As economic changes and regulatory winds continue to blow faster every year, credit unions should prepare for the future by effectively collecting and storing their data.
Recently, in my native state of Minnesota, a winter storm warning caused people to panic. Many people rushed to their local retailer for common household goods like fuel and food. They needed to store up different essentials in case they got stuck in their homes. It is amazing to see how people prepare when they know there is a storm brewing. Credit unions, similarly, are on the verge of a storm. Regulations and lending clubs are the main threats. Lending clubs are using data to steal members from credit unions while regulations are requiring more detailed (and forward looking) reporting. Lending clubs continue to take market share from credit unions without any physical branches. At the same time, the Financial Accounting Standards Board (FASB) has recently introduced a new regulation for calculating ALLL (Allowance for loan and lease losses), current Expected Credit Loss (CECL), and will require credit unions to account for the expected credit losses over the life of every loan. Credit unions must begin building their data reserves to support future decisions needed to serve their members. In order to comply with regulations and implement predictive analytics to stave off competition, credit unions must build a large data reserve.
Analytic Data Model
In order to prepare for regulations and defeat lending club competition, credit unions must begin storing their data. Deciding how to store data is a crucial strategic decision many credit union leaders are overlooking. Simply storing data in an archive is not enough. Data must be effectively integrated across systems to make sense of it at the organizational level. Executives do not want to know what data is in a loan origination system or a CRM database. They want to know what happened across the business over a period of time. Building an analytic data model (ADM) that syncs the business with its data is essential in preparing the data reserve.
Scalable Data Warehouse
Storing data in a scalable data warehouse is another essential step in building your credit union’s data reserve. Data warehouses should be designed to scale with the credit union business model. As source systems are inaugurated and decommissioned, your data warehouse should be able to seamlessly integrate all organizational data no matter what source systems are tapped.
The FASB has almost completed the CECL model for all financial institutions. It will require integrated historical data that many credit unions do not have. The former model used for ALLL accounting looked at “incurred” losses and was backward looking in nature. The new model may require all lenders to account for the risk of every loan throughout its entire lifecycle. A forward looking model will require more history to ensure the predictions of a loan’s future are accurate. This means that predictive analytics will be required for all credit unions when CECL is implemented as the new standard. Gathering data for as many years into history as possible should be a top priority for credit unions in 2016.
CECL is now a prime motivator to collect more data. However, the increased accuracy that more historical data brings to forecasting will allow credit unions to serve their members in a much more effective way. Determining the “next best product” is a great example of how predictive analytics will improve service to members. Credit unions will be able to predict the next need of every member and give forward looking advice through members’ preferred channels of communication. If a member got married in the last couple years, the credit union could offer a mortgage loan. If they recently paid off a car loan, the credit union can market a new vehicle loan. Housing a large data reserve, will give credit unions a boost in their predictive analytics capabilities.
There are other factors affecting every loan throughout the credit union. Monetary policies determined by the federal government, unemployment rates, and other sources of vital data will continue to keep the credit union in control of their destiny by effectively utilizing Big Data. Integrating Big Data from government, economic sources and other 3rd party databases will give credit unions vision to navigate the coming storm.
Credit Unions’ Future
As credit unions continue to hone their data analytics skills and amass a large data reserve, they will be able to continue learning from patterns and trends found in their data. Preparing for changes in regulations and the many opportunities (or threats) credit unions face will be much easier if a robust data reserve is established. Just as Minnesotans stock up on essentials prior to a blizzard, credit unions should see their data in the same light. They need data as their essential stockpile in times of economic uncertainty.
Now is the time to build a data reserve!