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The Future of Credit Risk:

AI, Quantum Algorithms,

and Beyond





Advanced and intensive course on credit risk modeling using artificial intelligence and quantum computing, among many other topics: credit scoring tools, modeling of PD, LGD and EAD parameters of the advanced IRB approach of Basel III, credit risk methodologies for IFRS 9 impairment models, stress testing models of credit risk and economic capital. The impact of COVID-19 on credit risk models is explained.


Machine and deep learning are used to build powerful credit scoring and behavior scoring tools, as well as to estimate and calibrate risk parameters and stress testing.


A module on advanced data processing is exposed, explaining among other topics: sampling, exploratory analysis, outlier detection, advanced segmentation techniques, feature engineering and classification algorithms.

The course explains the recent final reforms of Basel III regarding the new standard approach and Advanced IRB, IFRS 9 related to credit risk and the new guidelines on estimation of PD and LGD and treatment of exposures in default of EBA.


Predictive machine learning models are shown such as: decision trees, neural networks, Bayesian networks, Support Vector Machine, ensemble model, etc. And in terms of neural networks, feed forward, recurrent RNN, convoluted CNN and adversarial Generative architectures are exposed. In addition, Probabilistic Machine Learning models such as Gaussian processes and Bayesian neural networks have been included.


Advanced methodologies are taught to estimate and calibrate risk parameters: PD, LGD and EAD. The Lifetime PD estimate used in the IFRS 9 impairment models is exposed.


Methodologies and practical exercises of Stress testing in credit risk using advanced techniques of machine learning and deep learning are shown. And a practical exercise with financial statements to understand the impact of stress testing on capital and profits.

We have a global exercise to estimate the expected loss at 12 months and ECL lifetime using advanced credit risk methodologies, including PD, LGD, EAD, prepayment and interest rate curve models.

The course shows economic capital methodologies in credit card, mortgage, SME and Corporate portfolios. As well as capital allocation methodologies.


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 objective of the course is to show the use of quantum computing and tensor networks to improve the calculation of machine learning algorithms.

We show how quantum algorithms speed up the calculation of Monte Carlo simulation, the most powerful tool for developing credit risk models, representing an important advantage for calculating economic capital, lifetime PD and creating stress testing scenarios.

The objective of the course is to expose classical models against quantum models, explain the scope, benefits and opportunities.


The Course is aimed at professionals from financial institutions of credit risk and financial risks. For a better understanding of the topics, it is recommended that the participant have knowledge of statistics and credit risk.





Price: 6.900 €




  • Europe: Mon-Fri, CEST 16-19 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h






Level: Advanced


Duration: 40 h




  • Presentations PDF

  • Exercises in Excel, R, Python y Jupyterlab 



The Future of Credit Risk:  AI, Quantum Algorithms,

and Beyond



Anchor 10


​Module -1: Quantum Computing and Algorithms (Optional)

  • Future of quantum computing in banking

  • Is it necessary to know quantum mechanics?

  • QIS Hardware and Apps

  • quantum operations

  • qubit representation

  • measurement

  • overlapping

  • matrix multiplication

  • Qubit operations

  • Multiple Quantum Circuits

  • Entanglement

  • German Algorithm

  • Quantum Fourier transform and search algorithms

  • Hybrid quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Exercise 1: Quantum operations



Módulo 0: Análisis Exploratorio 

  • Exploratory Data Analysis EDA

  • Fuentes de datos

  • Revisión del dato

  • Definición del Target

  • Horizonte temporal de la variable objetivo

  • Muestreo

    • Muestreo Aleatorio

    • Muestreo Estratificado

    • Muestreo Rebalanceado

  • Análisis Exploratorio:

    • Histogramas

    • Q-Q Plot

    • Análisis de momentos

    • Box Plot

  • Tratamiento de los valores Missing

    • Modelo Multivariante de Imputación

  • Técnicas avanzadas de detección de Outliers y tratamiento

    • Técnica univariante: winsorized y trimming

    • Técnica Multivariante: Distancia de Mahalanobis

Module 1: Feature engineering

  • Feature engineering

  • Data Standardization

  • Variable categorization

    • Equal Interval Binning

    • Equal Frequency Binning

    • Chi-Square Test

  • binary coding

  • WOE Coding

    • WOE Definition

    • Univariate Analysis with Target variable

    • Variable Selection

    • Treatment of Continuous Variables

    • Treatment of Categorical Variables

    • gini

    • Information Value

    • Optimization of continuous variables

    • Optimization of categorical variables

  • ​Exercise 1: EDA Exploratory Analysis

  • Exercise 2: Detection and treatment of Advanced Outliers

  • Exercise 3: Multivariate model of imputation of missing values

  • Exercise 4: Univariate analysis in percentiles in R

  • Exercise 5: Continuous variable optimal univariate analysis in Excel

  • Exercise 6: Estimation of the KS, Gini and IV of each variable in Excel


Unsupervised Learning

Module 2: Unsupervised models

  • Hierarchical Clusters

  • K Means

  • standard algorithm

  • Euclidean distance

  • Principal Component Analysis (PCA)

