“There is a higher acceptance of AI and ML models in day-to-day decision-making

Balakrishnan Narayanan, Head of Analytics at Fibe (formerly known as EarlySalary)

The financial market is growing in sync with the dynamically changing customer experience. The younger generation today has started entering the credit cycle at least two years early as compared to the earlier generation. To know more about India’s readiness in terms of data analytics and AI, Rashi Aditi Ghosh of Elets News Network (ENN) had an interaction with Balakrishnan Narayanan, Head of Analytics at Fibe (formerly known as EarlySalary).

In your view, how significant is the use of analytics for the BFSI industry today?

In the era where technology has been driving the financial sector to soar high, Data Analytics has significantly been helping financial institutions to know their customers and deliver what’s best in their interest. Not only this, big as well as small financial institutions have been using analytics to ensure better risk management, profitable growth and performance for their ultimate success.

The increasing interest in the use of Data Analytics in the banking sector is also due
to the rapid changes occurring in this sector. This includes changes in technology, people’s expectations, their behavior and market structure. Companies are leveraging data to optimise themselves to become more efficient. Organisations today can manage the supply side of the equation as well as the demand side through a good monitoring system.
Banks today are experimenting with conversational AI platforms, which allows them to successfully service their customers using NLP and Voice BOTs. These BOTs are able to understand the context behind customer requests, resulting in requests being handled without any human intervention.

How are you helping Fibe (formerly known as EarlySalary) in driving its analytics functions?

Our focus is to deliver value to both the customers as well as the organisation through technology and analytics. Data plays a significant role in our day-to-day decision- making. We work very closely with the business to deliver the right results thereby reducing financial waste. Our team invests a lot of time in understanding our customer’s behavior through the available data.

This provides a personalised experience to our customers. We’ve been able to create propensity models that have helped us understand the customer’s current need and predict their future requirements.

We have also been able to provide faster resolution to customer queries.

As an organisation, we believe automation is the key to our success as this helps in keeping our operational costs at a very low level and ensuring profitability. The process
improvements using AI and ML in customer service ensure faster resolution and are a powerful tool for customer engagement.

We’ve enabled quicker disbursals of cash loans with our automated underwriting systems.

As the margin of error is very minimal and has a direct impact on the profitability of the organisation. A good data analytics team can elevate the capability of decision-making and portfolio monitoring with ease. Our models have learned to segment the customers correctly, even if they don’t have an existing credit history. More than 93 per cent of our loan decisions happen without any human intervention.

What major challenges do you come across while heading the analytics functions?

The financial market is growing and needs to change based on the changing lifestyle trends. The younger generation today has started entering the credit cycle at least two years early as compared to the earlier generation. However, due to a vast market and diverse customer expectations, finding the exact needs of the customers is tough. What technology or product might be suitable for some might not be suitable for others. Hence finding the perfect algorithms and techniques that might deliver the best result is sometimes not possible.

As innovation in the BFSI grows, it comes with its own set of challenges. Thankfully, the technology and analytics powering this space are getting more and more sophisticated to help tackle these issues.

Today, there is so much data that is being generated and with a deeper analysis of the same, we’ve been able to derive meaningful insights that could be valuable for decision- making and proactively understanding the ever-changing psyche of the customer. However, newer technologies becoming obsolete in a short span of time is a big concern for most organisations.

What would you like to comment on India’s readiness in terms of Data Analytics and AI?

Improved operational efficiencies and having better-detailed information for decision-making have only been possible with the advances being brought by Artificial Intelligence. The pandemic has brought into effect India’s ability to digital transformation. More and more organisations are now open to investing and experimenting with AI. According to a recent report, AI investments in India show a promising CAGR of 30.8 per cent and are estimated to reach $881 million by 2023. However, this investment amounts to 2.5 per cent of the total global investments in the AI sector. There is a higher acceptance of AI and ML models in day-to-day decision-making by most organisations which is driving the growth of the financial market model.

How do you see the future of analytics in the next three years?
The post-pandemic period has seen a surge in the usage of analytics to get better. It has created a better understanding of customers, tackling their demands beforehand, and assessing the risk involved.

With so much competition around, most companies are investing in innovation that they weren’t considering in the past. Companies would increase their focus and innovate further on customer retention.

With fintech building efficiency and reducing their overall operational cost through automation, large banks would follow suit. There could be hyper-automation using AI and ML for a better customer experience.

More and more companies have been using Data Analytics to analyse and understand their target customers and their needs and thus making better business decisions and thereby reducing financial waste.

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