Traditional credit risk steps to serve the vast unbanked population include compensating for perceived higher default incidence with higher interest rates or penalties and other fees, or utilizing microcredit where smaller loan amounts over shorter terms are managed through strong relationships between lenders and borrowers. Neither of these methods are ideal or particularly cost effective. This overly conservative approach can lead to missed opportunities for new revenue streams. The effective use of Big Data from a variety of sources can open up new and better opportunities for lenders.
Broaden the Credit Risk Market
Analyzing mobile and social media data creates insights for potential customers without credit history and still minimize the risk for the provider. Prepaid minutes purchase patterns on mobile phones can indicated steady or uneven cash flow. Timing and frequency of calls can indicate whether someone is working a steady job.
Minimize Credit Risk Non-repayment
Data at the time of a loan application eventually becomes outdated. Data from the customer’s payment behavior, interactions with the financial service provider and social media activity can all be combined to assess a customer’s current financial situation before it becomes a problem for the lender. Utility bill payment history is a strong indicator of fluidity.
How to Secure Access to Relevant Credit Risk Data?
Lenders have two options in capturing Big Data: pay for it or establish partnerships with those who do have the data. Paying for it is often not economically feasible, but partnerships are definitely viable. Mobile phone companies like to provide their customers with desirable offers. Mobile customers seeking loans of their own volition will open up their phone information for review. Credit providers support the development of reward cards that enable retailers to collection information about their customer’s purchasing habits. Governments value the implementation of credit bureaus to serve their neediest citizens.
How to Convert Data into Credit Risk Insights?
Hiring the right talent to provide insights from this endless stream of data is critical to the effective use of Big Data. Improving and increasing computing horsepower is a necessity as the quantity of social media data dwarfs historical credit data. The slightest reduction in loss rates can easily justify these hires and this investment in IT.