Analytics and big data within banking – what are the opportunities?

By John Glendenning | 22 September 2014

There has been a huge amount of hype around big data over the past two years. However, this approach to dealing with data can also provide tremendous opportunities as well. In 2013, Gartner estimated that 64 per cent of organisations planned to invest in big data over the following twelve months. The finance and banking sector is leading the charge to deploy new technologies that would take advantage of the huge volumes of data that are being created today.

In particular, banks are looking at how they can make more use of data closer to where they are making decisions. Should someone be able to carry out that transaction? What impact does a transaction have on the bank’s liquidity? What does the big picture look like for the bank in the market?

From these questions, IT teams within banks are considering how they can be more innovative in their approach to delivering services to their customers, whether this is in the retail or investment banking sectors. The discussions that are taking place here are around how new technologies can be employed to support competitive advantage in the market through use of big data.

There are currently a number of areas where banks are looking to make use of real-time data for their operations:

  • Pre- and post-trade risk analytics
  • Compliance regulation and financial liquidity analytics
  • Fraud analytics
  • New online services for retail banking customers

Each of these use cases represents an opportunity to improve the efficiency of decision-making based on having the most up-to-date information possible. For trading, being able to model the wider impact of decisions before and after they are made can help those trades to be more profitable for the organisation as a whole. Fraud analytics can help spot trends and spot that activity before it affects customers.

On the retail side, combining data from different sources – for example, customer details alongside location data and historic information – can be used to personalise and customise offers to those customers. This provides greater opportunities for banks to use their online payments and services in new ways. In this sector, retail banking CIOs are looking at some of the experiences that retail companies have experimented with, and then applying some of those approaches to their own service offerings. Online payments is a new area of competition for many of them, so building new services that can differentiate the bank is a great opportunity for competitive advantage.

Looking at the overall liquidity that a bank may have at any given point is a critical requirement for all banks to consider. As the overall economic market continues to develop and growth returns to market sectors, banks play a crucial role in supporting development through lending and availability of funds. However, banks have to maintain much larger levels of reserves in order to cover this lending.

It’s here that big data can have a significant future impact, as all banks engaged in lending and trades have to model their liquidity and risk more accurately. Following on from the credit crunch in 2008, the introduction of Basel III and new planned guidance from the US Federal Reserve will compel banks to hold more of their assets to protect against risk.

Over the next few years, banks will have to demonstrate to the appropriate regulation and compliance bodies that they are holding enough capital to cover themselves depending on trading positions and business requirements. However, big data has the potential to analyse how potential trading decisions can affect the requirement for whether additional liquidity will be required, or show where assets can be made available for other uses.

Over time, the bank can reduce the amount of capital that it is required to hold and instead look to generate further revenue opportunities. Here, big data can support more efficient use of capital and assets while allowing the bank to show it is in compliance with these forthcoming standards. Even as banks will have to hold more assets, this potential use case for big data will lead to significant potential savings over time. 

So why aren’t all banks taking this approach to using data, as part of their preparation for Basel III as well as to generate more potential profit now? Well, banks already are starting to deploy big data – according to Gartner, 32 per cent of banking organisations surveyed last year are implementing this kind of solution. However, in my discussions with customers, they are facing challenges in getting these big data projects from initial projects into full production.

Much of this is down to the traditional architecture that is currently in place within banks.  Traditional relational databases, otherwise known as RDBMS platforms, were originally designed before the advent of the Internet and cloud computing. Consequently, they were not designed to cope with the sheer volume of potential data that can be created and combined in real time in order to provide the analytics. Whether it is on the trading or the retail banking side, enterprise architects and developers within banks are looking at NoSQL databases instead to fill this role.

NoSQL, or Not Only SQL, describes a family of new database platforms that are designed to cope with this level of scale. Originally developed by the likes of Amazon and Facebook, platforms like Apache Cassandra are being taken up to fill the gap around scale and availability that RDBMS platforms cannot. Enterprise organisations are now using these technologies as well as part of their approach to dealing with data at scale.

One bank CIO I spoke to recently mentioned that he did not see other banks as his competitors; rather, he was looking at what the likes of Facebook and Google were planning to see how this might impact his organisation. Consequently, banks are looking at how to make the most of these web-scale IT platforms too and keep their IT architectures up to the task. In essence, this is about taking the DNA of businesses that were born on the web and applying it to the business of banking.

As banks look to maintain their competitive advantage through using data in real time, developers are moving to new databases to support their future use of that data. IT teams are looking to reduce their costs and improve how efficiently assets are used, moving over to an “Internet Enterprise” approach that is scalable and has high availability. As banks look to become even more data-driven, so these new platforms will become critical to their success.

By John Glendenning, Vice President EMEA, DataStax

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