Whether its analysing credit profiles in the personal banking arena or deconstructing complex investment portfolios for large institutions, the importance of handling “big data” well suddenly increased in the wake of the credit crunch.
Financial organisations learnt to their cost that they had not fully understood the assets they had purchased and also misread the credit profiles of their customers. It wasn’t that they didn’t have the data; it was more that they had failed to correctly deconstruct it to avoid the calamitous write-downs which followed.
On the heels of a clutch of bad mortgage loans, banks have since been sharpening up on who they lend to and the ability to correctly understand their customers has become vital.
Marc Gaudart, global head of consulting at credit referencing group Experian, says there has been a huge rise in investment in big data handling techniques by the country’s banks so that they are able to build decision tools around unique customer profiles.
“It means understanding all their outgoings and their income,” he says. “Things like rental information and [information from] utility companies to get a broader understanding of the P&L [profit and loss] of the customer.”
We’re in a phase of history when technology is often leading inspiration
Mr Gaudart says Experian is also investing in new decisioning software to go beyond standard credit scoring strategies.
The heavy financial loss, shouldered by many financial services firms after the global credit crisis, has triggered a change in attitude towards big data and how it can increase maximum investment return while protecting against risk.
Many banks, particularly those in the United States and UK, have now realised that using data sets from outside the organisation can further enhance their decision-making processes.
Mark Horta, principal for SunGard Consulting Services, says it is key for financial services to begin importing data from external sources and correlate this with internal data sources to extract better insights about the market and customers.
“Big data technology enables distributed processing for large data sets across a cluster of commodity computers using a simple programming model,” he says. “It is designed to scale up from single servers to thousands of machines, each offering local computation and storage that is highly reliable, scalable and fault-tolerant.
“The ability to store, manipulate and operate on massive amounts of data provides the opportunity for better analysis and better reporting.”
From the customer’s perspective, the banks are still facing a huge reputational challenge. Opinion polls conducted on behalf of the financial services industry repeatedly show trust in banks at an all-time low.
With this in mind, any kind of data sharing strategy which requires customer buy-in will need a clearly-defined communications strategy to inform customers of the benefits of sharing additional external data with banks.
Adam Fulford, strategy and planning director at Rufus Leonard, cites Google as a great example. He says Google has built an extremely successful business model that is both dependent upon and exists to serve human interaction with data.
“Predictive search and ‘did you mean’ simultaneously make it easier for consumers to find what they’re looking for and for Google to monetise the keywords by reducing variety,” he says.
“True innovation is a business model that, to at least some degree, reduces conflict between consumption and monetisation. It just so happens we’re in a phase of history when technology is often leading inspiration.”
Mr Fulford explains that this joined-up approach to consumption and monetisation could potentially give three benefits: building trust, creating an equitable exchange and delivering added marketing through word of mouth.
“Consumers [need to] see what’s in it for them, whether that be a combination of cash back and offers as in totem loyalty schemes, such as Nectar, added services bolted on to accounts and cards, or in lower fees or APRs,” he says.
“Getting trust in a smaller group of people specially selected to help develop the scheme will be easier than trying to convince the nation. Their experience and confidence will act as a bridgehead to wider trust in the scheme.”
Essentially, the financial services community can learn a lot from searching beyond the traditional methods of the past. Greater integration of data storage, decisioning and marketing will ultimately prove to be key.
With the UK government keen to expand competition in the banking sector and new market entrants, such as Metro Bank, Virgin Money and Shawbrook, promising to do things in a different way, it is possible that this innovation may not come from the financial behemoths of the past.
Whether its analysing credit profiles in the personal banking arena or deconstructing complex investment portfolios for large institutions, the importance of handling “big data” well suddenly increased in the wake of the credit crunch.
