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Quantum Computing for Credit Risk

Credit risk modeling course 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.

COURSE OBJECTIVE

Among other topics, quantum computing, quantum circuits, important quantum algorithms, quantum mechanics, quantum error and correction, and quantum machine learning are exposed.

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.

WHO SHOULD ATTEND?

The Course is aimed at professionals from financial institutions interested in developing powerful credit scoring models and calibrating their output, as well as model managers in credit risk and data science departments.

For a better understanding of the topics it is necessary that the participant has knowledge of statistics and mathematics.

Friday, December 1, 2023
Monday, April 8, 2024
Europe: Mon-Fri, CEST 16-19 h
America: Mon-Fri, CDT 18-21 h
Asia: Mon-Fri, IST 18-21 h
Price: 9.000 €
Duration: 50 h
Presentations PDF

Exercises in Excel, R, Python, Jupyterlab y Tensorflow
yulia.tsedryk@fermacrisk.com
Banks
Quntum Risk PDF

Agenda

Module 1: Machine Learning

Definition of Machine Learning

Machine Learning Methodology

Data Storage

Abstraction

Generalization

Assessment

Supervised Learning

Unsupervised Learning

Reinforcement Learning

deep learning

Typology of Machine Learning algorithms

Steps to Implement an Algorithm

information collection

Exploratory Analysis

Model Training

Model Evaluation

Model improvements

Machine Learning in consumer credit risk

Machine Learning in credit scoring models

Quantum Machine Learning

Module 2: EDA Exploratory Analysis

Data typology

transactional data

Unstructured data embedded in text documents

Social Media Data

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

Exercise 1: EDA Exploratory Analysis

Module 3: Univariate Analysis

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 2: Detection and treatment of Advanced Outliers

Exercise 3: Stratified and Random Sampling in R

Exercise 4: Multivariate imputation model

Exercise 5: Univariate analysis in percentiles in R

Exercise 6: Continuous variable optimal univariate analysis in Excel

Exercise 7: Estimation of the KS, Gini and IV of each variable in Excel

Exercise 8: Word Cloud analysis of variables in R

Unsupervised Learning

Module 4: Unsupervised models

Hierarchical Clusters

K Means

standard algorithm

Euclidean distance

Principal Component Analysis (PCA)

Advanced PCA Visualization

Eigenvectors and Eigenvalues

Exercise 9: Segmentation of the data with K-Means R

Supervised Learning

Module 5: 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 10: Credit Scoring Lasso Logistic Regression in R

Exercise 11: Credit Scoring Ridge Regression in R

Module 6: 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 12: Credit Scoring KNN and PCA

Module 7: Support Vector Machine SVM

Support Vector Classification

Support Vector Regression

optimal hyperplane

Support Vectors

add costs

Advantages and disadvantages

SVM visualization

Tuning SVM

kernel trick

Exercise 14: Credit Scoring Support Vector Machine in R

Module 8: Ensemble Learning

Classification and regression ensemble models

bagging

bagging trees

Random Forest

Boosting

adaboost

Gradient Boosting Trees

xgboost

Advantages and disadvantages

Exercise 15: Credit Scoring Boosting in R

Exercise 16: Credit Scoring Bagging in R

Exercise 17: Credit Scoring Random Forest, R and Python

Exercise 18: Credit Scoring Gradient Boosting Trees

MACHINE LEARNING
Module 9: Introduction to Deep Learning

Definition and concept of deep learning

Why now the use of deep learning?

Neural network architectures

feedforward network

R deep learning

Python deep learning

Convolutional Neural Networks

Use of deep learning in image classification

cost function

Gradient descending optimization

Use of deep learning

How many hidden layers?

How many neurons, 100, 1000?

How many times and size of the batch size?

What is the best activation function?

Deep Learning Software: Caffe, H20, Keras, Microsoft, Matlab, etc.

