The course "Synthetic Data Strategies for Managing Low Default Portfolios" is specifically designed to assist financial institutions in managing credit portfolios, particularly those with low default rates. The course focuses on the use of synthetic data created through advanced algorithms of Gen AI and Quantum Computing. This synthetic data helps in enhancing the robustness and efficiency of credit risk models under such conditions. The course aims to achievebjectives and benefits: the following objectives and benefits:
Objectives:

Understanding Synthetic Data: Introduce participants to the concept of synthetic data, including its generation, characteristics, and applications in credit risk management.

Credit Scoring and Validation Models Enhancement: Teach how synthetic data can be utilized to improve credit scoring algorithms and validation models, especially in scenarios where real data is sparse or privacy concerns limit data availability.

Managing Low Default Portfolios: Focus on strategies for effectively managing low default portfolios, where traditional modeling techniques may struggle due to limited default instances.

Predictive Modeling for PD (Probability of Default) and LGD (Loss Given Default): Explore advanced modeling techniques using synthetic data to accurately estimate PD and LGD, critical components in credit risk measurement.

Stress Testing: Provide insights on using synthetic data for stress testing credit portfolios, ensuring that institutions remain resilient under various economic scenarios.

Regulatory Compliance and Ethical Considerations: Address regulatory compliance issues related to the use of synthetic data, including ethical considerations and ensuring models built with synthetic data meet industry standards.

HandsOn Experience: Offer practical experience through projects and case studies, enabling participants to apply synthetic data strategies in realworld scenarios.
Benefits:

Enhanced Model Performance: Synthetic data allows for the expansion of training datasets, leading to more robust and accurate credit risk models, especially beneficial for low default portfolios.

Reduced Model Bias: By generating diverse data scenarios, synthetic data helps in reducing model bias, ensuring more equitable credit scoring and lending practices.

Improved Stress Testing: Synthetic data enables the simulation of extreme but plausible scenarios for stress testing, beyond what historical data can provide, ensuring better preparedness for financial downturns.

Regulatory Compliance: The course equips participants with knowledge on how to use synthetic data while adhering to regulatory standards, reducing the risk of noncompliance penalties.

Innovation and Competitive Advantage: Understanding and utilizing synthetic data can place financial institutions at the forefront of innovation, providing a competitive edge in risk management practices.

Cost Efficiency: Synthetic data generation can be more costeffective compared to the acquisition of realworld data, especially in sensitive or highly regulated domains.

Privacy Preservation: Utilizing synthetic data mitigates privacy concerns, as it does not directly correspond to real individuals, thus safeguarding customer information.
By the end of this course, participants will be wellequipped with the knowledge and skills to implement synthetic data strategies in their credit risk management practices, leading to more effective management of low default portfolios and overall enhanced financial stability.
WHO SHOULD ATTEND?
The course is designed for financial professionals who want to enhance their skills in developing effective credit scoring models and finetuning their results. It is also suitable for credit risk and data science department managers responsible for managing these models. To better comprehend the course topics, participants are required to have a solid foundation in mathematics and statistics.
Schedules:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Price: 7 900 €
Early Bird Price: 6 900 €
Ending 7 May
Level: Advanced
Duration: 39 h
Material:

Presentations PDF

Exercises in Excel, R, Python, Jupyterlab y Tensorflow
AGENDA
Synthetic Data Strategies for Managing
Low Default Portfolios
Module 1: Synthetic data


Synthetic text

Synthetic media like video, image, or sound

Synthetic tabular data


Categories based on the amount of synthetic data

Fully synthetic data

Partially synthetic data

Hybrid synthetic data


Synthetic data generation
 Generative Adversarial Network

(GAN) models

Copulas (Statistics based)

Transformerbased models

Generative adversarial network (GAN) models

How to determine synthetic data quality?

