نوع مقاله : مقاله پژوهشی - کاربردی

نویسندگان

گروه مدیریت، دانشکده مدیریت، اقتصاد و حسابداری، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران.

10.22105/dmor.2021.247229.1220

چکیده

عدم توانایی بانک ها در اعتبارسنجی و ارزیابی مالی مشتریان و پیش بینی دقیق ریسک اعتباری تسهیلات گیرندگان، تاثیرات مخربی بر سیستم مالی جهانی و فعالیت های اقتصادی داشته و از اصلی ترین دلایل بحران های مالی جهانی در سال های اخیر بوده اند. هدف این تحقیق، تدوین مدل پیش‌بینی اعتباری مشتریان حقوقی بانک‌های خصوصی با بهره‌گیری از الگوریتم‌های فراابتکاری در شعبه های بانک پاسارگاد شمال غرب کشور بوده است. این تحقیق براساس هدف پژوهش، توسعه‌ای و براساس روش انجام کار توصیفی می باشد.جامعه آماری این تحقیق را دو بخش خبرگان و مدیران بانکی استان آذربایجان شرقی و مشتریان حقوقی بانک پاسارگاد در شمال‌غرب کشور تشکیل می دهند. حجم نمونه آماری برای جامعه اول، 58 خبره بانکی استان اعم از مدیران، مسئولین اعتباری و روسای شعب با سابقه کار اعتباری بانک های خصوصی تعیین شده و برای جامعه دوم، براساس نمونه‌گیری هدفمند 427 مشتری حقوقی بانک پاسارگاد انتخاب شده است. بمنظور جمع‌آوری داده‌ها از پرسشنامه و اسناد و مدارک بانک پاسارگاد بهره گرفته شده و روایی پرسشنامه به صورت روایی محتوا و براساس شاخص‌های نسبت روایی محتوا و شاخص روایی محتوا و پایایی پرسشنامه با استفاده از ضریب آلفای کرونباخ مورد بررسی و تأیید قرار گرفته است. بمنظور تجزیه و تحلیل داده‌ها از آزمون t، تحلیل عاملی تأییدی، شبکه عصبی مصنوعی چند لایه، شبکه عصبی آموزش دیده با الگوریتم ژنتیک، شبکه عصبی آموزش دیده با الگوریتم ازدحام ذرات و شبکه عصبی آموزش دیده با الگوریتم تکامل تفاضلی استفاده شده است. یافته‌های پژوهش نشان می‌دهد که هر چهار مدل فوق قادر به پیش‌بینی اعتباری مشتریان حقوقی بانک‌های خصوصی هستند و بهترین روش برای پیش‌بینی اعتباری مشتریان حقوقی بانک‌های خصوصی، شبکه عصبی آموزش دیده با الگوریتم تکامل تفاضلی با کمترین مقدار خطا نسبت به سه روش دیگر است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Compilation a credit forecasting model for legal customers of private banks using meta-heuristic algorithms (Case study: Pasargad Bank branches in the north, west of the country)

نویسندگان [English]

  • Mohammadreza Etebari
  • Naser Feghhi Farahmand
  • Soleyman Iranzadeh

Department of Management, Faculty of Management, Economics and Accounting, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

چکیده [English]

Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The aim of this study is to compile a credit forecasting model for legal customers of private banks by using meta-heuristic algorithms in the branches of Pasargad Bank in the northwest of Iran. This research is base on the purpose of developmental research and based on the method of performing descriptive work. The statistical population of this study is in two sections of banking experts and legal customers of Pasargad Bank in the northwest of the Iran. The statistical sample size for the first community of 58 banking experts including managers, credit officials and heads of branches in with credit work experience in private banks and for the second community, 427 legal clients were selected based on targeted sampling. In order to collect data in this research, a questionnaire and documents of Pasargad Bank have been used. The validity of the questionnaire was investigated as content validity and based on the indicators of content validity ratio and content validity index. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient. In order to analyze the research data, t-test, confirmatory factor analysis, multilayer neural network, genetically trained neural network, trained neural network with particle swarm optimization and trained neural network with differential evolution will be used. The research findings show that all four models are able to predict the credit predictions of the legal customers of private banks and the best way to predict the credit predictions of the legal customers of private banks is the neural network trained with differential evolution algorithm with the least amount of error compared to the other three methods.

کلیدواژه‌ها [English]

  • Credit Forecasting
  • Neural Network Algorithm
  • Genetic Algorithm
  • Differential evolution algorithm
  • Particle Swarm Optimization Algorithm
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