Some Clients

Module 16: Deep Learning Convolutional Neural Networks CNN

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• 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 39: Credit scoring using deep learning CNN

​Module 17: Deep Learning Recurrent Neural Networks RNN

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• 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 40: Credit Scoring using Deep Learning LSTM

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Module 18: Generative Adversarial Networks (GANs)

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• Generative Adversarial Networks (GANs)

• Fundamental components of the GANs

• GAN architectures

• Bidirectional GAN

• Training generative models

• Synthetic Data

• Credit Scoring using GANs

• Exercise 41: Credit Scoring using GANs

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Module 19: Calibrating Machine Learning and Deep Learning

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• 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 42: Optimization Credit Scoring Xboosting, Random forest and SVM

• Exercise 43: Optimized Credit Scoring Deep Learning

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​Module 20: Traditional Scorecard Construction

• scoring assignment

• Scorecard Classification

  • Scorecard WOE

  • Binary Scorecard

  • Continuous Scorecard

• Scorecard Rescaling

  • Factor and Offset Analysis

  • Scorecard WOE

  • Binary Scorecard

• Reject Inference Techniques

  • cut-off

  • parceling

  • Fuzzy Augmentation

  • Machine Learning

• Advanced Cut Point Techniques

  • Cut-off optimization using ROC curves

• Exercise 44: Building Scorecard in Excel, R and Python

• Exercise 45: Optimum cut-off point estimation in Excel and model risk by cut-off point selection

• Exercise 46: Confusion matrix to verify Type 1 and Type 2 Error in Excel with and without variables

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QUANTUM MACHINE LEARNING

​Module 21: Quantum Credit Scoring

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• 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 47: Quantum K-means

• Exercise 48: Quantum Support Vector Machine to develop credit scoring model

• Exercise 49: Quantum feed forward Neural Networks to develop a credit scoring model

• Exercise 50: Quantum Convoluted Neural Networks to develop a credit scoring model

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Module 22: Tensor Networks for Quantum Machine Learning

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• 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 51: Construction of credit scoring using tensor networks

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PROBABILISTIC MACHINE LEARNING

​Module 23: Probabilistic Machine Learning

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• 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 52: Gaussian process for regression

• Exercise 53: Credit scoring model using Bayesian Neural Networks

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MODEL RISK

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Module 24: Model Risk in Credit Scoring

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• Model Risk

• Model risk in deep learning

• Model risk in credit scoring

• black boxes

• cut-off decision

• absence of data

• Model Risk for not updating or recalibrating

• Ethical concepts of credit scoring

• Exercise 54: Model risk in credit scoring due to not recalibrating on time

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CREDIT SCORING MODELS

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Module 25: Credit Scoring Models by Product

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• Origination Credit Scoring

  • Credit Card Score

  • Mortgage Score

  • consumption scores

  • Car Score

• Behavior Score (BS)

  • Temporal horizon

  • Dashboard data information

  • Panel data regression

  • Cox regression

  • Behavior Score with macroeconomic variables

  • transition matrices

  • Behavior Score with transition matrices

  • Transaction Score

  • Machine Learning Models

  • BEHAVIOR SCORE ON CREDIT CARDS

• Exercise 55: Behavior Score Logistic Regression in Python data 2

• Exercise 56: Behavior Score Support Vector Machines in python

• Exercise 57: Behavior Score Random Forest in python

• ​Exercise 58: Behavior Score Gradient Boosting Trees in python

• Exercise 59: Behavior Score Deep Learning LSTM in python

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Module 26: Typology of Scoring models

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• Response Score

• Income score

• Churn Score

• Origination Fraud Score

• Behavior Fraud Score

• Collection Score

• Recovery Score

• Big Data Scoring

• Exercise 60: Fraud Score with neural networks

• Exercise 61: Income Score

• Exercise 62: Collection Score

• Exercise 63: Recovery Score

• Exercise 64: Quit Score

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CALIBRATION OF PD MODELS

Module 27: Calibration of the Probability of Default PD IRB

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• PD estimation

  • econometric models

  • Machine Learning Models

  • Data requirement

  • Risk drivers and credit scoring criteria

  • Rating philosophy

  • Pool Treatment

• PD Calibration

  • Default Definition

  • Long run average for PD

  • Technical defaults and technical default filters

  • Data requirement

  • One Year Default Rate Calculation

  • Long-Term Default Rate Calculation

• PD Model Risk

  • Conservatism Margin

• PD Calibration Techniques

  • Anchor Point Estimate

  • Mapping from Score to PD

  • ​Adjustment to the PD Economic Cycle

  • Rating Philosophy

• PD Trough The Cycle (PD TTC) models

• PD Point in Time PD (PD PIT ) models

• PD Calibration of Models Using Machine and Deep Learning

• Exercise 65: PD Calibration in Machine Learning Models

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Module 28: Machine Learning models to estimate Lifetime PD under IFRS 9

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• Credit scoring models to estimate Lifetime PD

• PD Lifetime in IFRS 9

• Impact of COVID-19 on models

• Climate Risk Impact

• Inflation impact

• Impact of rising prices

• Regression Models

  • Logistic regression

  • Logistic Multinomial Regression

  • Ordinal Probit Regression

• VAR and VEC models

• Machine Learning Model​

  • SVM: Kernel Function Definition

  • Neural Network: definition of hyperparameters and activation function

  • deep learning

  • LSTM

• PD Calibration of Models Using Machine and Deep Learning

• Exercise 66: PD Lifetime using logistic regression

• Exercise 67: PD Lifetime using multinomial regression in R

• Exercise 68: PD Lifetime using SVM in Python

• Exercise 69: PD Lifetime using Deep Learning in Python

• Exercise 70: PD Lifetime using Deep Learning LSTM in Python

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VALIDATION OF PD MODELS

​Module 29: Validation of PD models

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• Definition of PD Backtesting

• PD Calibration Validation​

  • normal test

  • Binomial Test

  • Traffic Light Approach

• Traffic Light Analysis and PD Dashboard

• PS Stability Test

• Forecasting PD vs. Real PD in time

• When should we recalibrate or reestimate a credit scoring model?

• Re-development

• Re-estimation

• Model Risk in PD

• Machine Learning to validate PD models

• Artificial Intelligence to recalibrate and rebuild models autonomously

• Exercise 71: Backtesting PD in Excel

• Exercise 72: Forecasting PD and actual PD in Excel

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AUTOMATION OF CREDIT SCORING AND PD WITH AI

Module 30: Automation of Credit Scoring and PD Modeling

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• 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 73: Automation of the modeling, optimization and validation of credit scoring hyperparametry

• Exercise 74: Automation of PD modeling and validation