Investing in Big Data flexibility

Last month, the Joint Committee of the European Supervisory Authorities (JCESA) announced that in 2016 it would review the use of big data by financial services firms. The stated aim of the JCESA, which is made up of the European Securities and Markets Authority (ESMA), the European Banking Authority (EBA) and the European Insurance and Occupational Pensions Authority (EIOPA), is to “analyse the adequacy of sectoral regulatory frameworks and identify any regulatory and/or supervisory measures which may need to be taken.” While not an explicitly stated aim of the committee, many expect there will also be a need for it to define the characteristics of Big Data – a task which could take time and effort to address.

While unlikely to demand sweeping changes initially to the way data is processed within the financial services industry as a whole, it is still possible that the committee could request regulatory changes to how that data is allowed to be used for client transactions and marketing. The possibility, albeit slight, that regulation or compliance could change highlights the need for flexibility in the software selected to manage data. If investment is made in solutions that don’t allow for potential updates in future regulation or customer demands, then that money is effectively being wasted. While Big Data can provide a significant business advantage now, it’s important to note that users can limit themselves if they select a product that boxes them in without allowing for future innovation.

Benefiting from Big Data

In order to fully understand why changes to the regulatory framework might impact Big Data, it’s important to firstly take a step back to consider how financial service firms currently innovate through data strategies.

The explosion in the amount of structured and unstructured data being generated within the industry has created the opportunity to derive valuable actionable insights through frameworks such as Hadoop.

The first wave in this process was largely related to handling data moving from legacy infrastructure that struggled to cope with growing volumes. Some companies are now able to use this Big Data to address and improve their fraud detection and prevention capabilities. By doing so, they not only reduce the negative impact on customers but also significantly reduce their costs. As an example, by using modelling methods across a variety of data sources­ – including card membership information, spending details, and merchant information – it’s possible to stop fraudulent transactions before substantial losses are incurred.

To continue to provide the best protection for customers, constant changes are required as security threats evolve. The companies most successful in addressing fraud prevention through Big Data have been those which recognise that the problem never ends, but simply changes. As a result, firms need a Big Data platform that allows them to build new applications and tools, while maintaining constant vigilance over developing threats.

Another mainstay of Big Data use today focuses on customer experience. Companies are looking to create a 360 degree view of a customer through their data so that they can then create products for a specific issue or segment. As a company then uses this insight to add new products to its inventory or begins to innovate within its existing offerings, more data is created to further enhance the understanding of the customer. As different data is captured and new needs are identified, the timeliness and relevance of interactions becomes key to repeatedly improving customer experience and staying ahead of competitors.

Keeping on top of regulation

Without having insight into the motives behind JCESA’s launch of an investigation into the use of Big Data, one can made an educated guess that the related security and privacy concerns are major driving forces behind the scrutiny. This is in line with growing pressure to ensure consumer protection and fairness in financial transactions, which is at the heart of revamped financial regulations aimed at all types of firms.

With use cases multiplying as companies innovate with the vast quantities of data they are collecting, there is a clear challenge in keeping sensitive information safe and secure.

Financial services companies already recognise their first priority is ensuring they are compliant. They pay close attention to existing laws for safeguarding data, regardless of its volume. As part of this, financial services companies are mindful of data lineage, monitoring, reporting and auditing. Companies have generally done a good job identifying where sensitive data exists in structured fields. However, in unstructured data such as weblogs, emails or text files, sensitive data such as credit card numbers and transaction amounts are tougher to track. This complexity drives a much higher need for identifying and classifying sensitive data, which in turn requires specialised and flexible big data platforms to be effective.

With the volume and form of unstructured data only increasing, data platforms that allow analytics and applications regardless of data type will improve fraud detection and help deliver better customer service.

Preparing for the future

While the JCESA will review the existing use of big data by financial companies, there is the prospect of even more data being created by the Internet of Things on the horizon. A recent report issued by Deloitte, entitled ‘Management Information for Conduct Risk-underpinning better decision-making’, argued that financial services firms should be preparing now for how they intend to manage data from a range of new areas – from telematics used for insurance to smart building management tools. While financial services firms are used to capturing data from many sources (such as trading floors and credit card events) in real time, the prospect of even more being generated puts greater pressure on selecting the right platform to drive further digital innovation.

Big Data is currently undergoing an evolution, as the discipline moves from the theoretical and experimental to operational reality. This evolution has happened very quickly and Big Data capabilities are undoubtedly moving quickly, particularly in the finance world. While flexibility is key to enabling this process, it’s also about being able to implement these advances without adding further complexity. This will become particularly important as the regulatory and risk management environment continues to change. As a result, companies should be looking to ensure that current investments will stay relevant and drive long term value.

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