Credit Modeling
39 items from mourarthur/awesome-credit-modeling ★176
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Network based credit risk models tandfonline.com
Peer-to-Peer lending platforms may lead to cost reduction, and to an improved user experience. These improvements may come at the price of inaccurate credit risk measurements. The authors propose to augment traditional credit scoring methods with “alternative data” that consist…
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'Lending by numbers': credit scoring and the constitution of risk within American consumer credit tandfonline.com
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.
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Good practice in retail credit scorecard assessment tandfonline.com
In retail banking, predictive statistical models called ‘scorecards’ are used to assign customers to classes, and hence to appropriate actions or interventions. Such assignments are made on the basis of whether a customer's predicted score is above or below a given threshold…
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Machine Learning in Credit Risk Modeling slideshare.net
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.
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Credit scoring methods: Latest trends and points to consider sciencedirect.com
"(...) This article aims at providing a systemic review of the most recent (2016–2021) articles, identifying trends in credit scoring using a fixed set of questions. The survey methodology and questionnaire align with previous similar research that analyses articles on credit…
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Regulatory learning: How to supervise machine learning models? An application to credit scoring sciencedirect.com
The arrival of Big Data strategies is threatening the latest trends in financial regulation related to the simplification of models and the enhancement of the comparability of approaches chosen by financial institutions. Indeed, the intrinsic dynamic philosophy of Big Data…
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A multi-objective approach for profit-driven feature selection in credit scoring sciencedirect.com
In credit scoring, feature selection aims at removing irrelevant data to improve the performance and interpretability of the scorecard. Standard techniques treat feature selection as a single-objective task and rely on statistical criteria such as correlation. Recent studies…
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Classification methods applied to credit scoring: Systematic review and overall comparison sciencedirect.com
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…
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A comparative study on base classifiers in ensemble methods for credit scoring sciencedirect.com
In the last years, the application of artificial intelligence methods on credit risk assessment has meant an improvement over classic methods. Recent works show that ensembles of classifiers achieve the better results for this kind of tasks.
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Credit scoring using the clustered support vector machine sciencedirect.com
Introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations associated with traditional nonlinear support vector machine (SVM) based methods for classification…
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Combination of feature selection approaches with SVM in credit scoring sciencedirect.com
An effective classificatory model in credit scoring will objectively help managers who rely on intuitive experience. This study proposes four approaches using the SVM (support vector machine) classifier for feature selection that retain sufficient information for classification…
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Multiple classifier application to credit risk assessment sciencedirect.com
(Corrigendum) - This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how such accuracy could be improved by using classifier ensembles.
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients sciencedirect.com
This research compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of…
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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research sciencedirect.com
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…
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Recent developments in consumer credit risk assessment sciencedirect.com
The riskiness of lending to a credit applicant is usually estimated using a logistic regression model though researchers have considered many other types of classifier, but data quality issues may prevent these laboratory based results from being achieved in practice. The…
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A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers sciencedirect.com
Surveys the techniques used — both statistical and operational research based — to help organisations decide whether or not to grant credit to consumers. It also discusses the need to incorporate economic conditions into the scoring systems and the way the systems could change…
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Instance sampling in credit scoring: An empirical study of sample size and balancing research.lancs.ac.uk
Discusses the traditional sampling conventions in credit modeling and argues that using larger samples provides a significant increase in accuracy across algorithms.
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Classifier Technology and the Illusion of Progress projecteuclid.org
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…
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Explainable Machine learning in Credit Risk Management papers.ssrn.com
Proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms.
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Credit Scoring and the Availability, Price, and Risk of Small Business Credit muse.jhu.edu
Finds that small business credit scoring is associated with expanded quantities, higher averages prices, and greater average risk levels for small business credits under $100,000, after controlling for bank size and other differences across banks.
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Data mining feature selection for credit scoring models link.springer.com
The features used may have an important effect on the performance of credit scoring models. The process of choosing the best set of features for credit scoring models is usually unsystematic and dominated by somewhat arbitrary trial. This paper presents an empirical study of…
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A literature review on the application of evolutionary computing to credit scoring link.springer.com
The aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.
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Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications link.springer.com
An important ingredient to accomplish the goal of a more efficient use of resources through risk modeling is to find accurate predictors of individual risk in the credit portfolios of institutions. In this context the authors make a comparative analysis of different statistical…
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Random Survival Forests Models for SME Credit Risk Measurement link.springer.com
Extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes (SMEs), proposing a non-parametric approach based on Random Survival Forests (RSF) and comparing its performance with a standard logit model.
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Consumer Finance: Challenges for Operational Research jstor.org
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.
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Statistical Classification Methods in Consumer Credit Scoring: A Review jstor.org
Classic introduction and review of the subject of credit scoring.
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Bankruptcy prediction for credit risk using neural networks: A survey and new results ieeexplore.ieee.org
The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work reviews the topic of bankruptcy prediction, with emphasis on neural-network (NN) models and develops an…
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Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring ieeexplore.ieee.org
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do…
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Machine Learning in Financial Crisis Prediction: A Survey ieeexplore.ieee.org
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.
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