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How AI Can Change The Face Of Customer Services In BFSI

And whether such an opportunity or emergency of being a first mover and building a stronger customer base should be ignored, it would only depend on how much one is ready to lose.

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Whenever a new technology comes up, the BFSI segment is one amongst the first to adopt and implement it. Something similar happened when automations were launched, and ever since Artificial Intelligence made its inroads, the trend has been same as well.

However, if one looks at the propositions that Artificial Intelligence (AI) can offer to the Banking, Financial Services and Insurance Sectors (BFSI) in general, there hasn’t been much current level of engagement. This can be partly attributed to the lack of clarity on a lot of AI related aspects such as the exact pain points which the solutions could solve, the time taken for implementation of the solution, the kind of investment that could be required, among others.

The lack of information on these fronts has however kept a lot of Indian as well as global banks from registering a significant increase in their revenue in the past 3-5 years. For instance, had the AI based fraud detection system which enables transaction monitoring, sentimental and trait analysis along with Facial Scanning had been employed, the 5% annual revenue which gets lost globally owing to cybercrime, could have been curbed to less than 2%. As surprising as it may sound, a lot of banks, the global ones included still rely on the age-old process of historical analytics to address fraudulent concerns.

Some Use Cases

BFSI segment is large in itself and offers multiple use cases that could be of immense value addition. For instance, AI solutions for segments like Investment Management, Payment Banking, Customer Acquisition, Wealth Management, Risk Mitigation, Pre and Post Loan Processing, etc., offer some low hanging fruits which could show immediate results for Revenue Maximization. 

One such strong use case for AI lies for Wealth Management and Capital Markets. A major concern which has hampered the growth of portfolio management or financial advisory businesses is the lack of accuracy in portfolio decision in addition to the compounded costs for the customers. An AI based Robo Advisor that uses the entire transactional footprints of the person in concern, can define the portfolios not just to mitigate risk to the minimum, but also ensure maximum benefits taking into account not just the requirements, but also the financial behavioural patterns that the said person exhibits.

The retail banking with its multiple manual or semi-automated processes can be another major benefactor – both when it comes to payments as well as loan, or other claim processing. Fraud Detection, as mentioned earlier could definitely be a major game changer here; especially with the advent of CNN (Convolutional Neural Networks) which are capable of processing visual cues and can thus enable even an ATM machine to recognize if the person requesting the transaction is the same as the card/account holder or not.

The digital payments market is about to touch over $500Bn by the end of FY-2020, and will be contributing to about 15% of the Indian GDP. Credit scoring and transaction routing are two domains which are set to benefit here. While analysing the transactional histories coupled with a Customer Data Platform (CDP) which enables hyper-personalisation, can help the banks save on the 40% credit applications that get rejected due to the false positives being raised, analysing and storing the best transactional routes every time a payment is processed, can help the firms suggest the preferred payment routes to the end users and thus reduce the overall transactional time and hence preventing transaction time outs.

If we talk of loan processing, the mundane documentation tasks can be easily replaced with an AI based engine which can conveniently assist the end users with the form filling and also prevent any corrections that need to be done at a later stage, thus saving both effort and manhours which could then be utilized for other more important tasks. The reason, an AI application is a significantly needed disruption when it comes to loan processing would be clear looking at the data: about 35% of the applications in India get rejected annually and about 60% of these rejects are because of lack of documentation – a loss to the tune of INR 15,000 crore for the Indian banks alone.

Another extension of the functionalities suited for loan processing can be of essence in the Insurance sector as well, which again is both data-and-time-intensive. Incorporation of AI powered analytics engine for claim processing and/or a smart communicator that can assist end users in real time to help solve their concerns can help build a good brand reputation.


We can go on discussing about how critical AI is for the BFSI in India; especially since its economy has been growing and the predictions are positive. What we need to understand is that with the ease of doing business index improving, the foreign banks are going to set base here. Many of these banks have already warmed up to the concept of AI and are even implementing it in their processes. The competition for the home-grown banks is thus not internal, it comes internationally.

And whether such an opportunity or emergency of being a first mover and building a stronger customer base should be ignored, it would only depend on how much one is ready to lose.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.

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Subrat Parida

The author is CEO and Founder at

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