Banks have always had huge amounts of data; at major, money-center banks it runs into petabytes, but they have traditionally used only a fraction of it. The definition of big data in finance is continually evolving, but the concept starts with data too big for traditional tools like relational databases.
The leading bank users of big data are employing it to understand each customer as an individual for improved customer service and more effective cross-selling and pricing. Interest rates are a good example. Big data can help a bank see which customers are interest-rate-sensitive to help them retain existing customers and attract new ones without giving away any more than necessary. Big data in finance can also provide insight into how long an individual customer will stay and what their value will be over that time.
Looking to New Sources
Banks need to rely more on data now because as banking becomes more digital, bankers are losing their personal interactions with customers. They will have to rely on data such as product searches on the bank’s website or comments on social media to understand customers and spot opportunities like car loans, mortgages and college savings accounts. Novantas, a consulting firm with experience in banking, reports plenty of gaps and conflicts as banks seek to become more digital, and advises that mastering big data will be essential in helping banks attain that coveted point where a single data management platform can not only compile internal, vendor and web-derived data from customers, but also apply consistent sets of models and rules as well as supply continuously updated output on which to base coordinated research.
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