Big data may be one of the hottest topics in corporate circles, but many companies find it challenging to efficiently corral and capitalise on the vast quantities of information being generated. One exception is financial service firms who are increasingly using the information to sharpen their competitive edge and restore profitability in the fallout of the financial crisis.
This trend has been well documented in a crop of studies, including a report conducted last May by the IBM Institute for Business Value in conjunction with the Saïd Business School at the University of Oxford, Analytics: The real-world use of big data in financial services. It found that 71 per cent of the 124 financial services companies polled believed that the use of big data and analytics could create a competitive advantage. This was up from the 36 per cent two years ago and slightly higher than the 63 per cent of cross-industry participants. Overall, 1,144 business and IT professionals in 95 countries were canvassed.
Customer-centric outcomes were also a priority for 55 per cent followed by risk/financial management (23 per cent) and new business models (23 per cent). It noted that almost 75 per cent of the financial service firms had either started developing a big data strategy or implementing big data as pilots or into process, on par with their cross-industry peers.
A separate report published last September by US-based consultancy NewVantage Partners, Big Data Executive Survey 2013: The State of Big Data in the Large Corporate World, showed that financial service firms were at the forefront. It revealed that around 75 per cent were spending more than $1 million on big data with 94 per cent planning to do so by 2016, compared to 44 per cent and 67 per cent for health and life science firms, respectively. It polled 90 executives representing more than 50 organisations, of which around 35 hailed from the financial services arena.
Lenders are able to enhance their credit-rating processes with social and behavioural data sourced from social media
“After the financial crisis in 2008, banks and asset management firms were looking at big data to meet regulatory and compliance requirements,” says Randy Bean, co-founder of NewVantage. “That has changed and over the past year, they have begun to not just use the data for defensive reasons, but also offensively in terms of identifying new products and services for customers. It also allows companies to reach decisions and test out new products in a faster and more efficient way. For example, in the past it might have taken a firm $1 million to $1.5 million and several months to test and validate a new product. Today, it could take 30 to 60 days and cost around $500,000.”
Uwe Neumann, technology and telecom analyst at Credit Suisse, adds: “The key advantages of big data are that it helps a bank to better understand its clients, improves decision-making and strengthens the competitive position. A bank is able to develop solutions and more-tailored products for a person’s specific budget.”
Lenders are also able to enhance their credit-rating processes with social and behavioural data sourced from social media, according to according to Christine Schmid-Frey, bank analyst at Credit Suisse. Together with standard financial data, this big data would provide a more complete picture of the prospective borrower and allow the bank to more accurately judge his or her risk profile.
To date, US banks, asset managers and insurance companies are ahead of their UK and European peers. One reason is the difference in data protection laws between the two regions, according to Bernd Richter, partner at consultancy Capco. “The rules are not only stricter, but they are also not harmonised. For example, the European Union, through its Digital Agenda for Europe, drives all members towards a ‘digital single market’ with a unified data protection law, while each EU country today has its own set of regulations. It is much easier in the US which has one market and set of laws.”
Creating a strategy though is challenging regardless of location. “The questions being asked are how does big data complement and fit within an organisation’s existing information architecture, and what governance and new roles should be created,” says Stephen Mills, big data analytics strategy lead at IBM UK & Ireland. “Although each organisation will have their own approach to big data analytics, it is important to have someone at the top of the organisation to champion a data driven approach to change.”
This is why the role of chief data officer has been gaining in stature. Citi was a pioneer, having created the position in 2006, but others, including Bank of America and Visa, have more recently followed. Although the job continues to evolve, the main thrust is to develop, drive and implement an enterprise-wide, unified data strategy. Others, such as Wells Fargo and State Street, are establishing separate divisions. For example, last year the latter launched the State Street Global Exchange, a big data division devoted to portfolio modelling, investment analytics, data management and data projections. The aim is to better analyse client data, detect risks and monitor the efficiency of portfolios.
Wells Fargo, on other hand, set up its own enterprise big data lab to better detect fraud, but also to more accurately pinpoint each customer’s needs and interests. “We wanted to understand the activity across the different channels of the bank – ATM, internet, voice or mobile – to identify patterns of behaviour,” says John Ahrendt, senior vice president, enterprise data and analytics at Wells Fargo. “This enables us to provide a better customer experience and answer questions more quickly than in the past. However, it is not a silver bullet, but a complementary analytical tool to our other data warehouses.”
European banks, albeit lagging their US counterparts, are also restructuring. HSBC, for example, is in the middle of building a maturity model. According to Alasdair Anderson, global head of architecture, HSBC Securities Services: “Initially HSBC asked its internal people to investigate the possible business benefits of big data. We then brought in some external experts to put together a high-level strategy that would allow us to execute on the high-value business uses that we had identified. This has taken us into a build phase where we are currently cross-training our staff and hiring new talent to allow us to execute on our planned implementations.”