Fidelity

Privacy

Utility

DEEP LEARNING
Module 2: 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 1: Credit Scoring using Deep Learning Feed Forward
Module 3: 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

Exercise 2: Credit Scoring using Deep Learning LSTM
Module 4: 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 3: Credit scoring using deep learning CNN
Module 5: Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

Fundamental components of the GANs

GAN architectures

GAN Implementation

Training generative models

Deep Convolutional GANs (DCGANs)

Conditional GANs (cGANs)

Cycle Generative Adversarial Networks (CycleGAN)

Least Squares Generative Adversarial Network (LSGAN)

Wasserstein Generative Adversarial Networks (WGAN)

Stacked GenerativeAdversarial Networks (StackGAN)

Credit Scoring using GANs

Exercise 4: Creating synthethic data for Credit Scoring using GANs
Module 6: Tuning Deep Learning for GANs

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

Exercise 5: Optimization Credit Scoring Xboosting, Random forest and SVM

Exercise 6: Optimized Credit Scoring Deep Learning
PROBABILISTIC MACHINE LEARNING
Module 7: 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 7: Gaussian process for regression

Exercise 8: Credit scoring model using Bayesian Neural Networks
Module 8: Variational Autoencoder (VAE)

Evaluating Generative Networks

Evidence lower bound (ELBO)

Reparameterization

Variations

Architecture and operation

Variational Autoencoders

Conditional VAE

βVAE


Importance Weighted Autoencoder

VAE Issues

Inexpressive Posterior

The Posterior Collapse

Latent Distributions

Exercise 9: Variational Autoencoder
Generative Artificial Intelligence
Module 9: Generative AI

Introducing generative AI

What is Generative AI?

Generative AI Models

Generative Pre trained Transformer (GPT)

Llama 2

PaLM2

DALLE


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 10: Embeddings for words, sentences, question answers

Exercise 11: Embedding Visualization

Exercise 12: First let's prepare the data for visualization

Exercise 14: PCA (Principal Component Analysis)

Exercise 15: Embeddings on Large Dataset

Exercise 16: Prompt engineering

Exercise 17: Advanced Prompting Techniques

Exercise 18: Large Language Models (LLMs)

Exercise 19: Retrieval Augmented Generation

Exercise 20: Traditional KMeans to LLM powered KMeans

Exercise 21: Cluster Visualization

Exercise 22: Semantic Search

Exercise 23: Tokens and Words

Exercise 24: Tokenization in Programming Languages
MODEL VALIDATION
Module 10: Validation for credit scoring

Model validation

Validation of machine learning models

Regulatory validation of machine learning models in Europe

Out of Sample and Out of time validation

Checking pvalues 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

KFold Cross Validation

Exercise 25: Credit scoring model using logistic regression

Exercise 26: Gini Estimation, Information Value, Brier Score, Lift Curve, CAP, ROC, Divergence in Excel

Exercise 27: Jackkinifng in SAS

Exercise 28: Bootstrapping in R

Exercise 29: KFold Cross Validation in R
Module 11: Stability tests

Model stability index

Factor stability index

Xisquare test

KS test

Exercise 30: Stability tests of models and factors
SYNTHETIC DATA in Credit Scoring
Module 12: Synthetic data for Credit Scoring

Synthetic Minority Oversampling Technique SMOTE

Generative Adversarial Network (GAN) can be used to generate synthetic data for credit scoring purposes

Synthetic data can be used to develop and refine models

Benefits

Privacy preservation

Enhanced modelling

Innovation without constraints

Insightful market research

Regulatory Compliance


Using Generative Adversarial Networks (GANs) vs SMOTE

Exercise 31: Credit Scoring using SMOTE for balancing data

Exercise 32: Credit Scoring using Autoencoders VAE for generating synthetic data

Exercise 33: Credit Scoring using GAN for generating synthetic data
Explainable Artificial Intelligence
Module 14: 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 34: XAI interpretability of credit scoring
QUANTUM COMPUTING
Module 15: 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 quantumclassical algorithms

