Using Unstructured Data to Tidy Up Credit Reporting

The consumer credit reporting agencies in the U.S. — especially the big three of Equifax, Experian, and TransUnion — help consumers and society in general by aligning costs with risk. Analytics is now helping by reducing uncertainties in the alignment.

The credit data housed in the reporting agencies traditionally focuses on people’s personal credit and payment history, down to details about how promptly they’ve repaid loans and when they were late on a payment. Companies that grant credit, ranging from mortgages to car loans to credit card limits, use the agency’s information to decide what products to offer and on what terms. People with “clean” histories may get better terms; people with smudged financial backgrounds may not. But other dirt — inaccuracies and incomplete information — leads to uncertainty and costs everyone.

Greg Jones is one of the data specialists tasked with making the data as clean as possible. As vice president of Enterprise Data & Analytics at Equifax — a global provider founded in 1899 that generates 158 billion credit-score updates per month and operates or has investments in 19 countries — Jones says he’s “accountable for our enterprise Search Match and Entity Resolution systems.”

In a conversation with Sam Ransbotham, an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative, Jones explains that Equifax is expanding its sourcing of data to include unique data assets and exploring social media and other unstructured data sources, and that this expansion has the potential to make individual profiles even more exact, improving the market for everyone.

Let’s start by hearing about what your team at Equifax is doing in analytics.

We ingest data into our systems, and some of that data is not always clean or straightforward. What we do is use a set of deterministic business rules and then conditional probability theory to determine if a record belongs to a certain person.

On the one hand, we look at nice, beautifully structured data from a credit card company, where the information is of very high quality. On the other, we could look at information that is from other sources, like some type of unstructured or semi-structured data.