Lending institutions are becoming more proactive in using big data to screen loan applications. This goes beyond core business processes where customer information is sought. Yes, this is the world of alternative credit data that is beyond your traditional perception of consumer data. When used properly, it can offer you useful indicators to unearth more borrowers with acceptable risk quotient.
Get a Sense of Contemporary Market Situation
Lending institutions across the nation are easing their credit standards. Pessimism that gained roots during and after the recession is being slowly washed away. It seems that credit score is back to its glory days where a good score qualified consumers across multiple lending niches. However, this doesn’t mean that lending has become vaguely relaxed. The emphasis is still on identifying consumer profiles that present the least risk exposure. The mortgage marketplace is still recuperating while banks with recession-hammered reputations are also treading with caution. However, if you are a consumer-centric lending institution, you must be observing an increasing number of loan applications from retail customers. Here, the lending scale might be smaller but the margins are still reasonable. As a lender, you will need to handle more and more credit scores that are short of being ideal.
For many analysts, Subprime Lending was among the most prominent causes of the 2008 financial crisis. This was largely seen in the mortgage industry. Subprime loans were aggressively marketed only to realize later that the incidence of bad loans was rising exponentially. Now, Subprime Lending is making a comeback of sorts, albeit in a more subtle manner. Consumers with a FICO credit score of 660 or below are exploring the increasing borrowing options. Subprime borrower numbers have been rising in the automobile industry too. Subprime borrowing is expected to sustain through to 2014 and beyond. However, people with susceptible credit histories, with multiple loans, are not likely to benefit from this trend.
Alternative Credit Data
As a lender, you might be very circumspect about low credit scores. The apprehension is understandable. However, the emergence of big data should ease your worries. Using big data, consumer data collected from various sources can be gathered and methodically indexed. When analyzed systematically, it can identify low credit profiles that are not likely to default. This is like finding a hidden credit score—Hidden in the sense that the borrower doesn’t really know about it. Using data derived from non-conventional payment avenues, records of small loans, and lapsed credit accounts, understanding and predicting the payment behavior becomes easier. This can include payments made towards essentials like mobile phone connectivity or cable T.V.
This is particularly useful to categorize borrowers with manageable risk, but very little credit history. Millions of such consumers, including those with restored credit histories, present a significant volume that cannot be ignored. Yes, you need to explore beyond the conventional credit files to enter this untapped market. Such data is being increasingly sought by credit reporting agencies. This often includes tapping into public records for income information. For borrowers, it is a golden chance to get their credit-bruised lives back on track. For aggressive lenders, this alternative data gives a chance to drive their margins. Lenders are more likely to approve applications with a history of acceptable debts and a timely payment pattern.