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Big Data and Credit Unions: Machine Learning in Member Transactions

Big Data and Credit Unions: Machine Learning in Member Transactions

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 while individual credit unions may not have the resources of a corporate giant, advances in data storage and software tools mean that credit unions can start using similar tools and deriving similar value.

For this research, five credit unions in the United States 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, we found that members follow simple paths during their life cycle and adopt different consumer products at each stage.

The credit unions were each looking for different things, and the plasticity of big data means that, with the right tools and the right inputs, you can discover very different things. Because a machine learning project is only as helpful as the data that flow into it, the participating credit unions got the most specific insights. But their combined data still offer generalizable findings for all credit unions. You'll want to watch this trend: Big data is here to stay.

Categorized: 'Strategy'

Tagged: 'big data' 'consumer behavior and market research' 'member transaction data' 'data analysis' 'banking technology'

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