The paper-based income statement and balance sheet models for SMB lending are inefficient at best and may not be the best arbiters of credit risk. Provenir Managing Director Paul Thomas told PYMNTS about the benefits of data, both structured and unstructured, in the (digital) SMB lending process.
Data is simply data until it is put to use.
And in the lending process, data can mean the difference between good risk and bad. But the challenge lies in grabbing the right data, at the right time, with speed. In some cases, banks are adapting to, and adopting, risk models that go well beyond the traditional income statement and balance sheet-based models.
In one recent announcement, Provenir, which provides real-time risk decisioning technology, said it had been chosen by TBI Bank, operating in Bulgaria and Romania, to help with credit decisions for consumers and also small businesses.
In an interview with PYMNTS, Provenir Managing Director Paul Thomas said that the cloud platform is flexible enough so that it can be used on a market-by-market basis and is granular enough so that it can even drill down into demographics on what he termed a “city-by-city or even town-by-town” basis. As such, he said, value lies in the ability to work with both structured and unstructured data to help speed time to lending — in this case, a matter of seconds versus the several-week timeframes that are the hallmark of the traditional lending process.
That comes against a backdrop where, as is no secret, banks have been hesitant to lend to smaller entities and have mandates in place that they must satisfy before parting with funds. Thomas noted that, in some cases, banks will not lend to firms below a certain revenue threshold or with a small staff, with the assumption that limited size or operating history may translate into poor risk profiles.
Using unstructured data, such as that found across social media conduits, said Thomas, has its benefits in the lending process, especially for SMBs. Speaking generally about lending profiles, he said that, regardless of whether the applicant is an individual or a business, “consumer behavior doesn’t stand still — it changes.”
And by tracking everything from work history to qualitative data tied to reputations, lenders can, in a data-driven way, speed credit decision-making. Another advantage comes as lenders using the cloud (and the mix of structured and unstructured data) are able to get what the executive likened to “an early warning system” about potential risk and KYC efforts. In addition, this allows lenders to more successfully deploy their own sales teams in developing business, he added, avoiding merchants or other firms that could be deemed, through confluence of data analytics, high-risk.