Deployment software: Nvidia and Cuda

Hardware, CPU, GPU and cloud environments

Advantages and disadvantages of deep learning

Module 10: 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 19: Credit Scoring using Deep Learning Feed Forward

Module 11: 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 20: Credit scoring using deep learning CNN

Module 12: 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 21: Credit Scoring using Deep Learning LSTM

Module 14: 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 22: Credit Scoring using GANs

Module 15: Calibration of 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

Exercise 23: Optimization Credit Scoring Xboosting, Random forest and SVM

Exercise 24: Optimized Credit Scoring Deep Learning

DEEP LEARNING
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 25: Gaussian process for regression

Exercise 26: Credit scoring model using Bayesian Neural Networks

PROBABILISTIC MACHINE LEARNING
Module 17: Validation of traditional and Machine Learning models

Model validation

Validation of machine learning models

Regulatory validation of machine learning models in Europe

Out of Sample and Out of time validation

Checking p-values in regressions

R squared, MSE, MAD

Waste diagnosis

Goodness of Fit Test

multicollinearity

Binary case confusion matrix

Multinomial case confusion matrix

Main discriminant power tests

confidence intervals

Jackknifing with discriminant power test

Bootstrapping with discriminant power test

Kappa statistic

K-Fold Cross Validation

Exercise 27: Credit scoring model using logistic regression

Exercise 28: Gini Estimation, Information Value, Brier Score, Lift Curve, CAP, ROC, Divergence in Excel

Exercise 29: Jackkinifng in SAS

Exercise 30: Bootstrapping in R

Exercise 31: K-Fold Cross Validation in R

Module 18: Stability tests

Model stability index

Factor stability index

Xi-square test

K-S test

Exercise 32: Stability tests of models and factors

MODEL VALIDATION
Module 19: Automation of Credit Scoring and PD Modeling

What is modeling automation?

that is automated

Automation of machine learning processes

Optimizers and Evaluators

Modeling Automation Workflow Components

Summary

Indicted

Feature engineering

Model generation

Assessment

Hyperparameter optimization

Reconstruction or recalibration of credit scoring

Credit Scoring Modeling

Main milestones

Evaluation and optimization

Possible Issues

PD calibration modeling

Evaluation and optimization

backtesting

Discriminating Power

Stability Tests

Global evaluation of modeling automation

Implementation of modeling automation in banking

Technological requirements

available tools

Benefits and possible ROI estimation

Main Issues

Model Risk

Genetic algorithms

Exercise 33: Automation of the modeling, optimization and validation of credit scoring hyperparametry

Exercise 34: Automation of PD modeling and validation

Explainable Artificial Intelligence

Module 20: Explainable Artificial Intelligence XAI

interpretability problem

model risk

Regulation of the General Data Protection Regulation GDPR

EBA discussion paper on machine learning for IRB models

1. The challenge of interpreting the results,

2. The challenge of ensuring that management functions adequately understand the models, and

3. The challenge of justifying the results to supervisors

Black Box Models vs. Transparent and Interpretable Algorithms

interpretability tools

Shap, Shapley Additive explanations

Global Explanations

Dependency Plot

Decision Plot

Local Explanations Waterfall Plot

Lime, agnostic explanations of the local interpretable model

Explainer Dashboard

Other advanced tools

Exercise 35: XAI interpretability of credit scoring

Explainable Artificial Intelligence

Module 20: Explainable Artificial Intelligence XAI

interpretability problem

model risk

Regulation of the General Data Protection Regulation GDPR

EBA discussion paper on machine learning for IRB models

1. The challenge of interpreting the results,

2. The challenge of ensuring that management functions adequately understand the models, and

3. The challenge of justifying the results to supervisors

Black Box Models vs. Transparent and Interpretable Algorithms

interpretability tools

Shap, Shapley Additive explanations

Global Explanations

Dependency Plot

Decision Plot

Local Explanations Waterfall Plot

Lime, agnostic explanations of the local interpretable model

Explainer Dashboard

Other advanced tools

Exercise 35: XAI interpretability of credit scoring

AUTOMACHINE LEARNING and XAI
Module 21: Quantum computing and algorithms

Objective: Quantum computing applies quantum mechanical phenomena. On a small scale, physical matter exhibits properties of both particles and waves, and quantum computing takes advantage of this behavior using specialized hardware. The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basic" states, meaning that it is in both states simultaneously.

