I recently had the opportunity to co-present on a webinar hosted by Filene Research Institute titled, “Rightsizing Big Data.” During this webinar, Filene’s Amy Eagan talked about big data and how businesses use it to gather intelligence and influence consumers in an impactful way. She gave examples of how using data analytics to build predictive models can help businesses know how to allocate resources based on predicted demand. Businesses gather intelligence on consumers that help them anticipate a need rather than simplly meet the need. All types of businesses are using data to inform models that help them predict the future and make smarter decisions. Many businesses gather large warehouses of data from their own interactions with their customers. While this data can be very powerful by itself, a whole level of predictive power is added when a dataset gathered from a differing perspective is combined with that in-house data set. The more dimensions that are added to the view of the consumer, the more clearer the picture becomes. This picture starts to include views on who a consumer is, what they like, how they have behaved in the past and what their future trajectory will be.
Credit Unions, as well as any financial institution, house very robust datasets on their members and customers. They have many pieces of personally identifiable information as well as vast amounts of data on loan payment performance, purchase behavior, assets, and investment preferences. The challenge for most credit unions is how to analyze this data and build models that will grow their business.
The Predictive Power of Alternative Data
One external dataset that is used by credit unions with every new loan that they book is credit bureau data. They use the past financial performance of a consumer to predict future behavior. Like many other types of data, predictive power can be added to this tradeline performance data by bringing in a dataset that is gathered from a different perspective. LexisNexis® Risk Solutions has a consumer credit scoring product called RiskView™ which uses a variety of alternative credit data that is mutually exclusive of tradeline data. This alternative data gives additional insight that aids in predicting credit risk and further segmenting borrowers. Additional segmentation of borrowers allows for a more competitive and profitable pricing model.
The credit union industry is still skeptical of allowing alternative data into their loan decisioning while banks and non-bank lenders alike are using it to sharpen their blades and the result is that they are stealing business away from the credit unions.
Credit unions need to use the data that they have along with the vast data available to them to gain or even just retain their market share. Let LexisNexis® Risk Solutions help you grow.