scaling

Broken curve model

Isotonic regression

Gaussian Process Regression

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

Bayesian and deterministic approach

Expert judgment

Prior distributions

Bayes' theorem

Posterior distributions

Bayesian PD Estimation

Markov Chain–Monte Carlo MCMC approach

Credibility intervals

Bayesian PD in practice

Calibration with Bayesian approach

Process Gaussian regression

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)

Confidence interval approach for PD LDP

PD estimation without correlations

PD estimation with correlations

Oneperiod and multiperiod estimation


Bayesian PD estimation for LDP

Neutral Bayesian

Conservative Bayesian

expert judgment


Real analysis of PD of Corporate, Sovereign and Retail portfolios

LASSO regression to measure corporate default rate

Generating synthetic data for LDP using GAN

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
LIFETIME PD IFRS 9
Module 18: Transition Matrices and Temporary Structure of PD

Temporary structure of PD in IFRS 9

Properties of transition matrices

Markov chains

Multiyear transition matrix

discrete time

continuous time

Generating Matrix

Exponential of a matrix


Duration method

Cohort method

Error management

PD temporary structure

Calibration of the temporal structure of the PD

Levenberg–Marquardt Algorithm

Economic Cycles

Calibration of the temporal structure of the PD for LDP

Exercise 51: Analysis and error exercise of Transition Matrix using cohort and duration approach in Python

Exercise 52: Calibration of the temporal structure of the PD
Module 19: Lifetime PD Models

PD Lifetime consumer portfolio

PD Lifetime mortgage portfolio

PD Lifetime Wallet Credit Card

PD Lifetime portfolio SMEs

vintage model

Exogenous Maturity Vintage EMV Model

decomposition analysis

Advantages and disadvantages


Basel ASRF model

Matrix ASRF model

Leveraging IRB in IFRS 9

Advantages and disadvantages


Regression Models

Logistic Multinomial Regression

Ordinal Probit Regression


Survival Models

Kaplan–Meier

Cox regression

Advantages and disadvantages


Markov models

MultiState Markov Model

Cox Semiparametric Model

Advantages and disadvantages


Machine Learning Model

SVM: Kernel Function Definition

Neural Network: definition of hyperparameters and activation function


Quantum Machine Learning Models

PD Lifetime Extrapolation Models

Exercise 53: using vintage EMV Decomposition model for estimating PD Lifetime

Exercise 54: using multinomial regression for estimating PD Lifetime

Exercise 55: using MultiState Markov Model for estimating PD Lifetime

Exercise 56: using matrix ASRF model for estimating PD Lifetime

Exercise 57: using SVM in Python for estimating PD Lifetime

Exercise 58: using Neural Network in Python for estimating PD Lifetime

Exercise 59: using Quantum Neural Network in Python for estimating PD Lifetime

Exercise 60: using Quantum SVM in Python for estimating PD Lifetime
Module 20: Advanced Calibration Lifetime PD

Calibration by nonlinear equation systems

Temporal structure calibration with Vasicek model

Calibration of transition matrices using Vasicek

Bayesian calibration

Fitting curve distributions

Extrapolation Calibration

Gamma Adjustment

exponential accelerator

Lifetime PD Recalibration


Nelson Siegel Calibration

Exercise 61: Lifetime Advanced Calibration Models PD Nelson Siegel

Exercise 62: Lifetime PD Advanced Calibration Models Gamma Adjustment

Exercise 63: Factor Fit Calibration

Exercise 64: Vasicek model calibration
LIFETIME PD QUANTUM
Module 21: Estimating Lifetime PD using Quantum Computing

PD lifetime modeling

Exogenous Maturity Vintage

Age Period Cohort

Classic Monte Carlo simulation

Quantum Monte Carlo Simulation

Quadratic acceleration over the classical Monte Carlo simulation

PD lifetime modeling

Monte Carlo Markov Chain MCMC

Quantum enhancement in MCMC

Exercise 65: Lifetime PD IFRS 9 estimation using quantum enhancements
LGD IFRS 9
Module 22: LGD IRB in Retail Portfolios

Impact of COVID19 on LGD

definition of default

moratoriums

Renovations and restructuring

Default Cycle

Real Default Cycles


Expected Loss and Unexpected Loss in the LGD

LGD in Default

Default Weighted Average LGD or Exposureweighted average LGD

LGD for performing and nonperforming exposures

Treatment of collaterals in the IRB

Workout approach

Techniques to determine the discount rate

Treatment of recoveries, expenses and recovery costs

Default Cycles

recovery expenses


Downturn LGD in consumer portfolios

Downturn LGD in Mortgages

LGD in consumption

LGD in Mortgages

LGD in companies

LGD for portfolios with replacement
Module 23: Econometric and machine learning models are used to estimate the Loss Given Default (LGD)

