A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Jae Kwon Bae,Jinhwa Kim

doi:10.1155/2015/179060

Abstract

This study suggests a methodology called a smart ubiquitous data mining (UDM) that consolidates homogeneous models in a smart ubiquitous computing environment. It tests the suggested model with financial datasets. It basically induces rules from the dataset using diverse rule extraction algorithms and combines the rules to build a metamodel. This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. To verify the feasibility and effectiveness of UDM, personal credit data and personal loan data provided by a Financial Holding Company (FHC) were used in this study. Empirical results indicated that UDM outperforms other models such as LR, BFM, MLP, C5.0, and NR.

Highlights

The Global Financial Crisis in 2008 resulted from unreliable credit ratings assigned to mortgage-backed securities by world-renowned credit rating agencies with vested interests
The result shows that the performance of ubiquitous data mining (UDM) is superior to that of the other models such as logistic regression (LR), neural networks (NN), Bayesian style frequency matrix (BFM), C5.0, and network rule extraction (NR)
The performance of the UDM is compared with that of LR, NN, BFM, C5.0, and NR to prove the efficiency of the suggested model using 5-fold cross validation process

Summary

Introduction

The Global Financial Crisis in 2008 resulted from unreliable credit ratings assigned to mortgage-backed securities by world-renowned credit rating agencies with vested interests. Desai et al [1] investigated the accuracy of credit rating prediction models by using the personal loan information of three United States credit unions. They have been conducted that have compared ANN with other traditional classification algorithms in the field of credit prediction models, since the prediction accuracies of ANN are better than linear discriminant analysis (LDA) and logistic regression (LR). Ince and Aktan [5] compared the performance of several credit rating prediction models applied to credit loan data They used four traditional statistical methods, multiple discriminant analysis (MDA), LR, neural networks (NN), and classification and regression trees (CART), and suggested that CART obtained the best accuracy performance. Cubiles-DeLa-Vega et al [6] used the credit card dataset of Peruvian microfinance institutions to develop credit prediction models

Objectives

Results

Conclusion