مقاله انگلیسی رایگان در مورد الگوریتم بهینه‌سازی کمینه برای مخاطره اعتبار مالی (الزویر)

مقاله انگلیسی رایگان در مورد الگوریتم بهینه‌سازی کمینه برای مخاطره اعتبار مالی (الزویر)

 

مشخصات مقاله
عنوان مقاله An Improved SMO Algorithm for Financial Credit Risk Assessment–Evidence from China’s banking
ترجمه عنوان مقاله الگوریتم اصلاح شده بهینه‌سازی متوالی کمینه برای مخاطره اعتبار مالی- مدرکی از بانکداری چین
فرمت مقاله  PDF
نوع مقاله  ISI
سال انتشار  مقاله سال ۲۰۱۸
تعداد صفحات مقاله  ۱۷ صفحه
رشته های مرتبط  اقتصاد
گرایش های مرتبط  اقتصاد مالی
مجله محاسبات عصبی – Neurocomputing
دانشگاه Academy of Mathematics and Systems Science Chinese Academy of Sciences China
کلمات کلیدی ارزیابی ریسک اعتبار مالی، SVM، بهینه‌سازی متوالی کمینه (SMO)، تنظیم کاری چهر متغیره
کد محصول  E5487
نشریه  نشریه الزویر
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بخشی از متن مقاله:
۱٫ INTRODUCTION

 The assessment of financial credit risk is emerging as an important research topic in the banking industry. The financial credit risk indicates the risk associated with financing, in other words, a borrower cannot pay the lenders, or goes into loan default. Credit risk assessment has become a particularly challenging issue for banks and financial institutions to access the performance of borrowers (customers), serving as the impetus to evaluate the credit admission or potential business failure of customers in order to make early actions. The great loss resulted from the financial distress or bankruptcy of customers usually leads to considerable criticism on the functionality of financial institutions due to the inappropriate evaluation of credit risk. Most governments are forced to implement rescue plans for the banking systems with more effective credit risk assessment. In China, the massive credit boom poses challenge for the quality of bank assets. In fact, total bad loans reached 1.27 trillion yuan at the end of 2015, the highest since the global financial crisis, on the back of an economic slowdown and a ballooning corporate debt. An meticulous management information system is in urgent requirement. Credit risk assessment, which enables or supports an early-warning detection and fast response mechanism, is a key in this system. Since 2004, the China Banking Regulatory Commission (CBRC), which is responsible for regulation of banking industry in China, enables a reporting system for credit data collection. In recent years, CBRC has attached much importance to risk characteristics mining, custom’s behavior analysis and risk assessment model.

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