Consumer credit risk assessment involves the use of risk assessment tools to manage a
borrower's account from the time of pre-screening a potential application through to the …

The last years have seen the development of many credit scoring models for assessing the
creditworthiness of loan applicants. Traditional credit scoring methodology has involved the …

In the context of credit scoring, ensemble methods based on decision trees, such as the
random forest method, provide better classification performance than standard logistic …

In this paper, we set out to compare several techniques that can be used in the analysis of
imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets …

Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …

In this paper, we study the performance of various state-of-the-art classification algorithms
applied to eight real-life credit scoring data sets. Some of the data sets originate from major …

A wide range of classification methods have been used for the early detection of financial
risks in recent years. How to select an adequate classifier (or set of classifiers) for a given …

Nowadays, the volume of databases that financial companies manage is so great that it has
become necessary to address this problem, and the solution to this can be found in Big Data …

A better development and implementation framework for credit risk scorecards Intelligent
Credit Scoring presents a business-oriented process for the development and …

Credit risk prediction models seek to predict quality factors such as whether an individual
will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of …