Future of quantum computing in insurance

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 quantum-classical algorithms

Quantum annealing, simulation and optimization of algorithms

Quantum machine learning algorithms

Exercise 36: Quantum operations multi-exercises

Module 22: Introduction to quantum mechanics

Quantum mechanical theory

wave function

Schrodinger's equation

statistical interpretation

Probability

Standardization

Impulse

The uncertainty principle

Mathematical Tools of Quantum Mechanics

Hilbert space and wave functions

The linear vector space

Hilbert's space

Dimension and bases of a Vector Space

Integrable square functions: wave functions

Dirac notation

operators

General definitions

hermitian adjunct

projection operators

commutator algebra

Uncertainty relationship between two operators

Operator Functions

Inverse and Unitary Operators

Eigenvalues and Eigenvectors of an operator

Infinitesimal and finite unit transformations

Matrices and Wave Mechanics

matrix mechanics

Wave Mechanics

Exercise 37: Quantum mechanics multi-exercises

Module 23: Introduction to quantum error correction

Error correction

From reversible classical error correction to simple quantum error correction

The quantum error correction criterion

The distance of a quantum error correction code

Content of the quantum error correction criterion and the quantum Hamming bound criterion

Digitization of quantum noise

Classic linear codes

Calderbank, Shor and Steane codes

Stabilizer Quantum Error Correction Codes

Exercise 38: Noise Model, Repetition Code and quantum circuit

Module 24: Quantum Computing II

Quantum programming

Solution Providers

IBM Quantum Qiskit

Amazon Braket

PennyLane

cirq

Quantum Development Kit (QDK)

Quantum clouds

Microsoft Quantum

Qiskit

Main Algorithms

Grover's algorithm

Deutsch–Jozsa algorithm

Fourier transform algorithm

Shor's algorithm

Quantum annealers

D-Wave implementation

Qiskit Implementation

Exercise 39: Quantum Circuits, Grover Algorithm Simulation, Fourier Transform and Shor

Module 25: Quantum Machine Learning

Quantum Machine Learning

hybrid models

Quantum Principal Component Analysis

Q means vs. K means

Variational Quantum Classifiers

Variational quantum classifiers

Quantum Neural Network

Quantum Convolutional Neural Network

Quantum Long Short Memory LSTM

Quantum Support Vector Machine (QSVC)

Exercise 40: Quantum Support Vector Machine

Module 26: 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 41: Neural Network using tensor networks

Module 23: Introduction to quantum error correction

Error correction

From reversible classical error correction to simple quantum error correction

The quantum error correction criterion

The distance of a quantum error correction code

Content of the quantum error correction criterion and the quantum Hamming bound criterion

Digitization of quantum noise

Classic linear codes

Calderbank, Shor and Steane codes

Stabilizer Quantum Error Correction Codes

Exercise 38: Noise Model, Repetition Code and quantum circuit

Module 24: Quantum Computing II

Quantum programming

Solution Providers

IBM Quantum Qiskit

Amazon Braket

PennyLane

cirq

Quantum Development Kit (QDK)

Quantum clouds

Microsoft Quantum

Qiskit

Main Algorithms

Grover's algorithm

Deutsch–Jozsa algorithm

Fourier transform algorithm

Shor's algorithm

Quantum annealers

D-Wave implementation

Qiskit Implementation

Exercise 39: Quantum Circuits, Grover Algorithm Simulation, Fourier Transform and Shor

