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What does big data really mean for the financial sector?

Thursday 9 February 2017

Big data banking is only as effective as the analytics.

Big data has been a buzzword on the lips of business managers and CIOs for at least the last two decades. Every industry, from financial to medical and even local authorities, appears to be investing in ways to be a part of the modern information gold rush. But what does big data really mean for the banking and financial sector? Greg Richards, Sales and Marketing Director of business intelligence specialist Connexica, explores further.

Traditionally, the financial sector hasn’t been the most receptive to new technologies. As an industry that thrives by minimising risk and making carefully calculated business decisions, choosing to handle high value sensitive information with what may simply be the technological flavour of the month is not a decision many rush to make.

Despite these reservations, the Financial Conduct Authority (FCA) included investment in technology as the top priority of its 2015/16 business plan, a clear sign that the sector needs to be making a more conscious push towards digitisation.

Investing in big data

The next step is big data, a technological phenomenon that has stirred significant interest from the banking industry. The idea that data generated during the everyday processes and operations of a business can be used to inform strategies and achieve objectives is an exciting one, particularly in a post-crash economy where banks are faced with constant scrutiny over the detail of risk reporting.

However, there remains a question of what big data truly means for the financial sector. Although the information can yield a competitive advantage for banks, for it to be effective it has to be analysed effectively.

The majority of the conversation surrounding big data banking to date, has looked at what models and systems are best at making the data accessible for analysts. Yet the question banks should be asking is, “how can this information be actionable for us?”

Although many financial institutions are increasingly using cloud computing to host software platforms and to store data, unfortunately, the analysis itself is still reserved for specially trained individuals — typically data analysts rather than bank managers.

This approach limits the functionality of big data. One of the most valuable characteristics of big data is that it gives banks a real-time insight into multiple data sets. The places that customers regularly use their cards, for example, can be analysed to highlight opportunities for additional revenue streams by partnering with relevant retailers. This could take the form of targeted customer-cashback offers or even to provide anonymised commercial insights to the retailer.

Changing the data analysis game

When choosing a data analytics package, banks should look beyond SQL-based software into the different types of big data analytics for financial services — notably search-based analytics.

Search-based analytic software makes use of natural-language search, the same technology that internet search engines use, for a simple and uncomplicated approach to navigating and inspecting data sets. As a result, cross-referencing becomes an easy process and correlations can be spotted without the need for a technical skill set. This means people at all levels in the bank can benefit from actionable business insights. Software such as Connexica’s CXAIR, for example, can even draw this data from a wide range of disparate sources, meaning that banks that prefer the traditional bespoke systems can make use of the functionality without the need for migration to a new system.

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