Big data has been causing quite a stir, and most of us have heard of it being used in the context of banking and financial services. But why is it such a big deal? Are the supposed opportunities really there?
The Forrester report titled "Big Data Adoption Trends In Asia Pacific: 2013 To 2014” noted that some markets and industry sectors in Asia Pacific (APAC) were early adopters of big data initiatives and that interest is strong across almost all sub-regions and verticals. This interest and adoption of big data in APAC is being driven by the general, cross-industry perceptions of the value of data in addressing customer demands and expectations and the urgent need to respond to changing market conditions while increasing operational efficiency.
A report from McKinsey says large-scale data gathering and analytics are becoming the “new frontier of competitive differentiation”. This made me curious to find out more about how the financial services industry is using Big Data.
It’s clear that the main reason why banks are busy working out ways to manage unstructured data (social and mobile data) together with transactional data, is to improve the customer experience, become more competitive and, obviously, to drive growth.
Banks face numerous challenges including customer retention, cross-selling, up-selling, developing new products that their customers actually want, as well as fraud prevention and cybercrime.
But can banks use social media to find out what their customers really think, above and beyond the ‘ticks in boxes’ of the usual customer satisfaction surveys? Any opportunity to gain better insight into what banking customers are really thinking is hugely valuable. If big data can help deliver a better customer experience through a more personal, perhaps less formulaic approach, then banks are surely on to a winning formula? Social media is already being used to find out what customers think of competitors and their products.
The other way that banks are already using big data to good effect is in identifying and preventing fraud. The Bank of England has insisted that Britain’s financial institutions put concrete plans in place as soon as possible to deal with the growing threat of cyber-attacks. Of course, banks are already highly adept at this and most carry out real-time detection already. But it’s when Big Data is combined with social data that the real insights can be found.
How do we stop Big Data becoming Big Bad Data?
One of the challenges is gaining permission to use the new data sets. It’s paramount that adequate controls are in place, because the further away from source the data gets, the harder it is to ensure compliance is maintained.
The Finance industry is already weighty with compliance issues, but perhaps something could be learned from some of the new technologies and techniques that companies like Google, Netflix and Amazon have developed.
Another initiative is to combine the expertise of academic researchers, industry and government to come up with a multi-disciplinary approach to Big Data solutions such as the MIT Big Data initiative. Launched in May 2012, its goal is to identify and develop new technologies to solve next generation data challenges. Initiatives such as this may hold the secrets of success regarding the mining of big data.
For financial institutions, big data is seen as a strategic imperative for dealing with the acute stress of renewed economic uncertainty, increasing regulation, proposed banking reforms and the legacy of customer mistrust following mis-selling and the credit crisis of 2008.
Data is absolutely necessary to provide the evidence to support new customer engagement initiatives. Converting big data programmes into successful activities that deliver meaningful business insight and provide sustained high-quality customer relationships can be costly, risky and sometimes fruitless. Location data provides much needed spatial context for many types of service. For example, linking insurance claims data to locations allows insurers to determine risks associated with particular geographies and postcodes-essential information for accurate product pricing.
According to recent , more than 75 per cent of senior executives from over 500 companies say that they are wasting more than half the data they already hold. Buying in great swathes of new technology and importing new data will improve inputs rather than outputs, while services provided to customers will remain utilitarian and transactional in nature. Rather than collecting more data, and spending more time and money managing it, firms must use their existing enterprise date in combination with other sources of highly targeted data in a more intelligent way.
About the author
Harry Singh is Vice President, Global Banking and Financial Markets, AMEA at BT Global Services.