  • Advanced PCA Visualization

  • Eigenvectors and Eigenvalues

  • Exercise 7: Segmentation of the data with K-Means R

Supervised Learning

Module 3: Logistic Regression and LASSO Regression


  • Econometric Models

    • Logit regression

    • probit regression

    • Piecewise Regression

    • Survival models

  • Machine Learning Models

    • Lasso Regression

    • Ridge Regression

  • Model Risk in Logistic Regression

  • Exercise 8: Credit Scoring Logistic Regression in SAS and R

  • Exercise 9: Credit Scoring Lasso Logistic Regression in R

  • Exercise 10: Model Risk Using Confidence Intervals of Logistic Regression Coefficients

Module 4: Trees, KNN and Naive Bayes

  • Decision Trees

    • Modeling

    • Advantages and disadvantages

    • Recursion and Partitioning Processes

    • Recursive partitioning tree

    • Pruning Decision tree

    • Conditional inference tree

    • Tree display

    • Measurement of decision tree prediction

    • CHAID model

    • Model C5.0

  • K-Nearest Neighbors KNN

    • Modeling

    • Advantages and disadvantages

    • Euclidean distance

    • Distance Manhattan

    • K value selection

  • Probabilistic Model: Naive Bayes

    • Naive bayes

    • Bayes' theorem

    • Laplace estimator

    • Classification with Naive Bayes

    • Advantages and disadvantages

  • Exercise 11: Credit Scoring KNN in R

Module 5: Support Vector Machine SVM

  • SVM with dummy variables

  • SVM

  • Optimal hyperplane

  • Support Vectors

  • Add costs

  • Advantages and disadvantages

  • SVM visualization

  • Tuning SVM

  • Kernel trick

  • Exercise 12: Credit Scoring Support Vector Machine

Module 6: Ensemble Learning

  • Set models

  • Bagging

  • Bagging trees

  • Random Forest

  • Boosting

  • Adaboost

  • Gradient Boosting Trees

  • Advantages and disadvantages

  • Exercise 14: Credit Scoring Boosting in R

  • Exercise 15: Credit Scoring Bagging in R

  • Exercise 16: Credit Scoring Random Forest, R and Python

  • Exercise 17: Credit Scoring Gradient Boosting Trees


​Module 7: Deep Learning Feed Forward Neural Networks

  • Single Layer Perceptron

  • Multiple Layer Perceptron

  • Neural network architectures

  • Activation function

    • Sigmoidal

    • Rectified linear unit (Relu)

    • The U

    • Selu

    • hyperbolic hypertangent

    • Softmax

    • Other

  • Back propagation

    • Directional derivatives

    • Gradients

    • Jacobians

    • Chain rule

    • Optimization and local and global minima

  • Exercise 18: Credit Scoring using Deep Learning Feed Forward

Module 8: Deep Learning Convolutional Neural Networks CNN

  • CNN for pictures

  • Design and architectures

  • Convolution operation

  • Descending gradient

  • Filters

  • Strider

  • Padding

  • Subsampling

  • Pooling

  • Fully connected

  • Credit Scoring using CNN

  • Recent CNN studies applied to credit risk and scoring

  • Exercise 19: Credit scoring using deep learning CNN

Module 9: Deep Learning Recurrent Neural Networks RNN

  • Natural Language Processing

  • Natural Language Processing (NLP) text classification

  • Long Term Short Term Memory (LSTM)

  • Hopfield

  • Bidirectional associative memory

  • Descending gradient

  • Global optimization methods

  • RNN and LSTM for credit scoring

  • One-way and two-way models

  • Deep Bidirectional Transformers for Language Understanding​

  • Exercise 20: Credit Scoring using Deep Learning LSTM

Module 10: 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 21: Credit Scoring using GANs

Module 11: Calibrating Machine Learning and 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 drop-out 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 22: Optimization Credit Scoring Xboosting, Random forest and SVM

  • Exercise 23: Credit Scoring Optimized Deep Learning and Model Interpretation

​Module 12: Traditional Scorecard Construction


  • Scoring assignment

  • Scorecard Classification

    • Scorecard WOE

    • Binary Scorecard

    • Continuous Scorecard

  • Scorecard Rescaling

    • Factor and Offset Analysis

    • Scorecard WOE

    • Binary Scorecard

  • Reject Inference Techniques

    • Cut-off

    • Parceling

    • Fuzzy Augmentation

    • Machine Learning

  • Advanced Cut Point Techniques

    • Cut-off optimization using ROC curves

  • Exercise 24: Building Scorecard in Excel, R and Python

  • Exercise 25: Optimum cut point estimation in Excel and model risk by cut point selection




Module 14: Quantum Machine Learning

  • What is quantum machine learning?

