Virtusa Corporation is a provider of information technology services which include IT consulting, application maintenance, development, systems integration and managed services. 

Big Data adoption across several sectors has been low with most companies not moving beyond the planning stage for projects. However, BFSI sector has embraced Big Data to improve efficiency, create enhanced user experience, customer acquisition and retention, and to create omni-channel platforms. As BFSI is a highly regulated industry, there are also a number of compliance norms that require companies to implement systems to track fraudulent transactions, money laundering or any funding of nefarious activities.

BFSI sector faces more number of frauds than any other industry, particularly, for the financial services companies and even government departments like income tax department. Technology can help drastically cut down on the frauds and also put in place processes to predict possible frauds and related scenarios. In the recent years, BFSI industry has seen marked increase in investments in risk analytics. 

Another segment which has been benefiting from Big Data analytics is insurance. In fact, insurance industry is one of the top five technology spending industries in the world, with IT spending total of USD 187 billion in 2014 and growing at 3.8 per cent through 2018 as per a Gartner forecast. Insurance companies are inundated with false claims and as such they need to put in place robust risk management systems. Big Data analytics enables insurance companies to assess risks, avoid fraudulent claims and importantly, underwrite risks. 

Income tax departments in several countries are now using Big Data to flag off high value transactions. This data is then analyzed to check further if they correspond to the IT returns. Such practices are leading to detection and penalization of un-taxed money. While non-declaration of income has been a major challenge for income tax department, recent trend of fraudulent IT refund claims has also set the alarm bells ringing. Data analytics can also play a role in unearthing such false claims. 

Banks and financial services companies are able to achieve their business goals with the aid of predictive analytics derived from extraction of actionable intelligent insights and quantifiable predictions to unravel and predict customer behavior, including channel transactions, account opening and closing, default, fraud etc. They have been able to offer omni channel banking after analytic insights covering lifecycle of the customer and profiling of the customer and his or her family members. Technology has meant banks are now able to acquire new customers, retain the existing pool while cross selling entire range of customized services and products. Credit score based on historical behaviour has also bolstered risk management for banks while they prune customer base to maintain only the profitable pool of customers. 

Big Data analytics also help banks and financial services companies to limit customer attrition. Since these players are constantly at risk of losing existing customers, they begin to offer free value added services or even waive annual fees to curtail customer exits. However, considering retention strategies have associated costs, they cannot be offered across the board. Big Data analytics can solve this problem by identifying the right action for the right customer. With predictive analytics, it is now possible to deduce the probability of churn for each customer. 

Predictive analytics can also improve conversion rates by several times and boost top-line growth for these players.
Predictive analytics can complement the marketing efforts of banks that can draw effective campaigns based on a combination of internal customer data, external data sets and applied advanced analytics techniques. Such techniques help them identify potential customers and target them through a focused approach.

Banks can also track the historical transactions and draw an analogy with the products purchased by the customer to arrive at a buying pattern. Banks can then compare this data with the buying patterns of other customers of the same age group and gender and design products suiting these needs. This data can be analyzed to detect transactions made using stolen cards as well. 

Recent instances of banks using data analytics to design new services include Commercial Bank of Dubai which launched its new virtual branch on Facebook, for users to check their account balance and transfer funds from within the social media. Likewise, Citibank introduced a tablet application featuring interactive, graphic displays to help its customers understand their spending and plan for future. Barclays launched a video banking service for customers who prefer to bank on the move. Some Indian banks have also started offering such services.

Deployment of Big Data analytics can bring agility to this sector particularly at a time when new market entrants are rapidly changing customer preferences. In order to remain competitive and retain and acquire new customers, BFSI industry players should fully leverage the potential of Big Data.