• Title/Summary/Keyword: credit scoring system

Search Result 15, Processing Time 0.028 seconds

Robust Design of Credit Scoring System by the Mahalanobis-Taguchi System

  • Su, Chao-Ton;Wang, Huei-Chun
    • International Journal of Quality Innovation
    • /
    • v.5 no.2
    • /
    • pp.1-16
    • /
    • 2004
  • Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis-Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach's effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

Mining Association Rules of Credit Card Delinquency of Bank Customers in Large Databases

  • Lee, Young-Chan;Shin, Soo-Il
    • Journal of Intelligence and Information Systems
    • /
    • v.9 no.2
    • /
    • pp.135-154
    • /
    • 2003
  • Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

  • PDF

Mining Association Rules of Credit Card Delinquency of Bank Customers in Large Databases

  • Lee, Young-chan;Shin, Soo-il
    • Proceedings of the KAIS Fall Conference
    • /
    • 2003.11a
    • /
    • pp.149-154
    • /
    • 2003
  • Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules that ore generating method. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by using association rules. We expect that the sets of rules generated by association rules could act as an estimator of good or bad credit status classifier.

  • PDF

Neural network rule extraction for credit scoring

  • Bart Baesens;Rudy Setiono;Lille, Valerina-De;Stijn Viaene
    • Proceedings of the Korea Inteligent Information System Society Conference
    • /
    • 2001.01a
    • /
    • pp.128-132
    • /
    • 2001
  • In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

  • PDF

Development of Software for Coarse Classifying

  • Jung, Ki-Mun;Kim, Myung-Cheol;Yum, Joon-Keun
    • Journal of the Korean Data and Information Science Society
    • /
    • v.17 no.4
    • /
    • pp.1085-1090
    • /
    • 2006
  • In general, the coarse classifying procedure splits the values of a continuous characteristic into bands and the values of a discrete characteristic into groups of values. Coarse classifying improves the robustness of the credit scoring system but it is complicate and troublesome procedure. Thus, in this paper, we develop a software for coarse classifying by using Visual Basic Language. By using the developed software, we can find the best split easily. Also, this software will help learners to study credit scoring.

  • PDF

Privacy-Preserving Credit Scoring Using Zero-Knowledge Proofs (영지식 증명을 활용한 프라이버시 보장 신용평가방법)

  • Park, Chul;Kim, Jonghyun;Lee, Dong Hoon
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.29 no.6
    • /
    • pp.1285-1303
    • /
    • 2019
  • In the current credit scoring system, the credit bureau gathers credit information from financial institutions and calculates a credit score based on it. However, because all sensitive credit information is stored in one central authority, there are possibilities of privacy violations and successful external attacks can breach large amounts of personal information. To handle this problem, we propose privacy-preserving credit scoring in which a user gathers credit information from financial institutions, calculates a credit score and proves that the score is calculated correctly using a zero-knowledge proof and a blockchain. In addition, we propose a zero-knowledge proof scheme that can efficiently prove committed inputs to check whether the inputs of a zero-knowledge proof are actually provided by financial institutions with a blockchain. This scheme provides perfect zero-knowledge unlike Agrawal et al.'s scheme, short CRSs and proofs, and fast proof and verification. We confirmed that the proposed credit scoring can be used in the real world by implementing it and experimenting with a credit score algorithm which is similar to that of the real world.

Generalized Partially Linear Additive Models for Credit Scoring

  • Shim, Ju-Hyun;Lee, Young-K.
    • The Korean Journal of Applied Statistics
    • /
    • v.24 no.4
    • /
    • pp.587-595
    • /
    • 2011
  • Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

A Commercial Paper Evaluation Model Based on the AHP (계층분석과정에 의한 기업어음 신용평가모형)

  • 이상석;홍재범
    • Korean Management Science Review
    • /
    • v.15 no.1
    • /
    • pp.97-115
    • /
    • 1998
  • This study aims to develop the methodology based on the AHP(Analytic Hierarchy Process) of evaluation for commercial paper. commercial paper is the ma product of merchant banks. commercial paper evaluation is annually performed by the credit-evaluation agency. Credit evaluation is performed by the informal judgemental system, which has potential to induce serious inconsistencies in decision-making. We present an objective scoring model which does not suffer from the weakness of the subjective judgement system. The model used is illustrated by analyzing the commercial paper evaluation for the 3 motor-companies(H, K and S motors).

  • PDF

ESTABLISHMENT OF CONSTRUCTION INDUSTRY CREDIT GUARANTEE SYSTEM-BASED ON TAIWAN'S CONSTRUCTION INDUSTRY

  • Ting-Ya Hsieh;Tsung-Shi Liu
    • International conference on construction engineering and project management
    • /
    • 2011.02a
    • /
    • pp.399-406
    • /
    • 2011
  • Various construction bonds and warranties critically burden the general contractor. Also, sporadic or cumulative delays of progress payment by the owner can further trap the contractor in a financial quagmire. Facing the possibility of cash flow deficiency and callous response from the banks, most construction firms may become financially incapable of market competition, and attractive project tenders become a bidding game among few deep-pocket players. The downside of such market environment is that the depth of pocket, rather than that of professional competency dictates the choice of market winners. In Taiwan, this has been a potential crisis to the construction industry after the financial crisis which started out since 2008. To encounter this problem, this research will examine the means to better manage the construction industry. Essentially, a credit guarantee system (CGS) is the prime solution to strengthen a bank's confidence in any particular construction firm. Thus establishing a national platform which evaluates and rewards a construction firm's overall credibility is pivotal, and this third-party rated credit can help a bank to render a loan more wisely. Finally, this paper will propose the ideal operating schemes of construction-specific CGS in Taiwan and a credit scoring prototype model for construction industry, as reference for the government and banks, respectively.

  • PDF