  • Qubit and Quantum States

  • Quantum Automatic Machine Algorithms

  • quantum circuits

  • 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 26: Quantum Neural Networks to develop a credit scoring model

Module 15: 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 27: Construction of credit scoring using tensor networks


​Module 16: Probabilistic Machine Learning


  • Introduction to probabilistic machine learning

  • 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 28: Gaussian process for regression

  • Exercise 29: Credit scoring model using Bayesian Neural Networks



​Module 17: Calibration of the 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

    • Long-Term 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 30: PD Calibration Models

  • Exercise 31: PD Calibration in Machine Learning Models

  • Exercise 32: Modeling the Margin for Caution PD


Module 18: Machine Learning models to estimate Lifetime PD under IFRS 9


  • Credit scoring models to estimate Lifetime PD

  • PD Lifetime in IFRS 9

  • Impact of COVID-19 on models

  • Climate Risk Impact

  • Inflation impact

  • Impact of rising prices

  • Regression Models

    • Logistic regression

    • Logistic Multinomial Regression

    • Ordinal Probit Regression

  • VAR and VEC models

  • Machine Learning Model​

    • SVM: Kernel Function Definition

    • Neural Network: definition of hyperparameters and activation function

    • deep learning

    • LSTM

  • PD Calibration of Models Using Machine and Deep Learning

  • Exercise 33: PD Lifetime using logistic regression

  • Exercise 34: PD Lifetime using multinomial regression in R

  • Exercise 35: PD Lifetime using SVM in Python

  • Exercise 36: PD Lifetime using Deep Learning in Python

  • Exercise 37: PD Lifetime using Deep Learning LSTM in Python

Module 19: LGD IFRS 9

  • LGD for IFRS 9

  • Comparison of regulatory LGD vs. IFRS 9

  • LGD adjustments

  • Selection of Interest Rates

  • Allocation of Costs

  • floors

  • Treatment of collateral over time

  • Marginal LGD


  • Loss Lifetime Concept

  • Lifetime LGD models

  • Exercise 38: Tobit regression, neural networks, SVM to estimate LGD

​Module 20: EAD IFRS 9

  • EAD for IFRS 9

  • Comparison of regulatory EAD vs. IFRS 9

  • Adjustments in the EAD

  • Interest Accrual

  • CCF PIT Estimate

  • Modeling of available lifetime

  • Prepayment modeling

  • Exercise 39: IFRS 9 EAD estimation for credit cards using machine learning

Module 21: Increase in Credit Risk SICR


  • Significant increase in credit risk (SICR)

  • Impact of COVID-19 on the increase in SICR risk

  • Recommendations Basel, EBA, ESMA, IFRS

  • Qualitative and quantitative criteria based on COVID-19

  • Increase in collective credit risk

  • Individual IFRS 9 credit risk increase

  • Phase migration matrices

    • Roll rate models

    • Markov model

  • Impact of COVID-19 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 40: Estimation of SICR credit risk increase using ROC discriminant power test in R and Excel


Module 22: Lifetime PD

  • 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 41: Lifetime PD IFRS 9 estimation using quantum enhancements




Module 23: Preparation of Econometric Models

  • Review of assumptions of econometric models

  • Review of the coefficients and standard errors of the models

  • Model reliability measures

  • Error management

  • not normal

  • heteroskedasticity

  • Outliers

  • Autocorrelation

  • Using Correlation to detect bivariate collinearity

  • Detection of multivariate collinearity in linear regression

  • Detection of multivariate collinearity in logistic regression

  • Exercise 42: Non-stationary series detection and cointegration

  • Exercise 43: Measuring Collinearity, Heteroskedasticity, Serial Autocorrelation, and Outilers



