In my last post I talked about the possibilities of big data. In this post I will look more at the technologies enabling this change, and how they can benefit banking.

Just like any other industry providing service to customers, retail banking has always aimed to increase its insight and responsiveness. Data analysis has been a key part of this. However, customer data was often stored in silos, making analysis time-consuming and incomplete. It is only now that truly agile and comprehensive big data solutions are becoming possible.

One of the incoming enablers is in-memory technology. Rather than storing customer data in hard drives searched by queries, in-memory technology can keep billions of transaction records in local memory, making it immediately accessible to fast searches by powerful processors. This makes real-time analysis a real possibility.

On a macroeconomic level, the ability to see shifts in the banking climate might warn us of the kind of extreme weather we have seen in recent banking crises. But what does it mean for individual customers?

Making Data Social

The two most visible impacts of real-time analytics will be the way banks can understand customers, and how they can communicate with them. Within seconds of the end of a transaction, a bank would be able to review their customer’s altered circumstances, compare it to similar changes across their customer base and respond immediately.

This could change communication between banks and customers for the better – allowing instantaneous responses to changing circumstances. Social media is a listening tool as much as a broadcast tool for businesses. In the same way, real-time analysis provides a conversation with customers: every transaction is like a Facebook status update.

Better transparency and understanding also benefits risk management, a vital part of any retail bank’s skill set. When a customer applies for a loan, for example, the terms can be matched against repayment histories from the entire customer base. Then, loans can be approved and rates set which more accurately reflect the potential risk.

Dangerous or complicated applications or behaviours can be flagged automatically. Good customers – who represent good risks – can be rewarded with improved rates and offers tailored to make a real difference to their banking, and to their relationship with their bank.

What would it be useful for bank employees to know immediately about their customers? What sort of tailored services could be offered? And how would you prepare to be able to see every relevant aspect of a customer’s data patterns, and to create new offers and services at less than a moment’s notice?