The Evaluating Auditing Risk by Data Mining Approach (Case Study: Bank Loans)

Document Type : Original Article

Authors
Davood Hemmati 1
Mehdi Arabsalehi 2
Abbas Toloie Eshlaghy 3
1 Accounting، Accounting and Management, Islamic Azad University, Isfahan, Iran
2 Assistant Professor of Accounting Department, University Of Isfahan
3 Full Professor of IAU, Science and Research Branch
Abstract
The purpose of this study was to Evaluating credit Auditing Risk by Data Mining Approach in banks listed in the Tehran Stock Exchange during the year of 2017 that has been developed based on data taken from 200 people who take out a loan of bank. Credit risk is the probable risk of loss resulting from a borrower's failure to repay a loan or meet contractual obligations. Traditionally, it refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for banks. Although it is impossible to know exactly who will default on obligations, properly assessing and managing credit risk can lessen the severity of loss. In auditing Extracting appropriate information from infinite number of data requires modern methods. Data mining is one of these tools and approaches. The research methodology is descriptive -survival and it can be considered as a type of applied research in terms of nature and content. The important inputs variables have been defined in feed forward neural network are annual income, Deposits go throw, house, job, Work experience, Family status, and check book status. In this research one major hypothesis were set forth. After completing the research and doing graphical tests in data mining technique, the research hypothesis was confirmed. Finding show that data mining technique has precise of 97 percentages approximately.
Keywords
Credit Auditing
Feed Forward Neural Networks
Credit System
Banks
Data mining
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