In the information age, banks have more data to cope with than ever before. But rather than prove a burden, this can be an opportunity to gain a genuine competitive advantage. John Morton, chief technology officer for the UK and Ireland with business analytics and intelligence provider SAS explains how banks can make the most of the mass of information available to them.
Q. Big data is a phrase which has been a major talking point in 2012, what does it mean for the financial world?
A. Big data covers a range of different aspects, but what the banking community seems to be interested in is its value. Recent reports have shown that just 5% to 10% of data is actually used by organisations. The rest is untapped, even though it contains a wealth of knowledge and information that could help drive business efficiency, innovation or even new market places. Today, businesses are using their structured data, but they are not using some other information, such as unstructured data.
There is another aspect to big data too, and that is how society is changing, how people are interacting with modern connectivity, apps, web interfaces and social media. All of this information is available for banks to understand what people are thinking about them, what services they are using and not using, what they like and what they dislike. That information could be used to influence new products and services that banks could offer to their customers. Big data is about pooling all of this information together and using it to help drive efficiencies through use of high-performance analytics.
Q. How can the banking sector go about achieving this?
A. Recently, the Centre for Economic and Business Research [CEBR] took information from the UK Office of National Statistics and developed an economic model that actually quantifies the value of data in the UK, and how it can be exploited within organisations and businesses. The cumulative economic benefit to the UK economy could be £216bn [$342bn] over the next five years, with the creation of 58,000 new jobs.
For financial services, the key areas are around retail banking and capital investments. Their key finding is that there are three areas which are going to drive gross domestic product growth in the UK over the next five years: business efficiencies, creating new products and services within the banking sector, and creating new business by exploiting available data.
Q. Do you expect financial services firms to drive adoption here?
A. Yes, we do. Currently, it has seen an adoption rate of about 39%, which is very good compared to all the other industry sectors. But over the next five years, the CEBR report identifies that that should grow to about 54% or 55%, in particular around those three key areas.
We are taking, for example, 8 billion rows of information with complex variations which might have previously taken 12 or 24 hours to run, and then providing that information in 10 or 12 minutes
Financial services have a unique leadership in using data to drive certain operations. Risk management is one and fraud is another where they have shown tremendous leadership. However, banks are not the leaders in all areas of using data. One of the areas that other sectors are leading on is customer profiling, and using customer information to innovate new products and services targeted to specific groups of individuals. That is quite surprising because banks have a huge wealth of knowledge about customer interactions and how they use money that could help drive some of those new services and capabilities.
Q. SAS has estimated that the investment banking sector could save as much as £5bn making use of big data. Where might this be particularly useful for capital markets operations?
A. Some of the key areas are around business efficiencies – the better use of investment capital and operational working capital, improving risk management, as well as looking at the fraud capabilities. Another area is growth from using data to drive more customer intelligence, and focusing on new services and products as well as trying to understand where other banks are better or differentiate themselves.
Q. Real-time risk management has been a prominent topic recently. How can data help banks improve their systems?
A. I think the best way to answer that is to give some examples. One of our banks looked at their whole loan application process – which took more than two weeks initially – and tried to cut that so that loan applications were agreed at the time of interacting with the customer. That is a worthy goal, especially since the bank's process had to go off and run mathematical models and check profiles and so on and so forth.
The bank brought all of that together so that as somebody is on the internet or you are dealing with somebody within a contact centre, it can go through all the processes and agree that loan as long as the customer’s profile is acceptable to the bank. Even if it is not acceptable, it can then have the opportunity to talk to that customer and put them into a different channel to retain them and give them the right type of service. In this modern world, if you find that if you are not getting that loan application in one place, you are going to go somewhere else.
Another challenge real-time risk management can help with is looking at and surveying what is happening within your own operations. If you are waiting until the end of the day to make sure that things have been done correctly, then you are introducing risk within your operations. With real-time risk analysis, and the ability to get down to and aggregate the finest detail in real time or near real time, you can look at what is happening on a trading floor or what traders are doing intraday or at different points within the day, and survey that, improving things and assuring that regulation is being adhered to.
Q. How are financial services firms making use of data analytics?
A. Today, most banks are using analytics very much on a departmental or a business unit basis, such as in their credit risk, market risk or anti-money laundering capabilities. What we are seeing really drive the market is including some of the other information that’s available, both from what is happening on the internet and social media channels as well as other information within the bank. This helps determine what can be done, and takes a more holistic view across the bank environment. That is where the exploitation of big data is really going to drive the greatest amount of demand.
Q. So what kind of bottom line benefits does that have?
A. The real benefit, from our perspective, of using these analytics, is understanding customers. Banks will not only be able to see and understand data from their own environment, but they will also be able to start sensing and hearing what is happening around them, and so bridge the gap between actuality and perception. This will help with decision making, and in understanding and responding to what is happening in the market place.
Q. How does that play into what SAS is doing in the market at the moment?
A. SAS is working with a number of banks and investment banks today around three key areas which we call our high-performance analytics capabilities. There, we are allowing banks to take existing SAS code, and run it much faster and closer to data, allowing faster response on information. We are also providing better visualisation of information, which can, for example, be displayed on handheld devices, allowing information to get to the key decision makers faster in a way that they can easily interpret.
Lastly, we have included some new analytics capabilities that allow better and faster analysis of information in more complex environments. We are taking, for example, 8 billion rows of information with complex variations which might have previously taken 12 or 24 hours to run, and then providing that information in 10 or 12 minutes and allowing data analysts to really look at that information and drill into it.
Q. What might get in the way of banks making the most of these opportunities?
A. From the experiences that we have been engaged in, and some of the soundings that were taken from around the world, there seems to be two areas that are really inhibiting the capability to deliver big data. Changing the organisation to be more responsive, and getting people with the right type of skills and capabilities to do all of this work. That may require a different mind-set.
