The retail banking industry has experienced disruption on a unprecedented scale over the past few years. Apple, Stripe and Square have changed the way we pay for things, while peer-to-peer lenders have opened up new funding avenues for startups and SMEs. Traditional banks do, however, still hold one major advantage over pretenders to their crown - the vast amount of financial information they hold about their millions of customers, and the structure and capital to properly exploit it.
The rise of digital banking has caused exponential growth in the amount of customer data that banks have, with all points of the customer journey now available to them. Analysis of this data, used alongside new technology and in particular mobile, should see them maintain their status as market leaders, particularly when it comes to customer loans, an area that has yet to really have been touched by newcomers to the market.
They can do this by offering products and services tailored to individuals in real time, and providing relevant loan offers when banks predict they might be needed. An Accenture report in 2014 argued that banks should be looking to emulate online retail giant Amazon’s ability to recommend products before customers have even looked for them. This is already happening to a certain degree when you buy products. For example, when you purchase an overseas flight or a car, the bank sends promotional offers of insurance to cover these products. In the future, such applications could be expanded even further. One way this could occur is if you are receiving a large bill for something, the bank could then send you a text message as you get it offering you a loan to cover the cost. An algorithm would calculate what interest rate would be most appropriate based on your historic borrowing patterns and its view of you as a credit risk. The money can then be wired over as payment almost instantaneously were you to accept.
Data will also mean that banks can more accurately gauge the risk of offering a loan to a customer. Predictive analytics models like the FICO scoring system can analyze consumers’ credit history, loan or credit applications, and other data to assess whether the consumer will make their payments on time in the future. This should limit banks’ exposure to risk when it comes to nonpayments.
In general, the capabilities offered by big data should see banks further involve themselves with their customers on a day-to-day basis. Geraldine McBride, a National Australia Bank director, has suggested that ‘banks have the opportunity to become major financial hubs by participating more broadly in their customers' lives.’ Customers have, traditionally, wanted access to their banks when they need it, which is why there is still substantial demand for traditional bank branches. Data can at least help banks go some way to helping banks retain this personalized experience, making the relevant content available to customers in the channel they want at the time they want it. But where does ‘personalization’ cross a line into intrusion? Trust in banks is not exactly high given their historical mis-selling scandals, and offers of products and loans will often likely draw skepticism from those who receive them. When it comes to using personal data, banks have far less leeway than is afforded, say, a retailer or a startup who might employ the same tactics. The implications for data to improve banking - for both bank and customer - are great, but a tightrope needs to be walked in order to make sure that trust is maintained and they don’t appear to be operating in an overly intrusive manner.