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Big Data and CUs: Machine Learning in Member Transactions
Friday, July 19, 2013 6:55 AM

Companies as varied as Amazon, Google, Walmart, and Wells Fargo are turning to “big data” for customer insights that will help them serve clients and capture market share. Big data is the analysis of huge data sets, and the latest report from the Filene Research Institute reveals how credit unions can mine members’ transactional data to predict member behavior and product life cycles, improve profitability and reduce risk.

The report is written by Philipp Kallerhoff, PhD, cofounder of Jumiya, a wellness platform that rewards active and healthy lifestyles with access to financial loans and better interest rates. Five credit unions in the U.S. and Canada proffered their members’ anonymous profile information and transaction details to the researcher, who used variables as diverse as gender, product balances, credit score, income, and transaction amounts to search for revealing correlations. The findings show that some simple patterns evolve using big data and machine learning (a branch of artificial intelligence that focuses on the construction and study of systems that can learn from data). In particular, the report found that members follow simple paths during their life cycle and adopt different consumer products at each stage.

Click here to read the full report.