Blog 2 of Transformational Banking series  . . . .

As everyone in the banking industry is well aware, banks hold a lot of data and many have spent several years utilising it. Some banks I know have been mining, analysing and really making their data work for them for over 25 years…but there aren’t as many banks like this.

Banking data has been used broadly: sales targeting, fraud, credit scoring, retention etc. And now in the era of “Big Data” more banking data is being collected, especially through online and mobile channels. All good? Yes, and here comes the “but”; it’s all “banking data”. Some may argue that clickstream data is not banking, but it is if the clicks are on bank pages, whether it’s internet banking or the banks web page.

Initiatives like PFM (Personal Finance Management), whilst useful, have further legitimised the collection of financial data only. However, for transformational digital banking, banks have to be more voracious about collecting data and more creative in its use.

For example a bank’s typical approach to credit scoring involves financial analysis of the customer’s income, outgoings and payments history. This approach assumes you need to check financially a person’s ability to pay. Companies like FriendlyScore and Veridu turn this model on its head and use social media data to validate a person’s identity and trustworthiness to pay. Similarly, last year China launched an initiative which will be rolled out nationwide by 2020 to create a “Social Credit” system. Initially, 8 companies have been invited to define scoring approaches, and these vary from analysing online spend (Allibaba/Sesame) to scoring on online dating (Baihe).

Imagine how much more customer service can be improved by understanding the customer’s emotional state when they are contacting you. Companies like Affectiva.com are leading the way in providing emotional detection and analytics. Similarly, several years ago Samsung demoed a prototype phone with in-built emotional detection that worked with several sensors. Their analytics worked on things such as the speed of typing, errors made, pressure and vibration. Microsoft have also demoed “mood sensing” couches and even a “mood” bra.

Some banks have investigated the use of geo-location, for example to highlight the nearest ATM or branch. Some have gone further with geo-fencing, using “beacons” to present offers in real time, or to change electronic billboards as customers walk by locations pinpointed to 5m2.  But how about using Google image search to help you identify where a picture was taken. How could this be useful to a bank? If you could identify the location, you may understand the kind of holidays the customer takes, providing you with an idea of their lifestyle. Customers that use sites like Instagram will also give away how frequently they go on holiday.

The sources and use of data that banks can access are clearly vast, and with the Internet of Things the growth of data is about to explode further still. It will soon be possible to record a person’s entire life: what they saw, what they ate, where they went, how they felt, what they like/dislike, their heart rate, how often they brush their teeth, even how often they wear the same socks before they are washed, and more.

The key to using big data to transform a digital bank will be to gain the customer’s trust, giving them reason to volunteer the data to you, and this will happen more easily if the customer sees value for themselves in the way you use data. For example, being able to extend a credit facility instantly and easily whilst out shopping, getting discounts on things the customer likes, or even just helping them to manage the privacy of their data online.

For some time, one of my favourite sites (I wish a bank would do this for the UK) has been http://peoplelikeu.com.au/ launched by UBank, which allows you to compare how you spend your money with people similar to you (by age, earning, location, marital status etc). It recognises that either consciously or sub consciously we make comparisons and decisions based on other people. This site can be used by anyone, not just bank customers.

Going back to China’s social credit system. Some of the feedback from users was that they were happy to give up their data as it simplified processes; for example they could make a hotel booking without having to pay a deposit. Also, as less than half the people in China have a financial credit history, something that works on data broader than financials will also allow people access to credit.

It is clear there is a huge amount of data available and that with the right value for the customer in providing it, they will volunteer data to you. Even regulators with initiatives like PSD2 are pushing for data to become more openly available with the aiming of improving service and products for customers.

To drive digital transformation it time for banks to think broader thank bank data and really get creative about big data, before somebody else does!