If you are a small business owner, you likely have felt underserved by traditional lenders. If so, you aren’t alone. Many traditional lenders have steadfastly underserved the small business market due, in large part, to a reliance on antiquated systems and data for the underwriting processes – that is, evaluating the risk of a loan. To take that one step further, the reliance on antiquated systems also means that there is an inability to process new and emerging data sources, which contain extraordinarily powerful signals of business performance and credit risk.
As a consequence, small business owners seeking a loan or increased capital have been forced to depend on unreliable and relatively uninformative combinations of personal credit and a restricted set of historical data points. Assembling these data points, which include tax statements and business plans, is an expensive and time-consuming process, and it requires small business owners to anticipate and plan for their financial needs months in advance. That, as any small business owner would tell you, is nearly impossible.
In today’s world, where every person and business is measured in bits and bytes, that’s no longer acceptable. To address this need, new technology-driven companies are emerging and harnessing real-time analytics to transform financial lending, re-think how we assess creditworthiness, and efficiently deliver capital to small businesses. They are creating data and machine-learning systems capable of extracting insights at scale and making decisions that shape every single interaction with a customer. The result is that the entire process – from application to funding to servicing – is more streamlined than that of a traditional lender, giving the small business owner access to capital at the same speed, convenience, and choice that many would associate with online shopping. It’s an entirely new way of lending.
To understand how these companies have developed, one first must think about the shifting information landscape. In traditional lending, banks and financial services firms relied on limited and restricted sets of historical data to determine an individual or business’ creditworthiness. Most prominent among these is the personal credit score of the owner, a standard barometer of risk in consumer lending. While one may accept the personal credit score as a useful baseline, it is by no means an effective measure on its own of a businesses’ health.
Small businesses have massive data footprints, and evaluating their credit risk should involve much more than the owner’s FICO score. Data from credit card processing, accounting ledgers, bank transactions, secretary of state filings – even social media used for verification – contain a wealth of information that add up to a far more accurate and comprehensive portrait of merchant risk.
Not all relevant data are financial in nature. More unusual data, like social data, do not necessarily paint a financial picture of a company, but do help validate information that small business owners provide in their applications. This type of data also helps detect fraud.;
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