By now, credit unions are aware of the industry’s changing landscape. Credit Unions are facing Fintech influence, industry disruption, and realize it is no longer an option, but a necessity to capture and optimize every piece of obtainable data to remain competitive in financial services. Large banks have been investing heavily in big data and continue to do so. Unfortunately, these banks are directly competing with credit unions, which don’t have the same resources available to effectively invest in big data. Awareness is step one, but one state is taking action to put the power of big data in the hands of credit unions.
As the article “Keys To Making Big Data Affordable” on CUtoday states, “The only way small credit unions can afford Big Data is through collaboration… which has a new offering to help not-so-big cooperatives dig much deeper into their member information.” By leveraging the collaborative essence of the community, credit unions have the opportunity to share the high costs of Big Data tools and scientists, while simultaneously offering more available data and more precise analytics through the utilization of industry data pools.
The Minnesota Credit Union Network (MnCUN) has formed a partnership to white-label the M360 Enterprise solution created by OnApproach, which provides an agnostic platform to pull data from all of a credit union’s disparate data sources. As stated in the article, for a credit union to take full advantage of their data, it would typically require paying large sums for SQL programmers, data scientists, licensing fees, and data analytics hardware and software. John Ferstl, VP of Network Services at MnCUN, stated that such an investment generally means $2-3 million and several years of work, but “Going with the league’s solution will be a small fraction of that cost.”
In addition to cost-savings, credit unions have a lot to gain from aggregate data collected from other credit unions. The data pool contains not only data on recent transactions, but credit unions will benefit from historic data. “For example, a small CU that does not have enough loss history to build their own appropriate ALM model, but if I can take data from a large number of similar-size, similar-portfolio credit unions in Minnesota and go back seven years, we can build an ALM model that is appropriate for their credit union”, stated Ferstl. Data pools allow credit unions to benefit from data other organizations have collected, which improves analytics and leads to more reliable solutions.
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