Multiple classifier application to credit risk assessment
(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.
- from
- Credit Modeling
- added
- 2026-10-10
- likes
- 0
similar
-
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.
-
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…
-
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…
-
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…
-
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 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…
Credit Modeling › Credit Scoring: “(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.”