Advantages and disadvantages of LGD Predictive Models

Forward Looking models incorporating Macroeconomic variables

Parametric and nonparametric models and transformation regressions

Typology of LGD Multivariate Models

Linear regression and Beta transformation

Linear Regression and Logit Transformation

Linear regression and Box Cox transformation

Logistic and Linear Regression

Logistic and nonlinear regression

Censored Regression

Generalized Additive Model

Beta regression

Inflated beta regression


Support Vector Regression

Support Vector Classification

Random Forest Regression

XGBoosting Regression

Neural networks

Deep learning

Exercise 66: econometric models for estimating LGD:

Logistic and Linear Regression

Beta regression


Exercise 67: Machine Learning and Deep Learning Models for estimating LGD:

Random Forest Regression

XGBoosting Regression

Deep learning Regression


Exercise 68: Comparison of the performance of the models using Calibration and precision tests.
Module 24: LGD models in IFRS 9

Comparison of IRB LGD vs. IFRS 9

Impact on COVID19

IFRS 9 requirements

Probability Weighted

Forward Looking


IRB LGD adjustments

Selection of Interest Rates

Allocation of Costs

floors

Treatment of collateral over time

Duration of COVID19


LGD PIT modeling

Collateral Modeling

LGD IFRS 9 for portfolio companies

LGD IFRS 9 for mortgage portfolio

LGD IFRS 9 for corporate portfolios

credit cycle

Tobit Regression


IFRS 9 LGD using LASSO Regression

Machine Learning Models

Support Vector Machine

Neural Networks


Exercise 69: using Tobit regression in R for estimating LGD IFRS 9

Exercise 70: using Neural Networks regression and classification for estimating LGD IFRS 9

Exercise 71: using Support Vector Machine regression and classification for estimating LGD IFRS 9
EAD IFRS 9
Module 25: Advanced EAD and CCF modeling in IFRS 9

Impact of COVID19 on credit lines

Guidelines for estimating CCF

Guidelines for Estimating CCF Downturn

Temporal horizon

Transformations to model the CCF

Approaches to Estimating CCF

Fixed Horizon approach

Cohort Approach

Variable focus time horizon


Econometric Models

Beta regression

Inflated beta regression

Fractional Response Regression

Mixed Effect Model


Machine Learning Models

neural networks

SVM


Intensity model to measure the withdrawal of credit lines

Exercise 72: using OLS Regression Model for estimating CCF

Exercise 73: using CCF Logistic Regression Model for estimating CCF

Exercise 74: using Neural Networks and SVM for estimating CCF
EAD IFRS 9
Module 26: Prepayment Rate Modeling

Prepaid and other options

IFRS 9 requirements

Probability Weighted

Forward Looking

IFRS 9 prepayment modeling

Cox regression

Logistic regression


Survival rate estimate

Joint Probability Model with Prepayment rate and Lifetime PD

Exercise 75: Modeling IFRS 9 prepayment model for mortgage portfolios in R and Excel
Module 27: Lifetime EAD for credit lines

Impact of the pandemic on the use of credit lines

Lifetime measurement in credit cards

Lifetime EAD

IFRS 9 requirements

Probability Weighted

Forward Looking

Adjustments in the EAD

Interest Accrual

Estimating CCF PIT

Lifetime CCF Estimate

lifetime EAD modeling

Model of the use of credit lines with macroeconomic variables

Credit card abandonment adjustment

Lifetime EADmodel for pool of credit lines

vintage model

Chain Ladder Approach


Exercise 73: Econometric model of credit line usage in R

Exercise 74: Lifetime EAD model for individual line of credit

Exercise 75: Vintage Lifetime EAD Model for Pool of Credit Lines in R and Excel
STRESS TESTING ECL
Module 28: Preparation of Econometric Models

Review assumptions of econometric models

Review the coefficients and standard errors of the models

Model reliability measures

Error management

Normal test

heteroscedasticity

Outliers

autocorrelation

Using Correlation to detect bivariate collinearity

Identifying the existence of multivariate collinearity in linear regression models.
 Identifying the presence of multivariate collinearity in logistic regression.

Exercise 76: Detecting nonstationary series, identifying cointegration, and detecting outliers.

