Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications
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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- 2026-10-10
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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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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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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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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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Consumer credit-risk models via machine-learning algorithms alo.mit.edu
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Credit Modeling › Credit Scoring: “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…”