Module 24: Modernizing macroeconomic dynamics using Deep Learning


  • Macroeconomic models

  • ​Neoclassical growth model

  • Partial differential equations

  • DSGE Stochastic Dynamic General Equilibrium Models

  • Deep learning architectures

  • Reinforcement Learning

  • Advanced Scenario Analysis

  • Exercise 44: Bellman equation macroeconomic model using neural networks

Module 25: Deep Learning models for macroeconomic projections


  • ​Trading strategies with 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

      • Forecasting market time series yields

      • NN and SVM algorithms for performance forecasting

      • Forecasting volatility NN vs. Garch

    • 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

  • Time series analysis with Facebook Prophet​

  • Prediction of the spread of Covid-19

  • Exercise 45: Forecasting GARCH volatility in Python

  • Exercise 46: Forecasting GARCH Multivariate volatility in R

  • Exercise 47: Forecasting financial series with Machine Learning using python

  • Exercise 48: Forecasting financial series and indices using Recurrent Neural Networks in Python

  • Exercise 49: Forecasting the Pandemic using RNN LSTM in Python

  • Exercise 50: Charge-off model with VAR and VEC

  • Exercise 51: Charge-off model with RNN LSTM


Module 26: Stress Testing PD and LGD

  • Temporal horizon

  • Multi-period approach

  • Data required

  • Impact on P&L, RWA and Capital

  • Macroeconomic Stress Scenarios in consumption

    • Expert

    • Statistical

    • regulatory

  • PD Stress Testing:

    • 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

  • LGD Stress Testing:

    • LGD Downturn: Mixed Distribution Approach

    • PD/LGD Multiyear Approach modeling

    • LGD stress test 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 52: Stress Testing PD in Excel and SAS multifactor model Credit Portfolio Views

  • Exercise 53: Stress Testing PD in SAS Multiyear Approach

  • Exercise 54: Stress test of PD and Autoregressive Vectors

  • Exercise 55: LGD stress test adjusted for climate change

  • Exercise 56: Stress Test of the LGD econometric model in Excel

  • Exercise 57: Joint Stress Test of the PD&LGD

Module 27: 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 58: Stress Testing PD and corporate portfolio transition matrices using transition matrix and ASRF model in SAS, R and Excel


​Module 28: ECL IFRS 9 Stress Testing

  • Stress testing IFRS 9 and COVID-19

  • 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 non-productive 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

  • Exercise 59: Stress Testing the ECL using matrices and time series R and Excel

Global Credit Risk Stress Testing


Module 29: Stress Testing in Balance Sheet and Income Statement


  • Firmwide stress testing methodology

  • Implementation of Firmwide Stress Testing

  • ECL IFRS 9 Incorporation

  • Differences vs. EBA and CCAR

  • Static vs Dynamic Balance Sheet

  • Application and Scenario Design

  • Integration of financial risks

  • available capital

  • Actions in management

  • Global Exercise 60: Stress Testing of credit risk in python, R, Excel with VBA

  • Business plan

    • Forecasting of the Balance Sheet in 3 years

    • Forecasting of the Income Statement in 3 years

  • ​Application of Scenarios and External Shocks

  • Network analysis of main variables

  • Incorporation IFRS 9 Provisions

  • Stress Testing and credit risk capital

  • Review of the Impact of credit risk in:

    • CET1 capital, regulatory capital and RWAs

    • ​Balance Sheet

    • P&L Income Statement

    • Excess limits

  • Dashboard in Excel


​Module 30: Quantum 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 61: DGSE model using deep learning

  • Exercise 62: Quantum Monte Carlo Simulation vs. Classical Monte Carlo Simulation



Module 31: Economic and Regulatory Capital Models

  • Regulatory Capital

  • Economic Capital Methodologies

  • PD Structural Models

  • Merton's model

  • Default Correlation

  • Future Asset Correlation

  • Unexpected Tax Loss

  • ASRF Economic Capital Models

  • Business Models

    • KMW

    • Creditmetrics

    • Credit Portfolio View

    • Credit risk +

  • Dependency modeling using copulas

  • Economic capital management

  • Economic Capital Model in credit cards

  • Advanced Model of Economic Capital in mortgages

  • Economic Capital Model in SMEs and Corporate Companies

  • Allocating Economic Capital

  • Exercise 63: Asset mapping

  • Exercise 64: Portfolio Approach: Estimation of EL, UL, ULC, Correlation and Economic Capital in Excel

  • Exercise 65: Creditrisk + on SAS

  • Exercise 66: Creditmetrics in Excel and R

  • Exercise 67: Single-factor model in Excel

Module 32: Concentration Risk

  • Climate change adjustments

  • Individual Concentration Model

  • Sector Concentration Model

  • Pykthins model

  • Cespedes et al model

  • Exercise 68: Measurement of concentration risk



​Module 33: Quantum Economic Capital


  • Distribution of credit risk losses

  • Quantum uncertainty model

  • Circuit Definition

  • Quadratic acceleration over the classical Monte Carlo simulation

  • expected loss

  • Cumulative distribution function

  • VaR

  • Expected Shortfall

  • Exercise 69: Quantum economic capital estimation

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