How big data is redefining banking‏ industry

Larger, Faster and More Efficient

Alongside advancing technologies and the changing nature of data, financial companies turn to big data as it also helps them meet new regulations and remain risk averse. For example, NYSE Euronext (a prominent global stock exchange company) employed big data analytics to detect new patterns of illegal trading. With their new markets surveillance platform, their analysing processes have been simplified and sped up within billions of trades. NYSE Euronext previously reported that the new infrastructure has reduced the time required to run markets surveillance algorithms by more than 99 percent.

As the world becomes increasingly technology-dependent, it comes as no surprise that the financial industry is also starting to utilise ‘big data’ in order to sharpen risk assessment and drive revenue.Experiencing millions of transactions daily, businesses in the financial sector handle an immense and growing ocean of data in a highly regulated environment. Whilst the industry is becoming increasingly competitive as well, research has shown that almost three-quarters of financial services companies have either started developing a big data strategy or implementing big data as pilots or into process. 

Also, IT resources required to support the solution had decreased by more than 35%. This, on top of improving the ability of compliance personnel to detect suspicious patterns of trading activity and take early investigative action, consequently reduces damage to the investing public.

Using Data Effectively

Furthermore, such applications have also enabled banking and financial corporations to use their data more effectively. The predictive analytics of customer data can help quickly identify creditworthy customers. Being able to have a better understanding of customers means that a more personalised service may be developed, which can then lead to better customer retention.

“The insurance sector is probably one of the fastest growing areas in finance where big data is being used in this way,” states Scott Baker, Senior Principal at Excellence Corporate Consuting. “In particular, focussing on how they can use their data to get a unique advantage on the competition.”

The same kind of system can be used in other areas, like forecasting the performance of investment portfolios. Several case-scenarios can be calculated and considered, where methods of managing the process can be optimised to potentially improve profitability and/or minimise losses. With this, the ability to utilise predictive analytics shows its capability to provide even better and faster ROI whilst giving greater insight to their customers.

Changing for the Better

Whilst the technology used to analyse and handle data has greatly evolved, it has also become less costly, more innovative and simpler to use. Big data stands to be a promising change for financial services companies, allowing them to improve efficiency. Information can now be used more effectively, further enabling them to establish the right metrics, processes and reports in a shorter amount of time. They can also react more quickly towards the ever-changing regulatory and competitive demands of the industry.

Recruitment an Issue

“The role of a data scientist or a Big Data Developer, didn’t even exist a number of years ago, these types of positions have grown considerably in all data centric sectors,” explains Scott. “Larger banking companies have actually been relatively slow off the mark with regards to using a number of big data technologies, compared to a number of smaller start-up companies or Fintech companies which have based their business model on the success of “Big Data” technology” he adds. “Many organisations are finding it really difficult to find candidates with the right calibre of skills, which means that those with the right skills can command a high salary. The situation won’t last forever, though. As this skillset becomes more commonplace so supply and demand will level out somewhat. Already data analysts and programmers are switching and upskilling their skillsets in order to enter into roles of this type. Candidates need to have good programming skills and need to have a good academic background but what will set them apart is a real aspiration for understanding the way data works and the meaning of data. It sounds like a dry subject, but actually some of the data that they can be working with is fascinating.”