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On the Banks of Big Data India’s BFSI Sector Poised for Big Leaps


Sunil Jose Managing Director, Teradata India

The Banking, Financial Service and Insurance (BFSI) industry in India seems to be at an interesting juncture. With an unbanked population of almost 40%, India has huge growth potential left – both for its public sector banks as well as private sector banks. Reports state that China and India could have a combined share of around 35% of the global banking assets by 2050. While that is the upside, the banking industry is also plagued with huge non-performing assets. Smaller, regional banks will struggle to compete against larger rivals and those with a high percentage of bad loans will increasingly have operational difficulties moving ahead.  Global factors such as the Greece default or the Chinese stock market crash also have the potential to rattle India’s financial markets and banking industry.

Technology is one great leveler. With the evolution of new payments banks and other banks adopting a branchless, contemporary and digital avatar, Big Data analysis and related technologies have the potential to play a significant role in redefining the functional attributes across the value chain for banks across customer acquisition and engagement, security and business competiveness.

Today, Big Data analytics is not only helping financial institutions maximize the value of data but is also helping them gain competitive advantage by minimizing costs, converting challenges to opportunities and reducing risks in real time. Banks have always been trusted with a lot of information regarding their clients. Big Data analytics is now helping banks to utilize this data effectively to provide their clients with a customised experience and in turn ensure their loyalty.

Some of the ways Big Data analytics helps banks and other financial institutions are as follows:

Improve offers and execute on targeted cross sell/ upsell of products and services:

One of the primary problems that banks face is that due to the immense burden of client information, ready access to that information and effective utilization of the same on a real time basis is not always possible. With the use of Big Data analytics, banks can access, analyze and apply this client information more effectively as it helps them with detailed client information on a real time basis. It allows them to provide clients with a more individual customized assistance which in turn helps to offer a variety of products, services and schemes that are direct and personalized, thereby not losing the business opportunity to be executed at every point of interaction in multi-channel context.

Contact center efficiency and problem resolution

Contact centers of banks are over burdened with information and real time access to information is a challenge all banks face. Big Data analytics helps with this, which in turn helps banks avoid all embarrassing situations. This increases the level of customer engagement and satisfaction.

Fraud detection and management

In multi-channel context, fraud management is a big challenge. It causes banks to lose millions on a monthly basis. A changing environment has offered fraudsters some tactical advantages and stiffened the challenge. Massive, rapidly accumulating volumes of uniquely structured data – much of it gathered from new opportunities to interact with customers – have opened new ground for fraudsters to exploit, even as the sheer volume has made it easier for them to hide. Conventional approaches to fraud detection and remediation are necessary but they remain effective to a point, as conventional tools simply cannot effectively and economically process what is known as Big Data.

Big Data analytics can enable banks to deploy and integrate rich and new data types to produce new and more sophisticated analyses against the fraudsters and track pattern of events continuously improving ability to arrest fraud in-flight, thereby changing the context from post facto fraud investigation to real-time fraud management and pro-active precautions on likely fraud candidates. In test cases, these analytics are massively effective at exposing not just the fraudsters themselves, but their networks and the people, places, pattern and processes they touch or will touch. It puts into place monitoring systems and through application of technology ensures that future frauds do no arise.

Counterparty credit risk management

Banks have to regularly enter into new agreements with new investors to keep their businesses going. In order to avoid risks and chances of financial damage, they have to evaluate the agreements and run background checks on their investors.  Big Data analytics helps banks run the checks effectively and efficiently and provide all ready information on a real time basis for thorough evaluation.

It can be concluded that incorporating Big Data analysis in their day-to-day operations is an essential step for all banks today. It assists them in not only ensuring personalised experiencesfor their clients and thereby attracting a bigger clientele but also helps them in reducing frauds and securing their systems. The expansion of the banking industry will ultimately depend on their ability to explore the full potential of Big Data analytics and incorporate the same in their functioning.

Banks are in the business of pricing risk, and Big Data analytics enables them to do so dynamically on an ongoing basis.

Sunil Jose is Managing Director, Teradata India

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