Big data analytics tools used by Banks and Financial Institutions
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Big data analytics tools used by Banks and Financial Institutions

By Durjoy PatranabishSoumya Subramanian

The Banking and Financial Services and Insurance industry (BFSI) is considered as one of the early adopters of analytics. Now with the unprecedented rise of data, the BFSI industry is on the threshold of reinventing itself. Additionally, factors such as rise in operational costs, cutting edge competition, and incremental risk are driving banks and other financial institutes to constantly innovate and differentiate.

Banking has always been considered a data heavy industry, thus analytics has the ability to redefine the playing field. Today, most of the major banks have started embracing advanced analytics and shifting towards more data-driven decision making. Analytics tools can help businesses across different horizontals of the organization ranging from Marketing and Sales, Operations to HR management. A data-driven and evidence-based business model allows banks to better understand their customers, markets, competitors to empower the workforce.

Today there is an abundance of tools that can help companies achieve analytics excellence. In fact, these tools are not new to most banks. What has changed is the scope of their applications which have been driven by deviations in the regulatory and economic landscape.

The challenge is in choosing the right one for the organization.  This should start with understanding the key problems that companies want to address – asking the right, well-defined questions. It is only when companies have clarity on the end objective that they can pick out the most apt from the array of tools available.

If we look at the analytics maturity curve, each stage of data management and analytics require different tools. Many times companies ignore the importance of having a robust data management system which is the foundation of the analytics. The data management can be divided into three major criteria – Database Management System, Data Modeling and ETL. Each step requires specialized tools such as SQL Server, TERADATA, Oracle, Informatica. After this, there is tremendous value that can be collected from this wealth of data.  Organizations can then focus on building high-end, dynamic reports by working with tools such as Microstrategy, Qlikview, Tableau and Spotfire. Each  tool comes with certain strengths and weaknesses, and depending on the existing infrastructures, feature, budget, data size etc.

When it comes to pure-play analytics, there are various ways in which banks can gain a competitive advantage. Tools such as SAS, R, SPSS are able to help organizations understand key business questions. Open source tools such as R and WPS enable companies to experiment with different techniques without making huge investments.

Companies, including those in the financial industry, are going the extra mile to understand their customer base. Today we have the most advanced and sophisticated tools and algorithms that can help analyze not only structured data but also unstructured data. With the advent of machine learning tools, such as Python, and techniques, such as Natural Language Processing, Support Vector Machines Learning Algorithm, are revolutionizing the BFSI industry.

Tools, in combination with intelligent modeling techniques, can really push the BFSI industry to new heights and across functions such as Risk, Marketing and CRM to:

  • Focus on customer-centric approach Monitor compliance effectively
  • Take faster and more data-driven decisions thus increasing operational efficiency which in turn contributes to the overall revenue enhancement
  • Compute various scorecards such as collection, application to better risk management
  • Identify key  products and customers
  • Allow senior management to obtain competitive insights to build into their overall strategy

However, BFSI industry is advised to walk with caution. While using various Big Data tools and techniques can reap huge benefits, one should never lose sight of the importance of data security. Companies should not shy away from making significant investments in building a vigorous data governance model and data encryption tools. This might seem insignificant but they are imperative for overall success.  There is already multiple use cases in the financial industry which stresses on the importance of companies using the right tools and techniques to leverage on the power of data.

Durjoy Patranabish
Durjoy Patranabish
Durjoy Patranabish has 19+ years of rich experience in diverse areas of the services industry, especially in Outsourcing, Banking & Financial Services. He has a...
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