Network based credit risk models
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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- Credit Modeling
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- 2026-10-10
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Presents the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments…
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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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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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'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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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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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.
Credit Modeling › Peer-to-Peer Lending: “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…”