Big Data in Lending for Community Banks and Credit Unions
By harvesting vast amounts of data about the consumer experience, Big Data offers more than a simple snapshot of an individual or group of consumers. Banking institutions used to depend on manual processes to enhance their business. Close relationships with account holders, a comparison of local competitors, or geographical and demographic understanding of their locations are all useful sources of information. In the age of technology, Big Data provides multifaceted insight into all the habits and needs of consumers, as well as a complex evaluation of the institution’s strengths and weaknesses. In 2019, IDC forecasts that global revenue for Big Data and business analytics solutions increased by 12% over the previous year, with the pace of growth expected to continue in the upcoming years.1
The abundance of Big Data may seem overwhelming, but in today’s competitive market it is important to understand how analytics offer banks a necessary competitive edge for their products, their current and potential markets, and the devices available to consumers. While over half of financial institutions with over $50 billion in assets had already implemented Big Data analytics by 2016, less than 9% of community banks and credit unions under $1 billion had invested in these services, most likely due to the cost and complexity involved in implementing the tools and talent to perform the necessary advanced analytics.2
The potential for growth and development in mortgage lending by using data analytics spans myriad categories. By gaining access to deeper profiles of applicants for a mortgage, including credit scores, age and income, institutions can develop robust segmentation of consumers that can be used to expand marketing strategies and consumer targeting. The insights can also benchmark an institution’s performance against peers and larger lenders, increase market opportunities, and describe risk trends.
Data analytics also allows institutions to understand at what point in a mortgage application, consumers tend to abandon the application. Institutions can then evaluate an array of solutions, from the wording of the question to the information requested such as liabilities or employment history. With these insights into the applications, institutions can predict conversion rates based on data early in the application and, for example, choose to invest in credit reports at a point with greater confidence of completion. A recent report by McKinsey & Co. found that one bank was able to reduce churn by 15% after investing in data analytics.3 Not only does this data increase efficiency in the application process, it also helps institutions offer enhanced digital experience when consumers apply online.
Similarly, information from data analytics helps forecast mortgage application activity by day of the week and time of day, allowing institutions to optimize the available staff. Many institutions have limited staff that often wear multiple hats. Data can help them look ahead and proactively manage the application volume and approval rates so they can best staff and support the applicant experience.
Big Data also informs institutions on how to expand products and attract new clients. A recent study in the US revealed that within six months, one bank had automatically validated borrower financials for nearly 75% of its loans, speeding up its pipeline by almost a third and cutting closing times by about five days.4
One important new offering for community banks and credit unions to harvest the power of Big Data analytics is Finastra’s Fusion Mortgagebot Data Insights tool, designed in a collaborative effort with users of Fusion Mortgagebot. Built on the Microsoft Power BI platform, the tool aggregates data across the nearly 1,400 institutions that use the Fusion Mortgagebot platform, extracting the information in manageable dashboards and illustrations.
By finding ways to access the power of advanced data analytics, community banks and credit unions in particular can enhance their business across a diverse range of processes, from providing a best-in-class digital experience to enhancing efficiency on site and in the back office to growing more competitive in local markets with their peers as well as larger institutions.
1 Shirer, Michael, and Goepfert, Jessica. “IDC Forecasts Revenues for Big Data and Business Analytics Solutions.” April 4, 2019. https://www.idc.com/getdoc.jsp?containerId=prUS44998419&utm_medium=rss_feed&utm_source=Alert&utm_campaign=rss_syndication
2 Koechlein, Frank. “Community Banks and Credit Unions Falling Behind In the Data Arms Race.” December 20, 2017. https://thefinancialbrand.com/69200/banking-data-analytics-strategy/
3 Garg, Amit, et al. “Analytics in banking: Time to realize the value.” April 2017. https://www.mckinsey.com/industries/financial-services/our-insights/analytics-in-banking-time-to-realize-the-value
4 Tally, Dennis. “Big Data: Changing the Mortgage Business.” May 2019. https://themreport.com/daily-dose/05-07-2019/big-data-changing-the-mortgage-business