Big Data Analytics in Financial Services


With growing application of analytics in business, the accuracy of the insights directly impacts the decision making and hence the growth of that business. Most of the industrial setup successfully function with reasonably accurate insights derived from sample data from their operations. However industries such as Banking, Insurance, Investment Banking and Foreign Exchange, because of the nature of their business, may not settle with anything less than 100% accuracy, an almost impossible task to achieve by analyzing sample data.

A part of Financial Services, these industries undergo innumerable transactions of all magnitudes exposing them to various risks every day. These risks arise not only due to fraudulent transactions but also attribute to noncompliance with governing regulations and operating standards. The management of most of these business design risk hedging strategies based on sample data analysis. These strategies stemming out of representative data may not provide complete risk coverage as each transaction is crucial purely from risk perspective. In such scenario, Big Data Analytics not only help financial services organizations to build strong risk hedging strategies but can also provide competitive advantages.

Big Data Analytics is the process to examine large volume of data to uncover latent information. The huge data coverage enhances the accuracy of the insights and related business decisions. The concept as it promises better risk management, at this juncture, appears as burnishing dawn for financial sector. While the concept offers a great opportunities for the industry however the organization may have to do some homework for its application.  On one hand the leadership may have to ensure their acceptance for Big Data while on the other side they would need to invest in the infrastructure to support massive data and analysis. This investment would be required for hardware support like data warehouse and for skilled talent pool to be able to code complex algorithm to run “real time” analysis.

Considering that an analysis performed on a larger sample size carries higher confidence, one may argue about the novelty of the theory behind Big Data. This is a valid thought but what makes Big Data special is its ability to solve bigger business problem more effectively. The situation can be aptly expressed as eleve en futs de chene, a French phrase meaning “wine has been aged in oak barrels”.

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Rupesh Khare

Rupesh has an extensive, around 18 years, experience in Consulting and Strategic Analytics. The experience spans into multiple domains such as HR Analytics, Pharmaceutical and Non Life Insurance (Actuarial- Property and Casualty). In this journey, he majorly worked with HSBC, Aon Hewitt, marketRx and XL Group. He is currently pursuing one year full time Sloan’s Master in Leadership and Strategy at London Business School, London. Prior to this, he has an integrated MBA (Finance) from IBS Ahmedabad and M Tech (Mining Engineering) from IIT Kharagpur.