A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

Nosratabadi, Hamid Eslami; Pourdarab, Sanaz; Nadali, Ahmad

doi:10.7763/ijiet.2011.v1.25

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The most widespread method of minimizing the risk used by the commercial banks is overstating the interest rate or establishing various types of surcharges (commissions) for using the loan, as a result of which there is a transfer of credit risk onto the responsible borrowers (Wilhelmsson and Zhao 2018). Apart from that, many banks do not have an appropriate level of organization for the monitoring and forecasting of operational risks due to the fact that the risk managers do not see them as a real threat, except for fraudulent actions of the borrowers or bank staff (Drobyazko et al 2020a;Nosratabadi et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Modeling of Bank Credit Risk Management Using the Cost Risk Model

Yanenkova¹,

Nehoda

Drobyazko³

et al. 2021

JRFM

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This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

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Section: Introductionmentioning

confidence: 99%

Modeling of Bank Credit Risk Management Using the Cost Risk Model

Yanenkova¹,

Nehoda

Drobyazko³

et al. 2021

JRFM

View full text Add to dashboard Cite

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Analytics using R for predicting credit defaulters

Sudhamathy

Venkateswaran

2016

2016 IEEE International Conference on Advances in Computer Applications (ICACA)

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Credit Risk Analysis and Prediction Modelling of Bank Loans Using R

Sudhamathy¹

2016

IJET

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Abstract-Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims to extract useful knowledge from tremendous amount of complex data sets. In this paper we aim to design a model and prototype the same using a data set available in the UCI repository. The model is a decision tree based classification model that uses the functions available in the R Package. Prior to building the model, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The final model is used for prediction with the test dataset and the experimental results prove the efficiency of the built model. Keyword-Credit Risk, Data Mining, Decision Tree, Prediction, R I. INTRODUCTION Credit Risk assessment is a crucial issue faced by Banks nowadays which helps them to evaluate if a loan applicant can be a defaulter at a later stage so that they can go ahead and grant the loan or not. This helps the banks to minimize the possible losses and can increase the volume of credits. The result of this credit risk assessment will be the prediction of Probability of Default (PD) of an applicant. Hence, it becomes important to build a model that will consider the various aspects of the applicant and produces an assessment of the Probability of Default of the applicant. This parameter PD, help the bank to make decision if they can offer the loan to the applicant or not. In such scenario the data being analysed is huge and complex and using data mining techniques to obtain the result is the most suitable option provided its efficient analytical methodology that finds useful knowledge. There are many such work has been done previously, but they have not explored the use of the features available in R package. R Package is an excellent statistical and data mining tool that can handle any volume of structured as well as unstructured data and provide the results in a fast manner and presents the results in both text and graphical manners. This enables the decision maker to make better predictions and analysis of the findings. The aim of this work is to propose a data mining framework using R for predicting PD for the new loan applicants of a Bank. The data used for analysis contains many inconsistencies like missing values, outliers and inconsistencies and they have to be handled before being used to build the model. Only few of the customer parameters really contribute to the prediction of the defaulter. So, those parameters or features need to be identified before a model is applied. To classify if the applicant is a defaulter or not, the best data mining approach is the classification modelling using Decision Tree. The above said steps are integrated into a single model and prediction is done ba...

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A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

Cited by 3 publications

References 17 publications

Modeling of Bank Credit Risk Management Using the Cost Risk Model

Modeling of Bank Credit Risk Management Using the Cost Risk Model

Analytics using R for predicting credit defaulters

Credit Risk Analysis and Prediction Modelling of Bank Loans Using R

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