Module 25: Quantum Machine Learning

Quantum Machine Learning

hybrid models

Quantum Principal Component Analysis

Q means vs. K means

Variational Quantum Classifiers

Variational quantum classifiers

Quantum Neural Network

Quantum Convolutional Neural Network

Quantum Long Short Memory LSTM

Quantum Support Vector Machine (QSVC)

Exercise 40: Quantum Support Vector Machine

Module 26: 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 41: Neural Network using tensor networks

QUANTUM COMPUTING
Module 27: Quantum Credit Scoring

Scoring assignment

Scorecard Classification

Scorecard WOE

Binary Scorecard

Continuous Scorecard

Scorecard Rescaling

Factor and Offset Analysis

Scorecard WOE

Binary Scorecard

What is quantum machine learning?

Qubit and Quantum States

Quantum Automatic Machine Algorithms

quantum circuits

quantum k means

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 42: Quantum K-means

Exercise 43: Quantum Support Vector Machine to develop credit scoring model

Exercise 44: Quantum feed forward Neural Networks to develop a credit scoring model

Exercise 45: Quantum Convoluted Neural Networks to develop a credit scoring model

PD Quantum and Machine Learning Models

Module 28: Quantum and Machine Learning Models of PD

PD estimation

Treatment of Panel data

PD Machine Learning Models

PD COX regression of survival

Cox XGBoost,

Survival Tree

Random Survival Forest

PD Bayesian Logistic Regression

PS Regression Lasso

PS SVM

PS Neural Networks

PS Quantum Neural Networks

PD Calibration

Calibration of econometric models

Anchor Point Estimate

PD calibration by vintages or vintages

vintage analysis

PS Marginal

PS Forward

Cumulative PD

Exercise 46: PD Calibration with COX XGBOOST Regression

Exercise 47: Calibration of the PD with Quantum SVM

Exercise 48: Calibration of PD with Neural Networks in R

Exercise 49: PD Calibration with Bayesian Logistic Regression in Python

Exercise 50: Calibration of the LASSO regression PD

Exercise 51: PD calibration with quantum neural networks in Python

Module 29: 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 52: PD calibration using Platt scaling and isotonic regression for traditional and quantum machine learning models

Exercise 53: PD calibration using Gaussian Process Regression

Module 30: 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 39: Bayesian PD of logistic model in Python

Exercise 54: PD using MCMC in R

Exercise 55: PD using Process Gaussian Regression

Module 31: IFRS 9 PD Forecasting

IFRS 9 requirements

Probability Weighted Outcome

Forward Looking

Lifetime PD modeling

PD Forecasting Modeling

PD Point in Time Forecasting

PS TTC Forecasting

Markov models

PIT PD Forecasting Models

ARIMA

VAR

VARMAX

ASRF

Traditional LSTM

Bayesian LSTM

Quantum LSTM

Exercise 56: Forecasting PD using VARMAX in R

Exercise 57: PD forecasting using quantum LSTM

Module 32: Lifetime PD

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

COVID-19 Pandemic Application

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

Multi-State Markov Model

Advantages and disadvantages

Machine Learning Model

Support Vector Machine

Deep Learning Models

Neural network architecture

PD Lifetime Extrapolation Models

Lifetime PD Calibration

Exercise 58: PD Lifetime using multinomial regression in R

Exercise 59: PD Lifetime using Multi State Markov model

Exercise 60: PD Lifetime using matrix ASRF model

Exercise 61: PD Lifetime using Quantum SVM

Exercise 62: PD Lifetime using traditional SVM in Python

Exercise 63: PD Lifetime using traditional Deep Learning

Exercise 64: PD Lifetime using Quantum Deep Learning

Module 33: LGD for IFRS 9

Comparison of IRB LGD vs. IFRS 9

Impact on COVID-19

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 COVID-19

LGD PIT modeling

Collateral Modeling

LGD IFRS 9 for portfolio companies

LGD IFRS 9 for mortgage portfolio

LGD IFRS 9 for corporate portfolios

IFRS 9 LGD using LASSO Regression

Machine Learning Models

Support Vector Machine

Random Forest

xgboost

Neural Networks

Exercise 65: Estimation and adjustments for LGD IFRS 9 using Random Forest regression

