ارائه یک مدل ترکیبی مبتنی بر یادگیری ماشینی برای طبقه بندی مشتریان مشترک صنعت بانکداری و بیمه
محورهای موضوعی : مدیریت بازرگانیحمیدرضا امیرحسنخانی 1 , عباس طلوعی اشلقی 2 , رضا رادفر 3 , علیرضا پورابراهیمی 4
1 - دانشجوی دکتری گروه مدیریت فناوری اطلاعات، واحد امارات، دانشگاه آزاد اسلامي، دبی، امارات متحده عربی
2 - استاد گروه مدیریت صنعتی،واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ایران
3 - استادگروه مدیریت صنعتی، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ایران
4 - استادیار گروه مدیریت، واحد کرج، دانشگاه آزاد اسلامي، کرج، ایران
کلید واژه: الگوریتم ژنتیک, بیمه, بانک, ماشین بردار پشتیبان, طبقه بندی,
چکیده مقاله :
رقابت¬های جهانی، صنابع پویا و چرخه¬های نوآوری و فناوری که به سرعت در حال کوتاه شدن هستند همگی چالش¬های مهمی را برای صنعت مالی، بانکداری و بیمه ایجاد کرده¬اند و نیاز به تجزیه و تحلیل داده¬ها جهت بهبود فرآیندهای تصمیم¬گیری- در این سازمان¬ها بیش از پیش اهمیت پیدا کرده است؛ در این میان، داده¬هایی که در پایگاه-های اطلاعاتی این سازمان¬ها نگهداری می¬شوند به عنوان منابع ارزشمند اطلاعات و دانش مورد نیاز جهت تصمیم-گیری¬های سازمانی مطرح می¬باشند؛ در این تحقیق بر روی مشتریان مشترک صنعت بانکداری و بیمه تمرکز شده است. هدف از این تحقیق، ارائۀ روشی جهت پیش¬بینی عملکرد مشتریان جدیدالورود بر مبنای رفتار مشتریان پیشین است؛ برای این منظور، از یک مدل ترکیبی مبتنی بر ماشین بردار پشتیبان و الگوریتم ژنتیک استفاده شده است؛ بدین ترتیب که ماشین بردار پشتیبان، وظیفه مدل‌سازی رابطه بین عملکرد مشتریان و اطلاعات هویتی آنها را بر عهده دارد و الگوریتم ژنتیک، وظیفه تنظیم و بهینه‌سازی پارامترهای ماشین بردار پشتیبان را عهده‌دار است. نتایج به دست آمده از طبقه بندی مشتریان- با استفاده از مدل پیشنهادی در این تحقیق- طبقه بندی مشتریان با دقت بالای ۹۹ درصد است.
Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.
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