“Why are you building another machine learning model on top of the credit reports?
Didn’t the credit reports already tell everything about your customers ?”
Someone from device financing background
Someone asked me that yesterday. Well, my short answer is, “segmentation“.
A brief about the story. We are in micro loan financing business. For each applicant, we will check his credit report from the credit bureau in real time. Even though he or she has a good credit score, we still feed all the information to a machine learning model in order to get a final decision.
Yes, the credit reports tell a lot about your customers. But these information are too general and unable to advise you for the next action — they do not know who and where your ideal customers are. How much to charge for the interest rate ? Will they repay on time ?
Remember,
- Someone who owe PTPTN repayment does not mean he will not pay for the car installment.
- Someone who pay for car installment does not mean he will pay for the device financing.
Thus, by learning from the historical data, your model further segments the customers that fits your business objective optimally.
They may not be someone that from strong credit score. They are those group of folks that willing to take up the offer and pay on time.
Original Post at https://www.linkedin.com/posts/zan-kai-chong_fintech-creditscore-activity-6689691964625055744-tUN7
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