نوع مقاله : مقاله پژوهشی
چکیده
با رشد روز افزون کامپیوتر، مقادیر زیادی از داده ها به وسیله ی سیستم های مختلف به وجود
می آیند. در حال حاضر مسئله ى پیش روی سازمان ها، دیگر جمع آوری داده ها نیست، بلکه توانایی
استخراج اطلاعات مفید از میان آنهاست. همانند دیگر بخش های اقتصادی، شناخت و جذب مشتریان
کم ریسک و سودآور برای صنعت بیمه نیز داراى اهمیت است. بیمه ى اتومبیل یکی از مهم ترین
رشته های بیمه ای در ایران است. اگر شرکت های بیمه به طبقه بندی مشتریان با توجه به ویژگی های
قابل مشاهده بپردازند، می توانند نرخ پوشش دهی بیمه و سود خود را افزایش دهند و از سوی دیگر
فشاری بر افراد با ریسک کم برای جبران خسارات وارده به وسیله ى افراد ریسک زیاد به شرکت های
بیمه وارد نشود. در این تحقیق طبقه بندی ریسکی بیمه گذاران با استفاده از دو تکنیک شبکه ى
انجام شد. در ابتدا عوامل تأثیر گذار بر ریسک بیمه گذاران k-means خودسازمان ده و الگوریتم
شناسایی شد و سپس بخش بندی مشتریان با استفاده از دو روش نام برده به صورت جداگانه انجام
گرفت و ویژگی های مشتریان در هریک از بخش ها مشخص شد. در پایان مقایسه ای بین دو روش
صورت گرفت و تفاوت های آنها بیان شد.
کلیدواژه‌ها
عنوان مقاله [English]
isk Based Comparison between two data mining methods in segmentation of car insurance customers (Case Study: Mellat Insurance Company
چکیده [English]
Due to the sharp rise of the information technology (IT), the amount of data
stored in databases is dramatically on the rise. Analyzing the stored data and
converting it to information and knowledge which is applicable in organizations
requires powerful instruments. As with other economic sectors, recognizing and
attracting low-risk and profitable customers are of high significance for insurance
industry. Car insurance is one of the most important insurance branches
which accounts for a great deal of portfolio of insurance industry. Risk segmentation
of policyholders on the basis of observable features can help insurance
companies to reduce loss, raise the rate of insurance coverage, and prevent them
from making an inappropriate choice in the insurance market. In this study, the
segmentation of comprehensive car insurance customers on the basis of risk was
selected through self-organizing map and K-means. At first, the effective factors
on the risk of policyholders are identified. Then, the insurance policyholders are
segmented using the proposed SOM and K-means. Customers’ characteristics
in every cluster are identified. Finally, the two methods compared with each
other. The advantages and disadvantages of them illustrated
کلیدواژه‌ها [English]
- Customer segmentation
- self-organizing map
- k-means
- Comprehensive auto insurance
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