Awesome Credit Modeling

A growing collection of awesome papers, articles and various resources on credit scoring and credit risk modeling.

Credit scoring is the term used to describe formal statistical methods used for classifying applicants for credit into risk classes. Lenders use such classifications to assess an applicant's creditworthiness and probability of default.Contents

Introduction

Credit Scoring

Institutional Credit Risk

Introduction

Statistical Classification Methods in Consumer Credit Scoring: A Review - Classic introduction and review of the subject of credit scoring.

Consumer Finance: Challenges for Operational Research - Reviews the development of credit scoring (the way of assessing risk in consumer finance) and what is meant by a credit score. Outlines 10 challenges for Operational Research to support modelling in consumer finance.

Machine Learning in Credit Risk Modeling - James (formerly CrowdProcess) is a now-defunct online credit risk management startup that provided risk management tools to financial institutions. This whitepaper offers an overview of machine learning applications in the field of credit risk modeling.

'Lending by numbers': credit scoring and the constitution of risk within American consumer credit - Examines how statistical credit-scoring technologies became applied by lenders to the problem of controlling levels of default within American consumer credit. Explores their perceived methodological, procedural and temporal risks.

Machine Learning in Financial Crisis Prediction: A Survey - Reviews 130 journal papers from the period between 1995 and 2010, focusing on the development of state-of-the-art machine-learning techniques for bankruptcy prediction and credit score modeling. Also presents their current achievements and limitations.

Fintech and big tech credit: a new database - This Working Paper by the Bank of International Settlements, while not as focused on credit risk, maps the conditions for and niches occupied by alternative credit, be it provided by fintechs or big tech companies.

Credit Scoring

Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research - There have been several advancements in scorecard development, including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. This paper compares several novel classification algorithms to the state-of-the-art in credit scoring. In addition, the extent to which the assessment of alternative scorecards differs across established and novel indicators of predictive accuracy is examined.

Classification methods applied to credit scoring: Systematic review and overall comparison - The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the growing number of academic studies about credit scoring shows a variety of classification methods applied to discriminate good and bad borrowers. This paper aims to present a systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis. The general results show the use and importance of the main techniques for credit rating, as well as some of the scientific paradigm changes throughout the years.

Classifier Technology and the Illusion of Progress - A great many tools have been developed for supervised classification, ranging from early methods such as linear discriminant analysis through to modern developments such as neural networks and support vector machines. A large number of comparative studies have been conducted in attempts to establish the relative superiority of these methods. This paper argues that these comparisons often fail to take into account important aspects of real problems, so that the apparent superiority of more sophisticated methods may be something of an illusion. In particular, simple methods typically yield performance almost as good as more sophisticated methods, to the extent that the difference in performance may be swamped by other sources of uncertainty that generally are not considered in the classical supervised classification paradigm.

Awesome Credit Modeling

A growing collection of awesome papers, articles and various resources on credit scoring and credit risk modeling.

Introduction
Credit Scoring
Institutional Credit Risk
Peer-to-Peer Lending
Sample Selection
Feature Selection
Model Explainability

Introduction

Statistical Classification Methods in Consumer Credit Scoring: A Review - Classic introduction and review of the subject of credit scoring.
Consumer Finance: Challenges for Operational Research - Reviews the development of credit scoring (the way of assessing risk in consumer finance) and what is meant by a credit score. Outlines 10 challenges for Operational Research to support modelling in consumer finance.
Machine Learning in Credit Risk Modeling - James (formerly CrowdProcess) is a now-defunct online credit risk management startup that provided risk management tools to financial institutions. This whitepaper offers an overview of machine learning applications in the field of credit risk modeling.
'Lending by numbers': credit scoring and the constitution of risk within American consumer credit - Examines how statistical credit-scoring technologies became applied by lenders to the problem of controlling levels of default within American consumer credit. Explores their perceived methodological, procedural and temporal risks.
Machine Learning in Financial Crisis Prediction: A Survey - Reviews 130 journal papers from the period between 1995 and 2010, focusing on the development of state-of-the-art machine-learning techniques for bankruptcy prediction and credit score modeling. Also presents their current achievements and limitations.
Fintech and big tech credit: a new database - This Working Paper by the Bank of International Settlements, while not as focused on credit risk, maps the conditions for and niches occupied by alternative credit, be it provided by fintechs or big tech companies.

Credit Scoring

Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research - There have been several advancements in scorecard development, including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. This paper compares several novel classification algorithms to the state-of-the-art in credit scoring. In addition, the extent to which the assessment of alternative scorecards differs across established and novel indicators of predictive accuracy is examined.
Classification methods applied to credit scoring: Systematic review and overall comparison - The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the growing number of academic studies about credit scoring shows a variety of classification methods applied to discriminate good and bad borrowers. This paper aims to present a systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis. The general results show the use and importance of the main techniques for credit rating, as well as some of the scientific paradigm changes throughout the years.
Classifier Technology and the Illusion of Progress - A great many tools have been developed for supervised classification, ranging from early methods such as linear discriminant analysis through to modern developments such as neural networks and support vector machines. A large number of comparative studies have been conducted in attempts to establish the relative superiority of these methods. This paper argues that these comparisons often fail to take into account important aspects of real problems, so that the apparent superiority of more sophisticated methods may be something of an illusion. In particular, simple methods typically yield performance almost as good as more sophisticated methods, to the extent that the difference in performance may be swamped by other sources of uncertainty that generally are not considered in the classical supervised classification paradigm.