Big Data Analytics in Banking Industry
The notion “Big Data” is no more cramped to the realm of technology. It is expanding in diverse segments of the business world using some advanced statistical and mathematical models such as data mining, artificial intelligence, predictive analysis to gain new acumen ensuing superior and quicker business decisions. As the name itself indicate, is a concept used to define a huge collection of data whose volume and sizes are beyond the ability of traditional databases to collect, manage and analyze data with low dormancy. In this context, we can pose a question to ourselves “do banks and financial institutions have the potential to produce a huge volume of data?” The answer is, “Of course, Yes”.
Banks record millions of business transaction daily and these entries are real-time in nature. The volume of data generated by banks is not just large but also real-time in nature. Nonetheless, capturing and recording such a huge chunk of data is a challenging job for bankers. Big data analytics help them by providing a platform where these transactions can be recorded systematically.
Structuring and recording the data is useless until and unless there is a plan to make use of such a large data. Therefore, identifying the connection between the data captured and possible results is a puzzling task in today’s complex business world. The connections may be anything such as security and fraud detection, risk management, analysis of customer spending & investment pattern, compliance, financial reporting, market segmentation and product customisation, etc.
Big data gives insight into many complex areas of individual’s life including their lifestyle, needs, and preferences of their customers so that it is easy for banks to personalize services to the needs of each individual.
Decades ago, a typical bank customer would walk into a bank and be greeted by an executive who knew his name, his personal backgrounds and how best to serve his personal banking needs. This is quite an old model where banks have acquired and retained the customer’s trust and served them for a long time. However, the situations changed. People often be engaged in multiple assignments and travels to different geographical locations. If one day he stays in New Delhi, the immediate next day he may have to visit Paris on his business assignments. In such conditions, it is challenging for a bank executive to track his personal preferences and whereabouts to meet his needs. Big data gives insight into many complex areas of individual’s life including their lifestyle, needs, and preferences of their customers so that it is easy for banks to personalize services to the needs of each individual.
For a long time, the banks miserably failed to utilize the information generated by their own business. The big data has become a game changer in transforming their business process and conducts to identify business opportunities and potential threats. Generally, banks and financial institutions find big data from the sources such as log data, transactions, helplines, emails, social media, external feeds, sponsorship, audio, video and some other sources.
Introduction of big data in banking has destroyed many ground rules of business and transforming the landscape of the financial services industry. With a huge volume of data gushing from countless transactions, the banks are trying to find out innovative business ideas and risk management solutions. Each set of the data gathered over a period tells a unique story and shows the goalpost for a definite future period so that a business firm can capitalize on this information to attain a competitive edge in the market. Big data analytics can improve the extrapolative power of risk models used by banks and financial institutions. Big data can also be used in credit management to detect fraud signals and same can be analyzed in real time using artificial intelligence.
Big data analytics can improve the extrapolative power of risk models used by banks and financial institutions.
On a serious note, banking and finance industry cannot perceive data analytics in isolation. Along with identifying business opportunities, they should identify security threats, the occurrence of fraud and possible remedies. Further, they should attempt to connect big data across departmental and organizational silos. Many of the traditional banking entities in India have not yet begun their big data activities. The big banks have an edge in capitalizing on those opportunities. Therefore, big data science not only brings new insights to the banks, but it also enables them to stay a step ahead of the game with advanced technologies and analytical tools.
This is a guest post by Pavankumar Chandrappa, who is a subject matter expert in financial services. He is also a doctoral research scholar in the area of Technical analysis of stock markets and an MBA in Finance. He was involved in content development and review of financial management programmes at
Manipal University. His areas of interest include technical analysis, derivatives, inflation, foreign exchange and investment management. He has published several research papers in finance and has received several honors in recognition for his performance and achievements in the corporate and academic field.