“Data Analytics can bring down the cost to significantly low levels”

Vinod G, Head- Data Science & Analytics, South Indian Bank

There is a significant size of ‘unbanked’ segment, which may be difficult to bring into the umbrella of banking, if we stick to mere traditional tools. To know more about the journey of utilising analytics in the banking industry, Shruti Jain of Elets News Network (ENN), had an interaction with Vinod G, Head- Data Science & Analytics, South Indian Bank.

Data is now seen as the new oil’. What does it signify and how is it redefining decision-making?

There are two different interpretations of what oil is. And, data fits both. Firstly, oil can be seen as the substance that’s critical for things to move. (Let’s broaden the term, ‘oil’ to include electricity, in the case of electric vehicles). Just as oil is critical for any vehicle to move, data is becoming the most critical component for the progress of any business organisation. Gone are the days when data used be the mere ‘energiser’ or the ‘disruptor’ (‘speed petrol’ – anyone?).

Today, without data and its optimal use, businesses run a great risk of stalling totally. It is no more about a different way of doing things or a mechanism by which one ensures incremental business or one saves cost. (Not that it doesn’t. We’ll come to that in a minute). Data has a power of its own, if channelised well, can open up new, lucrative and relatively risk-free lines of business. Or, data analytics can bring down the cost to significant low levels. So, in today’s world, if one wants to move, one needs data and the right skills of analytics to tap it.

Another interpretation of oil is to lubricate. For efficiency of operations, one needs oil for its lubrication properties. The same goes with data too. Even when we don’t use it for more sweeping purposes such as a dedicated line of business or full- fledged automation for cost avoidance, data is still an important component for ensuring higher efficiency in carrying out the various business activities.

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Cost of operations is being brought down drastically through data and analytics. Accurate identification of customers is enabled through propensity modelling based on past data, thereby reducing wasted campaign efforts. Risky customers (especially in the domain of financing) are easily identified, thereby reducing collection costs that may otherwise be incurred through a paint-brush approach.

How is South Indian Bank adopting Big Data Analytics to maintain relevance and profitability in this hyper-competitive business environment?

We, at South Indian Bank, have embarked on a journey of utilising analytics, in a much bigger way, in the way we go about our activities. Be it, right from customer identification, getting to know our customers’ needs better, assessing the risk-levels of the customer before giving them loans, to customer journey management, analytics in collection-related activities, we have either put in place state-of-the-art predictive models to derive insights or have plans for it in the shorter term.

Using the footprints that our customers or prospective customers leave in the digital world, we are now able to understand the exact needs of different hyper-segments of our customers and offer tailor-made products and services for them. What is also important here is to make those relevant offers at the right time. And, that’s exactly what we are proposing to put in place, through big data analytics.

In your thoughts, what does the future hold for digital banking technology?

Frankly, things are changing so constantly and inorganically that it would be foolhardy to say with authority as to how the digital banking scenario is going to pan out in the medium-term future, leave alone long-term.

However, digitisation and analytics have already grown so much in the BFSI sector that we would very soon reach a situation where every single decision that is being taken by a bank or a financial services company, can be either fully machine-driven (full automation of the decision using ML- based models) or machine-enabled (critical insights culled out by the machine to the decision-maker so as to make it an objective call by the person, instead of relying on mere hunches). This could be true for not only the core functions of the bank (on-boarding liabilities customers, risk assessment of asset customers, exposure to different industries, automating operational processes, optimum capital allocation, loss-forecasting, etc.), but also in respect of support and control functions (right talent for the right position, Audit Intelligence, automated compliance alerts, etc.)

How is big data technology empowering customers today?

Digital banking technology has now reached a fair level of maturity, where literally every service that a customer needs, is available to her at his fingertips and from the convenience and comfort of his home or office. The self-service, enabled through digitisation and analytics, is not seen as a ‘delight’ factor anymore. They have very quickly become ‘hygiene’ factors and banks/ finance companies that do not provide these enablers, are digging up their own graves.

Also Read: Data Analytics: Driving India’s exponential business growth

Big data technology has also enabled the customers to meet all their other needs also without any physical contact. Eg., from soaps and groceries to white-goods to even cars, customers make their buy decisions online and, in most cases, even execute the purchase. It’s very important for banks to understand such needs of the customers and offer the right financing products at the right time. Financiers who develop that level of understanding of their customers would ensure a much better retention of their customer base.

What future innovations can be seen in the field of data science & analytics in the next two years?

Tie-ups and collaborations with technology and analytics divisions of other vendors and service providers to provide a holistic experience to the customer.

Eg., can a car showroom have an app-based or kiosk-based facility where the customer can swipe his debit card for making the margin money payment and drive away the car she always wanted? The more one views the customer and her needs in an all-encompassing way, the more the bank would be able to think of, design and deliver products and services with a better understanding of the customer. And that would be critical for building differentiation and beating competition.

There is also a significant size of ‘unbanked’ segment, which may be difficult to bring into the umbrella of banking, if we stick to mere traditional tools. However, using advanced analytics and predictive modelling, this segment can be brought into the ‘banking’ sector, thereby helping driving the economy up, without too much of incremental risk.

Eg., lot of commercial vehicle drivers nurse a secret ambition to own such vehicles themselves. However, they do not have much documents to show the banker as evidence of their repayment capacity. However, using predictive modelling, it would be possible to dig deeper into the profiles, their demographics, their experience, etc., and come up with a fairly accurate application scorecard. This can help bring a lot of these customers into the credit umbrella.

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