Exercise 77: Measurement of collinearity,

Exercise 78: Measurement of heteroskedasticity

Exercise 79: Measuring Serial Autocorrelation
AI STRESS TESTING
Module 29: Deep Learning for MacroEconomic Dynamics Modeling

Macroeconomic models

Neoclassical growth model

Partial differential equations

DSGE Stochastic Dynamic General Equilibrium Models

Deep learning architectures

Reinforcement Learning

Advanced Scenario Analysis

Exercise 80: using neural networks for Bellman equation macroeconomic model
STRESS TESTING PD and LGD
Module 30: Using AI for Forecasting and Stress Testing of PD and LGD

Forecasting models

Multivariate Models

VAR Autoregressive Vector Models

ARCH models

GARCH models

GARCH Models Multivariate Copulas

VEC Error Correction Vector Model

Johansen's method


Machine Learning Models

Supported Vector Machine

Neural network

Multivariate Adaptive Regression Splines

Development and validation base


Deep learning

Recurrent Neural Networks RNN

Elman's Neural Network

Jordan Neural Network

Basic structure of RNN

Long short term memory LSTM

temporary windows

Development and validation sample

Regression

Sequence modeling

Machine Learning learning and Quantum Machine Learning for forecasting PD

Exercise 81: Forecasting PD using Random Forest

Exercise 82: Forecasting PD using Neural Networks

Exercise 83: Forecasting PD using LSTM

Exercise 84: Forecasting PD using QUANTUM LSTM

Exercise 85: using RNN LSTM for stress testing Chargeoff model

Exercise 86: Forecasting PD with Pandemic variables and climate change using RNN LSTM in Python
Probabilistic Machine Learning for Stress testing PD

Exercise 87: using Bayesian Additive Regression Trees for Stress Testing PD

Exercise 88: using Bayesian Support Vector Machine for Stress Testing PD

Exercise 89: using Bayesian Neural Networks for Stress Testing PD
Module 31: Classical methodologies for stress testing Probability of Default (PD) and Loss Given Default (LGD).

Temporal horizon

Multiperiod approach

Data required

Impact on P&L, RWA and Capital

Machine Learning Models

Probabilistic Machine Learning Models

Macroeconomic Stress Scenarios in consumption

Expert

Statistical

regulatory


Stress Testing PD:

Credit Portfolio View

Multiyear Approach

Reverse Stress Testing

Rescaling

Cox regression


Stress Testing of the Transition Matrix

Approach Credit Portfolio View

credit cycle index

Multifactor Extension


Stress Testing LGD :

Downturn LGD : Mixed Distribution Approach

Multiyear Approach PD/LGD modeling

stress testing LGD for mortgage portfolios


Stress Testing of:

Defaults

Charge Off

Net Charge Off

Roll Rates

Rating/Scoring transition matrices

Delinquency bucket transition matrices

Recovery Rate and LGD

Losses on new impaired assets

Losses on old impaired assets


Exercise 91: Stress Testing PD in Excel and SAS multifactorial model Credit Portfolio Views

Exercise 92: Stress Testing PD in SAS Multiyear Approach

Exercise 93: Stress test of PD and Autoregressive Vectors

Exercise 94: Stress Test LGD

Exercise 95: Joint Stress Test of the PD and LGD

Exercise 96: Chargeoff model using VAR and VEC
Module 32: Stress Testing in corporate portfolios

Temporal horizon

Data required

Main Macroeconomic variables

Impact on P&L, RWA and Capital

ASRF model

Creditmetrics model

Using Transition Matrices

Use of the credit cycle index

Default forecasting

Stress Test Methodology for corporate portfolios

Impact on RWA and Capital

Exercise 97: Stress Testing PD and corporate portfolio transition matrices using transition matrix and ASRF model in SAS, R and Excel
STRESS TESTING ECL IFRS 9
Module 33: Stress Testing under ECL IFRS 9

Stress testing under IFRS 9 and COVID19

Pandemic scenarios applied to the ECL calculation

Stress Testing of IFRS 9 parameters

EBA Stress Testing 2023

Treatment of the moratorium

Possible regulatory scenarios

Impact on P&L

PIT starting parameters

PIT projected parameters

Calculation of nonproductive assets and impairments

Changes in the stock of provisions

Changes in the stock of provisions for exposures phase S1

Changes in the stock of provisions for exposures phase S2

Changes in the stock of provisions of exposures phase S3

Sovereign Exposure Impairment Losses

Impact on capital

Internal Stress Testing Model for ECL IFRS 9

Stage Migration

Stage Transition Matrix

Stress testing of PDs and credit migrations

Stress testing of exhibitions

Stress testing of recoveries

Exercise 98: Internal global exercise of ECL Stress Testing in R and Excel
QUANTUM COMPUTING FOR STRESS TESTING AND
MONTE CARLO SIMULATION
Module 34: Quantum Computing for Stress Testing

