CREDIT SCORING IN CONTEXT OF INTERPRETABLE MACHINE LEARNING. THEORY AND PRACTICE

ISBN: 978-83-8030-424-6

380 stron
format: B5
oprawa: twarda
Rok wydania: 2020

The monograph starts with a presentation of the historical and organizational setting of credit scoring and a critical review of data-related processes that are relevant for preparing credit scoring models. Afterward, being aware of the recent data-driven revolution, the exposition moves to the presentation of selected machine learning methods that can be used for credit scoring, with a special emphasis on variable selection methods which concern one of the key challenges of the modeling practice. The third group of topics that is covered are analytical tasks that are typically undertaken when a credit scoring model has been built, that is the model‘s performance evaluation, model monitoring and methods allowing to understand how complex machine learning models produce their forecasts. This is a crucial issue due to the fact that virtually all machine learning methods are of a black (grey) box type in their functioning and hence the results obtained, may be not comprehensible to the user. Approaches to overcome this inherent difficulty attract much interest in recent years and a new field of the so-called Explainable Artificial Intelligence (XAI) has emerged. The monograph is completed with a review of key aspects related to the deployment of credit scoring models in complex IT infrastructures, with emphasis on the most important problems related to the performance and scalability of the scoring process, as well as architectures and processes that can be used while implementing credit scoring models in the development of decision engines.
The editors and authors of the particular chapters have to be congratulated for producing a captivating read and useful volume. Their contributions present well-chosen aspects of modern approaches to credit scoring up to the highest academic standards, yet in a comprehensible and constructive fashion. They focused on the new and promising data-driven approaches, notably based on machine learning which will certainly dominate in the years to come.

SPIS TREŚCI

Foreword

Preface

1. Background of the credit scoring
Daniel Kaszyński
1.1 History of credit scoring
1.2 Classical scorecard
1.3 Data analytics revolution and its challenges
1.4 People and processes

2. Data processing for credit scoring
Maciej Kwiatkowski
2.1 Data management
2.2 Data sources
2.3 Data quality assurance
2.4 Data pre-processing
2.5 Conclusions

3. Variable selection methods
Karol Przanowski Sebastian Zając Daniel Kaszyński Łukasz Opiński
3.1 The importance of variable pre-selection
3.2 Comparison measures for the variable selection methods
3.3 Variable selection methods
3.4 Numerical experiment of variable selection
3.5 Conclusions

4. Selected machine learning methods used for credit scoring
Małgorzata Wrzosek Daniel Kaszyński Karol Przanowski Sebastian Zając
4.1 Classical credit scoring models
4.2 Machine learning for credit scoring
4.3 Frameworks for model development
4.4 Numerical results of models
4.5 Conclusions

5. Sensitivity of machine learning methods to data issues
Daniel Kaszyński Kinga Siuta Bogumił Kamiński
5.1 Outlying observations
5.2 Missing data
5.3 Selection of the target variable
5.4 Multicollinearity problem
5.5 The Simpson‘s Paradox
5.6 New categories in categorical variables
5.7 Complete separation problem
5.8 Too granular categorical data
5.9 Coding ratios
5.10 Conclusions

6. Model performance evaluation and model monitoring
Daniel Kaszyński Małgorzata Wrzosek Kamil Cerazy
6.1 The importance of validation and monitoring
6.2 Validation and monitoring process
6.3 Qualitative methods for credit scoring models validation
6.4 Quantitative methods for credit scoring models validation
6.5 Additional validation dimensions
6.6 Conclusions

7. Model interpretability and explainability
Marcin Chlebus Marta Kłosok Przemysław Biecek
7.1 Shapley values and Break-down
7.2 Permutation Feature Importance
7.3 Ceteris Paribus Plot/Individual Conditional Expectation
7.4 Partial Dependence Plot
7.5 An empirical example
7.6 Instance level
7.7 Global level
7.8 Extensions of base XAI analysis
7.9 Conclusions

8. Performance considerations and platforms for scoring models
Łukasz Kraiński
8.1 Introduction to computation performance
8.2 Performance areas relevant to scoring models
8.3 Platforms for building scoring models

9. Techniques for implementing models in decision engines
Aleksander Nosarzewski
9.1 Challenges in model deployment
9.2 Methods of exporting model object
9.3 Model deployment
9.4 Good practices in MLOps
9.5 Tools useful for MLOps

Conclusions

Bibliography

List of Tables

List of Figures

Index

About the authors