Exercise 66: Beta Regression and Neural Networks

Exercise 67: Estimating IFRS 9 LGD using Xgboost regression in Python

Exercise 68: LGD estimation using traditional SVM

Exercise 69: LGD estimation using quantum SVM

Exercise 70: LGD estimation using traditional neural networks

Exercise 71: LGD estimation using Bayesian neural networks

Module 30: 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 39: Bayesian PD of logistic model in Python

Exercise 54: PD using MCMC in R

Exercise 55: PD using Process Gaussian Regression

Module 31: IFRS 9 PD Forecasting

IFRS 9 requirements

Probability Weighted Outcome

Forward Looking

Lifetime PD modeling

PD Forecasting Modeling

PD Point in Time Forecasting

PS TTC Forecasting

Markov models

PIT PD Forecasting Models

ARIMA

VAR

VARMAX

ASRF

Traditional LSTM

Bayesian LSTM

Quantum LSTM

Exercise 56: Forecasting PD using VARMAX in R

Exercise 57: PD forecasting using quantum LSTM

Module 32: Lifetime PD

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

COVID-19 Pandemic Application

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

Multi-State Markov Model

Advantages and disadvantages

Machine Learning Model

Support Vector Machine

Deep Learning Models

Neural network architecture

PD Lifetime Extrapolation Models

Lifetime PD Calibration

Exercise 58: PD Lifetime using multinomial regression in R

Exercise 59: PD Lifetime using Multi State Markov model

Exercise 60: PD Lifetime using matrix ASRF model

Exercise 61: PD Lifetime using Quantum SVM

Exercise 62: PD Lifetime using traditional SVM in Python

Exercise 63: PD Lifetime using traditional Deep Learning

Exercise 64: PD Lifetime using Quantum Deep Learning

Module 33: LGD for IFRS 9

Comparison of IRB LGD vs. IFRS 9

Impact on COVID-19

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 COVID-19

LGD PIT modeling

Collateral Modeling

LGD IFRS 9 for portfolio companies

LGD IFRS 9 for mortgage portfolio

LGD IFRS 9 for corporate portfolios

IFRS 9 LGD using LASSO Regression

Machine Learning Models

Support Vector Machine

Random Forest

xgboost

Neural Networks

Exercise 65: Estimation and adjustments for LGD IFRS 9 using Random Forest regression

Exercise 66: Beta Regression and Neural Networks

Exercise 67: Estimating IFRS 9 LGD using Xgboost regression in Python

Exercise 68: LGD estimation using traditional SVM

Exercise 69: LGD estimation using quantum SVM

Exercise 70: LGD estimation using traditional neural networks

Exercise 71: LGD estimation using Bayesian neural networks

CREDIT SCORING
Module 34: Advanced EAD and CCF modeling for IRB

Impact of COVID-19 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: CCF neural network regression model