Quantum economics

Classic Monte Carlo simulation

Quantum Monte Carlo

Coding Monte Carlo problem

Breadth Estimation

Acceleration applying the amplitude estimation algorithm

DGSE model using neural networks

Quantum Monte Carlo Simulation vs Normal Monte Carlo Simulation

Exercise 95: DGSE model using deep learning

Exercise 96: Quantum Monte Carlo Simulation vs. Classical Monte Carlo Simulation ECL in Stress Testing
Significant Increase in Credit Risk SICR in IFRS 9
Module 35: Significant Increase in Credit Risk SICR

Significant increase in credit risk (SICR)

Impact of COVID19 on the increase in SICR risk

Recommendations Basel, EBA, ESMA, IFRS

Qualitative and quantitative criteria based on COVID19

Increase in collective credit risk

Individual IFRS 9 credit risk increase

Phase migration matrices

Roll rate models

Markov model


Impact of COVID19 on migrations

Estimation of PD Lifetime and PD Origination thresholds

Rating Variation

Determination of thresholds

KRIs for retail, mortgages and corporate

Increase in collective IFRS 9 credit risk

Use of discriminant test

ROC curve

false alarm rate

target hit rate

S2 size


Exercise 97: Estimating SICR credit risk increase using ROC discriminant power test in R and Excel.
IFRS 9 ECL MODEL
Module 36: Models for estimating the Lifetime
expected credit losses ECL

Macroeconomic scenarios impacted by COVID19

Lifetime Loss Forecasting using macroeconomic variables

Global Exercise 98: Estimating Lifetime Expected Credit Losses for a Consumer Credit Portfolio using R, Excel, and VBA.

Definition of macroeconomic scenarios COVID19

Impact of the scenarios on the estimate for COVID19

LGD modeling using economic scenarios

CCF modeling using economic scenarios

Abandonment Modeling

Prepayment Modeling

PD PIT modeling with economic scenarios

Lifetime PD Modeling

Estimate of financial income

Cash flow modeling

Estimated survival rate

12month ECL Expected Loss Estimate

12month ECL estimate COVID19 effect

ECL Lifetime Expected Loss Estimate IFRS 9 COVID19

Stress Testing of credit risk losses

Assignment analysis of the 3 stages

Comparison of ECL Estimates

Interpretation of results in the scorecard
IFRS 9
Validation of Expected Credit Loss
Module 37: Validation of ECL

Initial validation

Periodic validation

Monitoring

Main milestones of qualitative validation

Data quality

Default Definition

Relevance of the qualification process

Override Analysis

environmental dynamics

user test


Main milestones of quantitative validation

Samples used for validation purposes

Discriminating Power

population stability

Characteristic Stability

concentration analysis

Staging analysis

Parameter Calibration

ECL backtesting


Principle 5 – Validation

Exercise 99: Global Validation of ECL IFRS 9
GENERATIVE AI IN ECL IFRS 9
Module 38: Generative AI

Introducing generative AI

What is Generative AI?

Generative AI Models

Generative Pre trained Transformer (GPT)


Text generation, Image generation, Music generation, Video generation

Generating text

Generating Code

Ability to solve logic problems

Generating Music

Enterprise Use Cases for Generative AI

Overview of Large Language Models (LLMs)

Transformer Architecture

Types of LLMs

OpenSource vs. Commercial LLMs

Key Concepts of LLMs


Prompts

Tokens

Embeddings

Model configuration

Prompt Engineering

Model adaptation

Emergent Behavior

Specifying multiple Dataframes to ChatGPT

Debugging ChatGPT’s code
Human errors 
Exercise 100: Embeddings for words, sentences, question answers

Exercise 101: Embedding Visualization

Exercise 102: First let's prepare the data for visualization

Exercise 103: PCA (Principal Component Analysis)

Exercise 104: Embeddings on Large Dataset

Exercise 105: Prompt engineering

Exercise 106: Advanced Prompting Techniques

Exercise 107: Large Language Models (LLMs)
GENERATIVE AI in ECL IFRS 9

Exercise 108: Modeling Lifetime PD using generative AI

Exercise 109: Using Transformers for forecasting Lifetime PD

Exercise 110: Analysis of ECL results using generative AI

Exercise 111: Applications of autoregresive LLM in ECL IFRS 9