Machine learning predictivity applied to consumer creditworthiness
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…
- from
- Credit Modeling
- added
- 2026-10-10
- likes
- 0
similar
-
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.
-
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 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…
-
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…
-
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.
-
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…
Credit Modeling › Credit Scoring: “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…”