Quantum annealing, simulation and optimization of algorithms

Quantum machine learning algorithms

Exercise 35: Quantum operations multiexercises
Module 16: 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

DWave implementation

Qiskit Implementation

Exercise 36: Quantum Circuits, Grover Algorithm Simulation, Fourier Transform and Shor
Module 17: 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 37: Quantum Support Vector Machine
Module 18: Synthetic Data Generation using Copulas

Introduction to Copulas

Definition and motivation

Properties of copulas

Applications in synthetic data generation


Types of Copulas

Gaussian copulas

Archimedean copulas (e.g., Clayton, Gumbel, Frank)

Elliptical copulas

Tcopulas and other extensions


Modeling Dependencies with Copulas

Bivariate copula models

Multivariate copula models

Tail dependence and extremal dependence


Parameter Estimation

Method of moments

Maximum likelihood estimation

Goodnessoffit tests


Exercise 38: Generating Synthetic Data with Copulas

Exercise 39: Pairwise copula construction

Exercise 40: Multivariate copulabased data generation
Probability of Default for Low Default Portfolio
Module 19: 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 41: Logistic Model Bayesian PD in Python

Exercise 42: PD using MCMC in R

Exercise 43: PD using Process Gaussian Regression
Module 20: 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

Exercise 44: PD LDP confidence interval approach in R

Exercise 45: Multiperiod confidence interval approach PD LDP

Exercise 46: Neutral Bayesian PD in R

Exercise 47: Conservative Bayesian PD in R
Module 21: 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 48: PD calibration using Platt scaling and isotonic regression for traditional and quantum machine learning models

Exercise 49: PD calibration using Gaussian Process Regression
Module 22: 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 50: Bayesian PD of logistic model in Python

Exercise 51: PD using MCMC in R

Exercise 52: PD using Process Gaussian Regression
Structural and Reduced form Credit Models
Module 23: Structural Models of PD

Merton's model

Physical Probability of Default

BlackScholesMerton model

Black–Cox model

Vasicek–Kealhofer model

CDS Pricing

Curves in liquidity and nonliquidity conditions

CDS Implied EDF

CDS Spreads

Fair Value Spread

CDS Spread in Sovereigns

DD Default Distance

Impact of climate change

Coal Price Sensitivity

Exercise 53: Estimation of CDS Spread and PD

Exercise 54: Estimate of EDF and DD adjusted for climate change
Module 24: Climate Risk in Credit Risk

Credit Risk Transition risk

Scenario analysis to assess the transition risk component of a portfolio's credit risk

Introduction: preparing banks for the low carbon transition

A growing need for climate scenario analysis

The challenge for banks

Take advantage of and integrate the resources available to banks


An integrated approach to transition risk assessment Transition scenarios

Understand transition scenarios and their sources

Using scenarios for transition risk assessment

Closing the gap between climate scenarios and financial risk assessment


Borrower Level Calibration

Portfolio Impact Assessment

Link expected loss to transition impacts on portfolios

Assessment of probability of default (PD)

Loss Given Default (LGD) Assessment


Putting the Approach to Work: Lessons Learned from Banking Pilots

Piloting the transition risk methodology

Definition of sectors and segments

Evaluate the relative sensitivities of the segments

Determination of calibration points at the borrower level Case studies and results

The pilot transition scenario

pilot results


Transition Opportunities: Exploring an Institutional Strategy

evaluating the market

Grounding Opportunity Assessments in Scenario Analysis

Assessing the market attractiveness of the segment

Identification of banking capabilities

Discovering the opportunities with the greatest potential


Future Directions: Developing the Next Generation of Transition Risk Analysis


Physical risks and opportunities

An Integrated Approach to Physical Risk Assessment

Borrower Characteristics

Insurance as a risk mitigator due to extreme climatic and meteorological events

climate change scenarios


Impacts of climate change on the probability of default PD

Evaluation of changes in the productivity of the sector

Adjustment of income statement metrics

Determination of changes in the probability of default


Real Estate: Climate Change Impacts on LTV LoantoValue

Estimation of the impacts of extreme events on the value of properties.

