BSFI industry: Trends, Analytics and Careers


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

Re-defining long-standing approaches

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:

How FinQuest Institute can help:

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.



Founder: FinQuest Institute | Ekspert Consulting;;

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