Exercise 73: CCF Support Vector Regression Model in Python

Exercise 74: Neural Networks and CCF beta regression in R

Exercise 75: CCF Quantum SVM and Classic Regression SVM

Prepaid

Module 35: Contractual Options

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 PD Lifetime

Exercise 76: Random forest regression prepayment model in R and Excel

Module 36: EAD Lifetime for Lines of Credit

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

CCF PIT Estimate

CCF Lifetime Estimate

EAD lifetime modeling

Model of the use of credit lines with macroeconomic variables

Credit card abandonment adjustment

EAD Lifetime model for pool of lines of credit

vintage model

Chain Ladder Approach

Exercise 77: Neural network model for line of credit

Exercise 78: EAD Lifetime model for line of credit

Module 36: EAD Lifetime for Lines of Credit

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

CCF PIT Estimate

CCF Lifetime Estimate

EAD lifetime modeling

Model of the use of credit lines with macroeconomic variables

Credit card abandonment adjustment

EAD Lifetime model for pool of lines of credit

vintage model

Chain Ladder Approach

Exercise 77: Neural network model for line of credit

Exercise 78: EAD Lifetime model for line of credit

EAD IFRS 9
Module 37: PD Backtesting

PD validation

Backtesting PS

PD Calibration Validation

Hosmer Lameshow test

normal test

Binomial Test

Spiegelhalter test

Redelmeier Test

Traffic Light Approach

Traffic Light Analysis and PD Dashboard

PS Stability Test

Forecasting PD vs Real PD in time

Validation with Monte Carlo simulation

Exercise 79: Backtesting PD in Excel

Module 38: LGD Backtesting

LGD Backtesting

Accuracy ratio

absolute accuracy indicator

Confidence Intervals

transition analysis

RR Analysis using Triangles

Advanced LGD Backtesting with a vintage approach

Backtesting for econometric models:

test calibration

t-test

Wilcoxon signed rank test

Accuracy Test

F Test

Ansari–Bradley Test

Exercise 80: Comparison of the performance of the models using Calibration and precision tests.

Module 39: EAD Backtesting

EAD Performance

r squared

Pearson coefficient

Spearman correlation

Validation using ROC, KS and Gini

Exercise 81: Comparison of the performance of EAD models

BACKTESTING VALIDATION
Module 40: Modernization of 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 82: Bellman equation macroeconomic model using neural networks

Module 41: 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

Quantum Deep Learning

Time series analysis with Facebook Prophet

Prediction of the spread of Covid-19

Exercise 83: Charge-off model with VAR and VEC

Exercise 84: Forecasting financial series and Bayesian LSTM indices in Python

Exercise 85: Pandemic Forecasting using Multivariate RNN LSTM in Python

Exercise 86: Forecasting using quantum neural networks

Module 42: 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:

Net Charge Off

Rating/Scoring transition matrices

Recovery Rate and LGD

Exercise 52: Stress Testing PD in Excel and SAS multifactorial model Credit Portfolio Views

Exercise 87: Stress Testing PD using Bayesian LSTM

Exercise 88: PD stress test using Variational Quantum Regression

Exercise 89: LGD Stress Test using MARS Model

Exercise 90: LGD Stress Test using LASSO regression

Module 43: 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 91: Stress Testing PD and corporate portfolio transition matrices using transition matrix and ASRF model in SAS, R and Excel

Module 44: 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 95: DGSE model using deep learning

Exercise 92: Quantum Monte Carlo Simulation vs. Classical Monte Carlo Simulation

Module 43: 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 91: Stress Testing PD and corporate portfolio transition matrices using transition matrix and ASRF model in SAS, R and Excel

Module 44: 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 95: DGSE model using deep learning

Exercise 92: Quantum Monte Carlo Simulation vs. Classical Monte Carlo Simulation

AI STRESS TESTING
Module 45: Economic Capital Models

Regulatory Capital

Economic Capital Methodologies

Correlation of Assets and Default

Unexpected Tax Loss

ASRF Economic Capital Models

Business Models

kmv

creditmetrics

Credit Portfolio View

Credit risk +

Economic capital management

Allocating Economic Capital

Exercise 93: Portfolio Approach: Estimation of EL, UL, ULC, Correlation and Economic Capital in Excel

Exercise 94: Creditrisk + in SAS

Exercise 95: Creditmetrics in Excel and R

Exercise 96: Single-factor model in Excel

CREDIT RISK OF PORTFOLIO
Module 46: 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 97: Estimation EL, VAR, ES of quantum credit risk

QUANTUM ECONOMIC CAPITAL