Determining Changes in LTV LoantoValue Ratio


Physical Opportunities: Exploring an Institutional Strategy

Taxonomy of opportunities and data sources

evaluating the market

Evaluation of the financing demand of the sector

Sector evaluation

Assess the institutional capacity and market positioning of a bank

Evaluate opportunities


Future Directions: Towards the Next Generation of Physical Risk and Opportunity Analysis

Develop internal analytics and capabilities within banks

Strengthening the research base

Develop analytical platforms and tools to support physical assessments of risks and opportunities.

Improve information flows on physical risk and adaptation between banks and borrowers

Improve dialogue with governments and insurers



Exercise 55: Estimating PD and DD adjusted for climate change in transition risk

Exercise 56: Estimating PD and DD adjusted for climate change in physical risk

Exercise 57: Estimating LGD adjusted for climate change in transitional risk with carbon prices
Module 25: LGD in LDP portfolios

Treatment of LGD in Low Default portfolio (LDP) portfolios

Problems in (LDP) portfolios

Market LGD Approach

Expert decision trees for modeling recovery

Linear and options approach:

Definition: LGD, RR and CRR

Treatment of collaterals

Linear approach to estimate LGD

BlackSholes Options Approach to estimate LGD

Implied Market LGD Approach

Defaultable Bond

Implied LGD on CDS Spread

PDLGD Models

The structural Merton LGD

Vasicek LGD

The FryeJacobs LGD


Exercise 58: Calibration and Optimization of Implied LGD in Solver and VBA

Exercise 59: The structural Merton LGD model
BACKTESTING VALIDATION
Module 26: PD Backtesting

PD validation

Backtesting PD

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 60: Backtesting PD in Excel
Module 27: 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

Ttest

Wilcoxon signed rank test

Accuracy Test

F Test

Ansari–Bradley Test

Exercise 61: Comparison of the performance of the models using Calibration and precision tests.
Module 28: Synthetic data generation in PD and LGD

Synthetic Data Generation: Concepts and Methods

Definition and objectives of synthetic data generation

Traditional approaches (e.g., bootstrapping, Monte Carlo simulation)

Machine learningbased approaches (e.g., generative adversarial networks, variational autoencoders)


Preserving Privacy in Synthetic Data Generation

Privacy concerns in credit risk data

Differential privacy and its applications

Synthetic data generation techniques for privacy preservation in PD and LGD modeling


Evaluation Metrics for Synthetic Data

Metrics for assessing data utility in PD and LGD modeling

Privacy metrics (e.g., εdifferential privacy)

Validation techniques for synthetic PD and LGD data


Traditional Approaches to Synthetic Data Generation

Bootstrapping methods for PD and LGD data

Monte Carlo simulation techniques

Limitations and considerations in traditional approaches


Machine LearningBased Synthetic Data Generation

Generative models for PD and LGD data generation

Generative adversarial networks (GANs) for synthetic data generation

Variational autoencoders (VAEs) for synthetic data generation in credit risk analysis


Applications of Synthetic Data in PD and LGD Modeling

Synthetic data augmentation for model training

Model validation and stress testing using synthetic PD and LGD data

Case studies and realworld applications in credit risk assessment


Challenges and Future Directions

Ethical considerations in synthetic data generation for credit risk modeling

Emerging techniques and research directions in PD and LGD modeling

Industry trends and best practices for synthetic data usage in credit risk analysis


Exercise 62: Synthetic data generation for estimating and validating PD

Exercise 63: Synthetic data generation for estimating and validating LGD
AI STRESS TESTING
Module 29: 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 64: Bellman equation macroeconomic model using neural networks
Module 30: 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 Covid19

