شناسایی آثار دست‌کاری احتمالی در داده‌های اعتبارسنجی بر مدل‌های اعتبارسنجی مشتریان حقوقی با استفاده از داده‌کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه خوارزمی.

2 کارشناسی ارشد، دانشگاه خوارزمی.

چکیده

بانک‌ها برای پیشگیری از زیان‌های ناشی از ریسک اعتباری به دلیل عدم بازپرداخت اقساط تسهیلات بانکی، به اعتبارسنجی مشتریان خود می‌پردازند. بانک‌های توسعه‌ای به­‌دلیل اعطای تسهیلات با شرایط تسهیل­‌شده در نرخ، وثیقه و بازه زمانی بلندمدت تسهیلات صرفاً بر اساس میزان سرمایه در گردش موردنیاز مشتریان، به آن‌ها تسهیلات اعطا می‌کنند. این موضوع باعث می‌شود تا برخی از مشتریان اقداماتی را در صورت­‌های مالی و سایر داده‌­های خود صورت دهند که بتوانند از تسهیلات بیشتر از نیاز خود برخوردار شوند. هدف پژوهش حاضر، بررسی آثار این نوع تقلب بر مدل‌های اعتبارسنجی و ایجاد یک مدل اعتبارسنجی پایدار و حساس به تقلب است. جامعه آماری این پژوهش شامل شرکت‌هایی است که به‌منظور اخذ تسهیلات به شعب بانک توسعه صادرات مراجعه کرده‌اند. متغیرهای این پژوهش شامل 55 متغیر مالی و غیرمالی است که بر اساس آن‌ها مدل اعتبارسنجی ایجادشده و به کارگرفته شده ‌است. ابتدا شرکت‌هایی که احتمالاً در صورت­‌های مالی دست‌کاری احتمالی در سرمایه در گردش داشتند، شناسایی و برچسب‌گذاری شدند و درنهایت با استفاده از ترکیبی از روش‌های داده­کاوی و گزارش شاخص‌های ارزیابی عملکرد به بررسی آثار این نوع تقلب بر مدل‌های اعتبارسنجی پرداخته شد تا بتوان به مدل بهینه و پایدار یک بانک توسعه­‌ای دست یافت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Identifying the Impact of Fraud on Corporate Customers' Credit Scoring by Data Mining Approaches

نویسندگان [English]

  • Seyed Mahdi Sadat Rasoul 1
  • Omid Mahdi Ebadati 1
  • Mahsa Sadat Bakhtiari 2
1 Assistant Professor, Kharazmi University.
2 M.Sc., Kharazmi University.
چکیده [English]

Credit risk is one of the most important risks which banks and financial organizations face. It is related to unpaid and delayed installments. Banks evaluate their customers' credit in order to prevent this hazard. Development banks, which are the focus of this research, fund facilities based on working capital, so customers sometimes do fraud in declaring working capital. Considering fraud consequences and making a credit scoring model with sensitivity to fraud are the main aims of this research.  The statistical population of this research includes companies who have referred to branches of an Iranian Bank. This research includes 55 financial and non-financial variables based on the credit scoring model. In the first step, fraudulent companies have been realized. Finally, in order to offer an optimized and sustainable model through merging machine learning methods and reporting performance evaluation indicators, the impacts of fraud have been considered.

کلیدواژه‌ها [English]

  • Credit Scoring
  • Credit Risk
  • Fraud
  • Working Capital
  • Data Mining

مراجع

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