کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
382167 | 660742 | 2015 | 11 صفحه PDF | دانلود رایگان |
• Social lending has emerged as a viable platform alternative to banks.
• Widespread adoption depends on better risk attribution to borrowers.
• A random forest (RF) based method is proposed for identifying good borrowers.
• Our results indicate RF outperforms traditional credit scoring methods.
With the advance of electronic commerce and social platforms, social lending (also known as peer-to-peer lending) has emerged as a viable platform where lenders and borrowers can do business without the help of institutional intermediaries such as banks. Social lending has gained significant momentum recently, with some platforms reaching multi-billion dollar loan circulation in a short amount of time. On the other hand, sustainability and possible widespread adoption of such platforms depend heavily on reliable risk attribution to individual borrowers. For this purpose, we propose a random forest (RF) based classification method for predicting borrower status. Our results on data from the popular social lending platform Lending Club (LC) indicate the RF-based method outperforms the FICO credit scores as well as LC grades in identification of good borrowers.
Journal: Expert Systems with Applications - Volume 42, Issue 10, 15 June 2015, Pages 4621–4631