Credit scoring in banks and financial institutions via data mining techniques: A literature review
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TLDR
The findings of this literature review reveals that data mining techniques are mostly applied to an individual credit score and there is inadequate research on enterprise and SME credit scoring.Abstract:
This paper presents a comprehensive review of the studies conducted in the application of data mining techniques focus on credit scoring from 2000 to 2012. Yet, there isn‟t adequate literature reviews in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The studies are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques are also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because there is a major issue in a credit scoring problem. The findings of this literature review reveals that data mining techniques are mostly applied to an individual credit score and there is inadequate research on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Hybrid methods are investigated in four categories and two of the frequently used combinations are “classification and classification” and “clustering and classification”. This review of literature analysis provides scope for future research and concludes with some helpful suggestions for further research.read more
Citations
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Journal ArticleDOI
A comparative study on base classifiers in ensemble methods for credit scoring
TL;DR: It is shown that a very simple base classifier, based on imprecise probabilities and uncertainty measures, attains a better trade-off among some aspects of interest for this type of studies such as accuracy and area under ROC curve (AUC).
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Digitalisation and Big Data Mining in Banking
TL;DR: This paper presents the significant progressions and most recent DM implementations in banking post 2013 and identifies the key obstacles and presents a summary for all interested parties that are facing the challenges of big data.
Journal ArticleDOI
An insight into the experimental design for credit risk and corporate bankruptcy prediction systems
TL;DR: This work reviews more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications and provides some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures.
Journal ArticleDOI
Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers
Adaleta Gicić,Abdulhamit Subasi +1 more
TL;DR: A three‐class model that can improve credit risk assessment in the microfinance context is created by analysing different ensemble classification methods that empower the effects of the synthetic minority oversampling technique used in the preprocessing of the imbalanced microcredit data set.
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A new approach for credit scoring by directly maximizing the Kolmogorov–Smirnov statistic
Fang Fang,Yuanyuan Chen +1 more
TL;DR: A new credit scoring method that Directly Maximizes the Kolmogorov-Smirnov statistic (DMKS) is proposed and compares favorably with the popular existing scoring methods considering the tradeoff among predictive ability in terms of KS, computational complexity and practical interpretability.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Introduction to Data Mining
TL;DR: This book discusses data mining through the lens of cluster analysis, which examines the relationships between data, clusters, and algorithms, and some of the techniques used to solve these problems.
Journal ArticleDOI
Neural network credit scoring models
TL;DR: Results demonstrate that the multilayer perceptron may not be the most accurate neural network model, and that both the mixture-of-experts and radial basis function neural network models should be considered for credit scoring applications.
Journal Article
Statistical Classification Methods in Consumer Credit Scoring: A Review
David J. Hand,William Henley +1 more
TL;DR: A wide range of statistical methods have been applied, though the literature available to the public is limited for reasons of commercial confidentiality as discussed by the authors, and particular problems arising in the credit scoring context are examined.