Have we thought about how much data is consumed in a day? Every transaction we make, every google search, every text message, all in all accounts to almost 2.5 quintillion bytes of data that the entire population produces in a single day and for reference, 1 quintillion has 18 zeros! Let’s take a step back to see the evolution of integrating data in banking.
Following years of accustomed methods, the banking sector has planned their business operations adapting to new challenges by this revolution - a digital economy. With the outbreak of digitalization, a parallel between traditional techniques of data mining and introducing digital technologies to analyze big data can be drawn that breaks traditional data collection methods. Commercial Banks constantly face turbulent challenges to transform data by adopting technology and thrive or abide by legacy. This is the biggest dilemma.
With so many different kinds and large volumes of data, banks struggle to cope with procuring, storing and organizing this raw data. Banks face significant challenges in regards to big data’s ability to capture the mounting volume of data. Trying to analyse the required amount of information generated from data collected using an outdated infrastructure puts the stability of the entire system at risk. The whole process is inefficient, costly and inexpensive.
Capgemini analysis suggests only 37% of banks have hands-on experience with big data implementations, while majority of banks are still focusing on pilots and experiments. A dearth of analytics talent, high cost of processing data, and a lack of strategic focus on big data are also major stumbling blocks.
The above survey explains the main barriers in adopting big data in banking and highlights the various problems associated in trying to adopt data and analytics into the banking system. Big data solutions must ensure banks can proactively manage risks, if they are to harness the full potential of their data. With the constant need to evolve and survive, embracing big data solutions provides a competitive edge.
Larger the volume of data, higher the risk and increase in the possibility of fraudulent activity. The collection of various data sets supports concerns over privacy. Introduction of new regulations
force banks to monitor and secure customer data through different security systems. Only 38% of organizations worldwide are ready to handle the threat surrounding data privacy, according to ISACA (Information Systems Audit and Control Association) International, further reaffirming the importance of a strategic involvement in managing security.
The spurt of big data has opened new opportunities for the banking sector to grow.
However, the question still remains: How to make use of big data in banking to maximize its true potential?
The rise in competition, regulatory constraints and customer needs, financial institutions are seeking new ways to leverage technology to gain efficiency. Commercial banks aim to transform their business process and use aspects of big data to gain a competitive advantage. The continued adoption of big data and analytics will inevitably transform the landscape of financial services. With additional challenges faced while incorporating big data analysis, it will nonetheless result in increased efficiency.