Big Data in Banking and Finance

By | September 30, 2015

The movement from in-person to on-line banking and the regulatory requirement to maintain historical data has lead to huge and diverse volume of data being collected by Banks and financial institutions. In simple terms, data collected by banks is estimated to grow by 76% in 12-18 months. Though challenging, this exponential growth in volume, variety and velocity of data (3Vs of Big Data) offers extraordinary opportunity to trigger growth with new products and services.

Many players in the financial vertical, though wary at the start, have become enthusiastic adopters of big data technologies such as Hadoop.

Banks and financial companies are using innovative technologies to consolidate data that lies in different silos, department and regions, to analyze them holistically and derive enterprise-level analytics.

Those institutions that had employed relational database management systems found it challenging to process and analyze growing amount of data and have since moved to network of data stores to solve this problem.

Based on the early adopters, here are some broad developing trends in the area of big data, particularly for the financial vertical.

  • Bigger and better the data set, accurate the prediction – Data collected with greater granularity over longer period of time from diverse sources, make for better predictive models and forecasts.
  • Better predictive models, particularly the ones related to customer behaviour leading to launch of innovative products/services that have increased conversion rates.
  • Reduction in cost of doing business, if Operations can use data to understand how customers do business with them.
  • Predict non-performance of assets by analysing payment pattern, prioritize collection and reduce chance of non-collection.

Big data is driving innovation in today’s financial services market particularly in the area of capital management, regulatory compliance, corporate performance, trade execution, security, fraud management, and other instrumental disciplines. It is particularly so in uncertain times, companies that flourish are the ones that employ statistical modeling, and predictive analytics for real-time decision making.


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