Thinking differently about data: the new opportunity for banks

By Eli Rosner, chief product and technology officer, Finastra

September 3, 2018 | Finastra

Often described as the ‘new oil’ or even the currency of the digital economy, data is key to the business strategy of every organization. For banks, building an understanding of how to harness the variety, velocity and volumes of data available to them will dictate the difference between success and failure in the very near future.

To understand the forces at work here, it helps to paint a picture of why and how data is front and center of business planning. One reason is the absolute magnitude of data available: it’s said that 90% of all data that exists today was generated in the last two years. Data is created by social media, in the cloud, by IoT devices, on increasingly open and hyper-connected IT systems and is accessible via high performance network bandwidths.

All of this has opened the door to new ways of managing and using data, from virtual and augmented reality, Natural Language Processing (NLP), Autonomous driving, to machine learning and artificial intelligence.

Of course, data has always been valuable but its sheer volume, together with almost limitless storage and superfast processing power, means the way organizations can harness and analyze data has changed. A new profession has come to the fore over the past few years, whereby data scientists are able to look for patterns in data sets, rather than providing standard reports created, for example, by business analysts.

The current transformation from Science, Technology, Engineering, Mathematics (STEM) to Science, Technology, Engineering, Arts, Mathematics (STEAM) is another manifestation of how data analysis has changed. Data scientists look beyond stark numbers to include insights relating to anthropology or social history, creating a more holistic view of customer behavior and their propensity to make financial, spending and other decisions in the future.

There are now different maturity levels when using data to understand evolving customer motivations:

  • Level one – What happened?
  • Level two – Why did it happen?
  • Level three – What’s going to happen?
  • Level four – How can I make it happen?

Only a few organizations are at this fourth level of maturity, where data can augment human intelligence to encourage customers to make the decision like buying a product. Buy a new bike from Amazon and there’s a big chance you’ll be offered a safety helmet or other accessories as well, for example.

Banks are no different to any other organization that deals with consumers and businesses in a regulated industry sector across international borders. However, they are unique in the levels of trust that customers have in banks’ ability to look after their money.

The downside to this position is that consumers and businesses don’t think of their banks as being particularly innovative. The days of customers visiting a bank branch just to draw out money are (more or less) over. There’s an increasing number of people who choose not to deal with cash at all, plus a new fleet of Fintechs that can provide a more engaging experience.

So while trust exists, customer loyalty is fading and banks need to recognize that they can’t survive by operating as they used to 15 years ago. Circumstances have changed forever, but banks’ ability to operate within a global ecosystem – while remaining compliant with stringent regulation and maintaining high levels of privacy and security – is more important than ever.

How can banks leverage the data revolution to transform the service they provide to customers? How can they ensure that data is the enabler of brand new conversations with customers that build loyalty, improve retention and attract new business?

The answer lies in banks’ ability to integrate different open data sets to create a unique proposition for individual customers. Data can be derived from internal information, but then blended with other data feeds such as social, weather, local events and traffic reports to create genuinely useful insights.

Take the example of a bank that can see from its own records that a customer’s daughter has a university graduation coming up in the next few weeks. The bank can also see that the customer tends to use a specific card more than other accounts, avoiding interest by paying off the balance each month.

From analysis of public event and traffic data, the bank can forecast that weather conditions will be hot and busy on the roads on the day of the party, so can advise the customer that they might be better off hiring a venue across town. They can also advise that the customer could take advantage of a partnership the bank has with a retailer supplying  soft drinks, together with another it has with a bakery that could provide a fantastic celebratory cake.

The discussion with the customer at the branch changes and the bank becomes a source of information and advice that is hugely valued by the customer. This is especially true when it provides a great offer on an interest free credit card to pay for everything and consolidate multiple balances.

A second example is a cereal crop farmer. If their bank integrates customer data with readily available forecast information, it could predict how well the farmer will do with the next harvest – including the fact that he or she is likely to need a new combine harvester (based on an increase in repair bills), then offering a special facility loan. This would come with a discount on farm supplies, or other tailored offers for relevant products such as weed killers.

While banks will need to be prepared to counter some consumers’ and businesses’ potential concerns about ‘Big Brother Syndrome’ through education and information, the likelihood of customers being both surprised and delighted by such insights will surely grow over time.

Banks will also need the tools and platforms to enable data scientists to navigate their way through huge volumes and varieties of information being gathered at ever higher velocity. Software developers are already producing plug-in apps for Finastra’s platform that enable banks to build accurate profiles of individual customers, including their needs, values and family circumstances, for example.

To really take advantage of the limitless possibilities that data analysis and data science provide, banks have to start thinking differently about their role in customers’ lives. Instead of simply providing banking services and secure places to keep money, in itself a rapidly commoditizing section of the financial services sector, banks must reinvent themselves to be trusted advisors with authentic insights that can only be constructed using powerful technology to interrogate multiple data sources.



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