مقایسه عملکرد مدل‌های کلاسیک و هوش مصنوعی در پیش‌بینی وضعیت اعتباری مشتریان بانک

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد گروه مدیریت صنعتی، دانشکده علوم اقتصادی و اداری، دانشگاه مازندران

2 استاد گروه مدیریت صنعتی،دانشکده علوم اقتصادی و اداری، دانشگاه مازندران

چکیده

 در حال حاضر در نظام بانکداری، عدم بازپرداخت تسهیلات به یکی از بزرگ‌ترین مسائل تبدیل شده‌است و به‌دلیل عدم وجود یک سیستم مناسب برای تخصیص تسهیلات، بانک‌ها و موسسات مالی دچار مشکلات عدیده‌ای ازجمله افزایش حجم مطالبات معوق شده‌اند. نظر به اهمیت ریسک اعتباری، بانک‌های تجاری در سطح دنیا درگذشته اغلب از روش قضاوتی برای تعیین ریسک استفاده می‌نمودند، لکن استفاده از این روش‌ها با توجه به توان محدود انسان‌ها در تحلیل هم‌زمان فاکتورهای مختلف مؤثر بر ریسک اعتباری در مقایسه با روش‌های آماری و هم‌چنین روش‌های هوش مصنوعی از کارایی کمتری برخوردار است. به همین منظور این تحقیق درصدد است تا کارایی مدل رگرسیون لجستیک و شبکه عصبی مصنوعی را در تشخیص وضعیت اعتباری مشتریان بانک در فاصله زمانی سال 1388-1392 بسنجد. بررسی نتایج نشان داد که دقت کل مدل شبکه عصبی در داده‌های آموزش 87% و رگرسیون لجستیک 2/77% تعیین شده‌است و خطای نوع اول و دوم در شبکه عصبی به میزان قابل‌ملاحظه‌ای نسبت به روش دیگر کاهش یافته است. با توجه به نتایج نمی‌توان انتظار داشت مدل‌های آماری با مفروضات کلاسیک نظیر خطی بودن روابط متغیرها، بتوانند ریسک اعتباری مشتریان را به درستی ارزیابی نماید؛ از این رو بکارگیری یا تلفیق تکنیک‌های هوش مصنوعی در این مساله ضرورتا توصیه می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of the Performances of Classical Models and Artificial Intelligence in Predicting Bank Customers' Credit Status

نویسندگان [English]

  • Narjes Ghasemnia Arabi 1
  • Abdolhamid Safaei Ghadikolaei 2
1 MSc. Department of Industrial Management, Faculty of Economic and Administrative Science, University of Mazandaran
2 Professor, Department of Industrial Management, Faculty of Economic and Administrative Science, University of Mazandaran
چکیده [English]

Currently, in the banking system, defaults in the repayment of loans have become one of the biggest problems, and banks and financial institutions have faced many problems such as the increase in the volume of outstanding receivables due to the lack of an appropriate system for allocating facilities. Considering the importance of credit risks, commercial banks used to apply judgment methods for determining those risks. However, the use of these methods was not efficient enough due to limited human abilities and, at the same time, various factors affecting credit risks in contrast to statistical methods as well as artificial intelligence methods. For this reason, this article measures the efficiency of logistic regression models and artificial neural networks in detection of bank customers’ credit status in the period of 2009-2013. The results indicated that the total accuracy rates of the Artificial Neural Network model and the Logistic Regression model were 87% and 77.2% respectively, and the error types I and II were reduced significantly in the neural network. According to the results, statistical models cannot be expected to properly evaluate the credit risk of customers with classical assumptions such as the linear relationship between variables. Therefore, application and integration of artificial intelligence techniques is strongly recommended in this regard.

کلیدواژه‌ها [English]

  • Credit scoring
  • Artificial neural network
  • Logistic regression
  • Banking system
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