top of page
Banking pic 1.jpg

Synthetic Data Strategies for managing Low Default





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:


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

  2. 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.

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

  4. 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.

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

  6. 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.

  7. Hands-On Experience: Offer practical experience through projects and case studies, enabling participants to apply synthetic data strategies in real-world scenarios.



  1. 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.

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

  3. 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.

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

  5. 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.

  6. Cost Efficiency: Synthetic data generation can be more cost-effective compared to the acquisition of real-world data, especially in sensitive or highly regulated domains.

  7. 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 well-equipped 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.


The course is designed for financial professionals who want to enhance their skills in developing effective credit scoring models and fine-tuning 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.



  • Europe: Mon-Fri, CEST 16-19 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h






Price:    7 900 €

Early Bird Price: 6 900 €


Ending 7 May


Level: Advanced


Duration: 39 h




  • Presentations PDF

  • Exercises in Excel, R, Python, Jupyterlab y Tensorflow



Synthetic Data Strategies for Managing

Low Default Portfolios



Anchor 10

Module 1: Synthetic data



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

  • One-way and two-way 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 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 5: Optimization Credit Scoring Xboosting, Random forest and SVM

  • Exercise 6: Optimized Credit Scoring Deep 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  

    •  β-VAE  

  • 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

    • DALL-E

  • 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

    • Open-Source 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



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 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 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: K-Fold Cross Validation in R

Module 11: Stability tests

  • Model stability index

  • Factor stability index

  • Xi-square test

  • K-S 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


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 quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Exercise 35: Quantum operations multi-exercises

​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

  • D-Wave 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

    • T-copulas 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

    • Goodness-of-fit tests

  • Exercise 38: Generating Synthetic Data with Copulas

  • Exercise 39: Pairwise copula construction

  • Exercise 40: Multivariate copula-based 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

    • One-period and multi-period 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

  • Black-Scholes-Merton model

  • Black–Cox model

  • Vasicek–Kealhofer model

  • CDS Pricing

  • Curves in liquidity and non-liquidity 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 Loan-to-Value

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

      • Determining Changes in LTV Loan-to-Value 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

  • Black-Sholes Options Approach to estimate LGD

  • Implied Market LGD Approach

  • Defaultable Bond

  • Implied LGD on CDS Spread

  • PD-LGD Models

    • The structural Merton LGD

    • Vasicek LGD

    • The Frye-Jacobs LGD

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

  • Exercise 59: The structural Merton LGD model


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

  • T-test

  • 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 learning-based 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., ε-differential 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 Learning-Based 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 real-world 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


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 Covid-19

  • Exercise 65: Charge-off 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

  • 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 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 learning-based 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 Learning-Based Scenario Generation

    • Generative models for scenario generation

    • Use of neural networks and deep learning for scenario design

    • Challenges and considerations in machine learning-based 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 Learning-Based Scenario Generation




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: Single-factor 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

    • Real-world 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 


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

bottom of page