Underwriting is an incredibly important and complex piece of the lending industry. The secret sauce of underwriting, or the underwriting methodology, varies from lender to lender and is often a closely guarded secret. Lenders devote a tremendous amount of time and money to hiring professionals and engaging vendors who can provide the right expertise in guiding them to achieve the perfect recipe. 

Increasingly important in underwriting is the use of “alternative data” – a variety of information about a prospective borrower that, while not directly related to credit, has proven to hold great predictive value. Using alternative data means a lot of data to sort through and find patterns. Accordingly, the latest advances in artificial intelligence and machine learning are put to work by lenders and data providers to get the most out of this data.

The Consumer Financial Protection Bureau recently indicated its consent to the use of alternative data in loan underwriting, thus making a stronger case that the use of such data points is here to stay. Furthermore, it is likely that the use of alternative data will increase as the computer algorithms behind it grow smarter and more powerful.

Defining alternative data

The use of alternative data has been most prevalent in the smaller credit agencies, as opposed to Experian, Equifax, and Transunion (the Big Three), although Transunion has emerged as a leader when compared to its competitors. The smaller agencies, such as Clarity Services, CoreLogic and Factor Trust, focus primarily on the subprime consumer credit market which serves individuals whose files are often thin or nonexistent. This means that traditional credit reports would not provide a useful picture of a prospective borrower’s creditworthiness.

Some products offered by these smaller agencies are still primarily focused on credit data, and others provide specific kinds of alternative data. For example, CoreLogic’s SafeRent offers a product that tracks housing rental history. Milliman’s Intelliscript product focuses on prescription drug history. Lexis Nexis, among multiple data products, provides data on consumers’ loss history in personal property and auto insurance through its Comprehensive Loss Underwriting Exchange, or C.L.U.E, report.

ID Analytics, which offers reports used for identity verification purposes, is itself representative of the utility of alternative data. The company on its website describes its “ID Network” as “a unique cross-industry repository of near real-time consumer information.” This set of data incorporates disparate sources to, in the company’s example, provide insight into “[whether] an individual with no traditional credit history is low risk because of a great payment record on wireless phones and utilities.” 

SafeRent, Intelliscript, and C.L.U.E. have a clear role in their respective industries: a landlord can obviously find great utility in a collection of data that indicates the rental history of a prospective tenant. But ID Analytics demonstrates the power of assembling multiple disparate sources and, through data analytics, putting them together to create a greater whole that reveals additional insights – an approach increasingly favored by lenders in both the consumer and business lending spaces.

Machine learning and artificial intelligence

As alternative data provides many new sources of data for a lender or an underwriter to examine, there is an increase in the amount of computing power needed to sift through and understand it all. This need is filled by machine learning, a subfield within the greater study of artificial intelligence. AI, according to Stanford computer scientist professor John McCarthy, is when a computer is able to perceive its environment and perform tasks that achieve goals in that environment. Machine learning focuses on creating computer algorithms that are able to deduce patterns and use them to make predictions. 

The ultimate ramifications of machine learning on society are unknowable at this point, but the impact is already being seen in the world of lending and credit underwriting. The increase in processing power and reduction in storage costs have created an environment where these algorithms can ingest and process greater amounts of data and, through the learning process, refine their processing efficiency over time.

Since the sets of data lenders use are vast and varied, machine learning is an integral part of turning all those data points into useful underwriting insights. The algorithm, forming new connections between this data and examining it at a deeper level than previously possible, is able to uncover groups of worthy borrowers that were ignored in the past.