Exercise 65: Chargeoff model with VAR and VEC

Exercise 66: Forecasting financial series and Bayesian LSTM indices in Python

Exercise 67: Pandemic Forecasting using Multivariate RNN LSTM in Python

Exercise 68: Forecasting using Quantum neural networks
Module 31: Stress Testing PD and LGD

Temporal horizon

Multiperiod 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 69: Stress Testing PD in Excel and SAS multifactorial model Credit Portfolio Views

Exercise 70: Stress Testing PD using Bayesian LSTM

Exercise 71: PD stress test using Variational Quantum Regression

Exercise 72: LGD Stress Test using MARS Model

Exercise 73: LGD Stress Test using LASSO regression
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 74: Stress Testing PD and corporate portfolio transition matrices using transition matrix and ASRF model in SAS, R and Excel
Module 33: 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 75: DGSE model using deep learning

Exercise 76: Quantum Monte Carlo Simulation vs. Classical Monte Carlo Simulation
Module 34: Synthetic Data Generation for Stress Testing

Introduction to Stress Testing and Scenario Analysis

Overview of stress testing in credit risk management

Objectives and methodologies of scenario analysis

Importance of stress testing for regulatory compliance and risk management


Synthetic Data Generation: Concepts and Methods

Definition and objectives of synthetic data generation

Traditional approaches (e.g., bootstrapping, Monte Carlo simulation)

Machine learningbased approaches for scenario generation


Designing Stress Scenarios and Extreme Events

Identification of key risk factors and drivers

Definition of stress scenarios and severity levels

Incorporating tail events and extreme shocks in scenario design


Synthetic Data Generation for Stress Testing

Generating synthetic stress scenarios using historical data

Calibration techniques for scenario parameters

Validation and sensitivity analysis of synthetic stress scenarios


Machine LearningBased Scenario Generation

Generative models for scenario generation

Use of neural networks and deep learning for scenario design

Challenges and considerations in machine learningbased approaches


Portfolio Stress Testing and Resilience Assessment

Application of synthetic stress scenarios to credit portfolios

Stress testing methodologies (e.g., Monte Carlo simulation, scenario analysis)

Evaluation of portfolio resilience and risk measures under stress


Interpreting Stress Test Results and Risk Management Implications

Analysis of stress test outcomes and scenario impact

Identification of vulnerabilities and risk concentrations

Risk management strategies and actions based on stress test results


Challenges and Best Practices in Synthetic Data Generation for Stress Testing

Ethical considerations and data privacy issues

Limitations and pitfalls of synthetic stress scenarios

Industry trends and emerging technologies in stress testing and scenario analysis


Exercise 77: Stress testing methodologies (e.g., Monte Carlo simulation, scenario analysis)

Exercise 78: Machine LearningBased Scenario Generation
PORTFOLIO CREDIT RISK
Module 35: Economic Capital Models

Regulatory Capital

Economic Capital Methodologies

Correlation of Assets and Default

Unexpected Tax Loss

ASRF Economic Capital Models

Business Models

KVM

Creditmetrics

Credit Portfolio View

Credit risk +


Economic capital management

Allocating Economic Capital

Exercise 79: Portfolio Approach: Estimation of EL, UL, ULC, Correlation and Economic Capital in Excel

Exercise 80: Creditrisk + in R

Exercise 81: Creditmetrics in Excel and R

Exercise 82: Singlefactor model in Excel
Module 36: Synthetic Data Generation for
Portfolio Management

Synthetic Data for Portfolio Management

Diversification and risk assessment

Portfolio optimization using synthetic data

Case studies


Analyzing the Effectiveness of Synthetic Data

Metrics for comparison

Realworld vs. synthetic data in financial analysis


Ethical Considerations and Privacy

Ethical implications of synthetic data

Privacy laws and regulations

Balancing innovation with ethical responsibility


Exercise 83: Synthetic data for Portfolio credit risk
QUANTUM ECONOMIC CAPITAL
Module 37: 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 84: Estimation EL, VAR, ES of quantum credit risk