How Big Data brings back the branch manager complete with virtual bowler hat
In the banking world we have long believed that Know Your Customer (KYC) is one of the key pillars of a secure banking infrastructure. We also call the consumer-facing part of our industry ‘retail banking’ yet as an industry banks focus strongly on the ‘banking’ and often ignore the retail part. Indeed, seldom does a bank move past the ‘know’ into the ‘understand’ which is a key component of the sales approach of most modern retailers.
Big Data is essentially all of the information that an organisation holds on their clients, often in multiple diverse systems, that can give a real indication of their lifestyle, habits, wants, needs – and most importantly how a retailer can meet them. Take for instance the international e-tailer Amazon. After my first visit to Amazon, the point where I share similar KYC data as I do with my bank, the complex data systems behind immediately begin to learn about me – what have I searched for, what did I end up buying, how did I pay, where was it shipped. Equipped with this information Amazon can now begin to entice me with interesting products when I next log on to their system – say for instance that I bought a Kindle eBook Reader and also the latest Harry Potter movie, Amazon would make the inference that I like eBooks and Harry Potter, so guess what it would recommend on my next visit? No prizes if you concluded that the system recommended that I might like to buy a Harry Potter eBook! But more than this, every interaction that I have with Amazon is logged and used to build a bigger, better picture of me and how I browse and buy.
Contrast this to when I log into my eBanking system. Well, I can see my accounts. Oh, and I can access services if I feel that I might like them but all of the data is static. Some eBanking will let me apply for a loan or even directly request new products, but mostly eBanking is seen as an information channel and not a sales channel. Portals were cool in the ‘90s, but the dotcom bubble put paid to most of those projects. Likewise, when I go into my branch, there are many posters indicating the availability of products but the person that takes my deposit isn’t informed by the system that I’m trying to ensure that I have cash in the account on time to avoid a rejected direct debit – and certainly not that a good thing to recommend could be a small overdraft or more flexible account. The failure to sell to my needs is further compounded by the fact that banks are masters at keeping data within each individual siloed department. The department that looks after my credit card has no clue what’s going on with my mortgage unless applying for one is blocked by issues with the other. Likewise, the current account team have no clue what is going on with my securities portfolio and investments. Given that all the clues are actually in my current account, it is this fact that is actually the most surprising one. As Big Data aficionado Chris Skinner suggested in his Financial Services Club blog, “If Amazon and Apple were run like a bank…they would separate customer data. They would organise themselves so that they have a book division, a music division, a film division and more, all competing against each other for share of customer wallet.”
There are many reasons why banks don’t utilise their Big Data, some structural, some historical, some legislative and some due to a lack of investment in the customer. Typically we see exactly the same issues when it comes to other banking infrastructure, such as payment hubs, where the disparate siloes of a bank are blinkered to the rest of the organisation and run purely to service the needs of their own product or segment. A good example of this is is usually the cards department of a bank – debit cards and credit cards are invariably run on completely separate systems, each with their own Card Management System (CMS), each team negotiating separately with the card scheme, often managing separate personalisation bureaus and only ever meeting on the screen of the branch system.
From a technology perspective this siloed approach is slowly changing. In the past few years there has been a strong emergence of the shared service centre (SSC) concept and many new entities dubbed ‘Knowledge Centre’ or ‘Competence Centre’. While this is certainly positive on a technology efficiency and infrastructure renewal front, to date I’m yet to visit a bank that has a shared customer lifestyle centre. No single entity within a bank will typically have ownership of the customer relationship any more. We used to have a form of Big Data, back in the day, when the local branch manager knew all of his (seldom ‘hers’ sadly) clients personally and managed his product offer to ensure branch profitability alongside happy, loyal customers – in the corporate megabank environment this specific approach is no longer possible, but why were the lessons never carried forwards? Retail banks really need to get back to the basics of ensuring happy, loyal customers – and the only way that they can do this is through using Big Data. Specialist companies that can support banks in tapping the potential of their Big Data, such as dunnhumby, Enqio and IBM, have been active for a number of years, primarily working with retailers and utilities, but more and more within the financial sector.
So what would a bank using Big Data look like from the outside? There are already small glimpses of this in the market today. BBVA began to look at Big Data in their ‘Future of Self Service Banking’ project, which culminated in a fantastic new interactive ATM that would look more at home in the Apple store than in a bank branch. This ATM looked at presenting the most frequently used operations by a particular user in the main part of the screen meaning that if I was using the ATM regularly on a Friday night to withdraw 100 euro, the first option I’d be presented with on Friday night would be ‘Withdraw 100 euro?’ which would be exceptionally convenient. Perhaps knowing how clumsy I am (from analysing data on previous insurance claims!) the screen might also have a second button that suggested a mobile phone insurance policy. How did they know I had a mobile phone? Simple, each month there’s a direct debit from my current account to the Telco which was at the level likely to suggest that I have a smartphone. Product suggestions are a simple example of cross-selling, but tackling Big Data offers more than that, it offers lifestyle analytics that allow smart recommendations rather than dumb advertising through the different interaction channels.
Join this internal information up with information gleaned from the public elements of my social networking presence and banks can move beyond merely promoting selling of financial products or savings. Perhaps they could promote discounts on travel because they see I take one city-trip each month, or link my card spending to other loyalty schemes such as my airline points? Maybe they see that I spend 20% of my net salary on food and another 40% on rent and utilities, and benchmark me against people of my age, profession and region. They can then offer tips for reducing my fixed expenses leaving more free cash for financial products. Using Big Data allows a bank to offer products and services that are the right fit – and at the right time. If the products offered match these two criteria then my likelihood of taking the product is higher, meaning my loyalty towards the bank increases and the amount of information I share with them increased. Big Data is consequently self-improving and one of the biggest assets that a modern bank has, but in terms of customer privacy will generate new issues in its own right – where I spend my money is of my concern and what happens in Vegas should stay in Vegas.
Getting the most from Big Data is no easy task and requires a huge shift in an organisation, rebuilding from the customer up. Principle Analyst at O’Reilly Radar, Edd Dumbill sums it up best, “The phenomenon of Big Data is closely tied to the emergence of data science, a discipline that combines math, programming and scientific instinct. Benefiting from Big Data means investing in teams with this skillset, and surrounding them with an organizational willingness to understand and use data for advantage.” The transformation might be huge, but proving to customers that you are interested in more than just their monthly salary could change their perception and begin to rebuild trust in the banking system.
This article was first published in Banking Technology SIBOS supplement 2012 (here)
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