A hybrid neural network approach for credit scoring

Abstract

The development of an effective credit scoring model has become a very important issue as the credit industry is confronted with ever-intensifying competition and aggravating bad debt problems. During the past few years, a substantial number of studies in the field of statistics have been conducted to improve the accuracy of credit scoring models. In order to refine the classification and decrease misclassification, this paper presents a two-stage model. Focusing on classification, the first stage aims at constructing an artificial neural network (ANN)-based credit scoring model to categorize applicants into the group of accepted (good) credit and the group of rejected (bad) credit. Switching from classification to reassignment, the second stage proceeds to reduce the Type I error by retrieving the originally rejected good credit applicants to conditional acceptance using the Case-Based Reasoning (CBR) classification technique. The proposed model (RST-ANN-CBR) is applied to a credit card dataset to verify its effectiveness. As the results indicate, the proposed model is able to achieve more accurate credit scoring than four other methods; more importantly, it is validated to recover potentially lost customers and to increase business revenues.

A Self-learning Audio Player That Uses a Rough Set and Neural Net Hybrid Approach

“…In [3], the researchers apply a neural network and rough set method to solve the problem of credit scoring. In that paper, the rough set method is used to core the training set by which we mean the determination of significant attributes.…”

“…It is about how to initialize the network weights and the number of iterations [3]. It will take a long time and a lot of iterations to run the neural network if we do not initialize a better weight than randomly generate the weigh for example.…”

“…When we use the term 'portability' in this thesis, we mean software portability. Software portability is means that the engines are not only useful in this project, but also they are easy to be applied to other projects that need these engines like the projects in [3][4]. When some projects or software need an engine to analysis the data by using RS hybrid NN, they can use our engine to work on that because the hybrid engine is separated from the music player.…”

Customer Credit Risk Assessment using Artificial Neural Networks

“…Hsieh and Hung in 2010, by using ensemble classification methods, neural networks and support vector machine proposed a classification system for bank applicants [8]. Chuang and Huang in 2011 proposed a two-stage method; in the first stage a neural network was used to classify an applicant as accepted or rejected and in second stage in order to identify rejected applicants who should have been accepted, a case-based reasoning was used [9]. Akkoc in 2012, proposed a three stage hybrid Adaptive Neuro Fuzzy Inference System model (ANFIS) for credit scoring, which is based on statistical techniques and Neuro Fuzzy.…”

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

“…Regarding recent credit scoring techniques, artificial neural network [4,5] has been criticized for its poor performance when incorporating irrelevant attributes or small data sets, while support vector machine, motivated by statistical learning theory [6,7], is particularly well suited for coping with a large number of explanatory attributes or sparse data sets [8][9][10][11]. Baesens et al studied the performance of various state-of-the-art classification algorithms on eight real-life credit scoring data sets [12].…”