The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
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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- 2026-10-10
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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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Machine learning predictivity applied to consumer creditworthiness fbj.springeropen.com
Analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, exploring machine learning techniques, and comparing their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are based on usual classification…
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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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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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Consumer credit-risk models via machine-learning algorithms alo.mit.edu
The authors apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. They are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults.
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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…
Credit Modeling › Credit Scoring: “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…”