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Quantum Computing for Credit Risk

 

 

 

COURSE OBJECTIVE

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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.

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

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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.

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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.

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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.

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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.

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

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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.

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

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WHO SHOULD ATTEND?

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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.

 

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Examples of Exercises

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Schedules:

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  • Europe: Mon-Fri, CEST 16-20 h

 

  • America: Mon-Fri, CDT 18-21 h

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  • Asia: Mon-Fri, IST 18-21 h

 

 

 

 

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Price: 11.000 €

 

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Level: Advanced

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Duration: 50 h

 

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     Material: 

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  • Presentations PDF

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

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Download: Course brochure in PDF

 

Some Clients

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AGENDA
 Quantum Computing for Credit Risk 

 

 

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Modular Agenda 

MACHINE LEARNING

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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

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Module 2: EDA Exploratory Analysis

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  • 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​

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Module 3: Univariate Analysis

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  • 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

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Unsupervised Learning

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Module 4: Unsupervised models

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  • 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

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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

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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

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Module 7: Support Vector Machine SVM

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  • 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

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Module 8: Ensemble Learning

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  • 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

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