BSFI industry: Trends, Analytics and Careers

Ameya Abhyankar
5 min readAug 30, 2021


An economy has certain sectors that are considered as its centerpiece, and a large part of economic activities are driven by these sectors. One such key sector is the banking, financial services and insurance (BFSI) industry. BFSI sector finds its presence in most activities that happen around us. For example, if a pharmaceutical company wants to setup a new drug manufacturing facility, it approaches lenders say banks/NBFC etc. (banking) for funding purposes. Once the funding is received it ropes in finance specialists (financial services) who help the company manage its finances in the best way. Further, this company would want to protect itself against losses from natural calamities, physical damages etc. so it buys an insurance policy from insurance providers in the market (insurance). Thus, from this simple example, we understand how closely intertwined the BFSI industry is with the events that happen in an economy.

In this article we discuss the latest trends in the BFSI space, understand how analytics and technology are re-defining the industry, and potential job opportunities to make a career in BFSI.

Trends in BFSI

BFSI industry is no stranger to innovation. New products, approaches and technologies are frequently adopted by the industry to stay in sync with the market demand. Over the past few years, digitization has become a key driver towards shaping the vision of companies. This has ushered in an era which is being dominated by big data and analytics which are expected to play a larger role in decision making for organizations. Digital transformation has enabled companies to improve both their top line and bottom-line growth through various means including sales strategy formulation, operational transformation, efficient usage of financial and human capital etc. Companies are increasingly viewing analytics as an object to provide value proposition rather than just an academic initiative. Therefore, companies have increased technology spending to reap the benefits of digitization for fulfilling their strategic goals. Analytics technologies like machine learning and neural networks are industry agnostic to a considerable degree which have resulted in them being widely accepted across industry domains including the BFSI.

Re-defining long-standing approaches

Let’s understand a simple example to see how analytics is re-shaping the older approaches, making them more efficient.

BFSI companies use a variety of financial models for their business purposes. For instance, banks have a dedicated ALM (asset-liability management) desk to manage its asset-liability positions on a daily basis. A bank’s entire business model is based on the logic of matching assets with liabilities. As a result, interest rates are a key risk factor which ALM desk has to manage. Market quotes for interest rates come in for specific tenors starting overnight rates uptil say 30 years. Banks have ready systems for fetching this market data for further usage. Now this data on a daily basis that too considered over a long period of time is naturally voluminous. For example, a bank is attempting to measure the interest rate sensitivity of its portfolio, it would be in-efficient to use all of the available data, the reason being not all of the available data may add value towards the goal of measuring portfolio risk. Thus, if the bank can have a model to process the incoming data and choose only those interest rate data points which contribute to the risk of its portfolio, that would potentially enhance the overall system performance.

Machine learning techniques can be used for achieving the above purpose. Machine learning algorithms implementing the concept of Principal Component Analysis (PCA) can be used to this end. A PCA algorithm accepts all of the incoming interest data as input, it processes this data so that and gives an output as a smaller set of interest rate data points which can explain close to 99% of the interest rate sensitivity of our portfolio. This is technically termed as dimensionality reduction. PCA can potentially reduce the load on the system resources, as the system will use only those tenor points as have been chosen by the PCA algorithm. This enables freeing up of valuable system resources which now can be used for other productive purposes. Advanced Machine learning libraries enable users to readily build algorithms for implementing PCA. For instance, Python in particular offers wide variety of powerful libraries for such implementations.

Job opportunities for the future:

Majority of new job roles in the BFSI business are expected to be related to technology and its application to BFSI. Thus, for prospective job hunters, it would be a good idea to build expertise on techno-financial concepts at the same time be aware of application of math and statistics to analytics. In a dynamic market that we are currently living in, companies are looking for professionals who possess a blend of finance + hands-on technology skills. The demand for such professionals is only going to grow with time. For professionals targeting exciting roles in the industry including model development/validation, product pricing, risk management, derivatives trading and structuring, etc. having a hands-on technology skill including knowledge of programming and databases is a must have!

How FinQuest Institute can help:

At FinQuest Institute, we conduct specialized training programs in the domains of Finance, Quantitative Analytics, and Programming for financial applications. Our training programs are carefully curated after analyzing the skill-gaps faced by the industry. Through a regular dialogue with our network in the financial industry, we strive to stay abreast with the dynamic nature of skill requirements of the industry. We aim to deliver high quality training content by combining our core domain knowledge with our teaching skills. Our pedagogy focuses on helping candidates get complete clarity on core concepts by relating all of our live classroom sessions with practical scenarios observed in the industry. Our efforts are channeled to enable candidates develop a transferable skill set which can be applied to various business challenges that they work on in the future.

This article was first published in the monthly newsletter ‘Samvad’ by Welingkar Institute of Management, Mumbai, India. FinQuest Institute was the sponsor for the August 2021 issue of this newsletter.