Case for Data Analytics in BFSI

A key element in achieving complete digital transformation for the BFSI sector in India is the adoption of data analytics. This is crucial given that lending and deposits in the Indian market have been increasing at a CAGR of 20% and are poised to see further growth. Such advancement will only spur the demand for automated solutions, driven by data analytics.

Why is Data Analytics Relevant?

The ability to use the right data effectively could make or break a company. Given a choice between no data analysis and incomplete data analysis, the latter would inflict more damage.

Nevertheless, data analytics can eliminate the vagaries of human judgement and make processes much more reliable, quantifiable and cost-effective. Data analytics eases scalability, enhances the ability to provide custom products granularly while introducing a higher degree of predictability.

Take for instance the ability of insurers to provide coverage for a single cab ride. Would such specific, case-based and extremely low-cost solutions been possible without analytics? It is only due to improved operational efficiency and lowered cost of delivery that unsecured business loans of a mere 2 lakhs can be offered or a single train journey can be insured for just 49.

Potential Applications of Data Analytics

Banks and insurance companies are enhancing their credit underwriting by looking into new data sources, including e-commerce transactions, insurance aggregator data, travel aggregator data, social media conversations and more. They can take credit decisions, such as who to give loans to and how to effortlessly collect money from customers that have missed payments. Banks can know where to set-up ATMs in the country and how much cash to make available, based on location data and withdrawal volumes.

Recommendation engines of the future will become more intuitive, offering customers suitable products and investment options, without the customer even having to visit a financial portal. Customer services, which has long been a pain point for financial institutions, can be automated to address queries efficiently without human intervention through self-learning AI-powered chatbots.

Creditworthy customers from a segment of society that has historically never been provided ample opportunities, due to lack of traditional eligibility, can be cherry-picked by data-driven financial firms. Risk assessment, compliance and reporting can all be enhanced too.

Driving Higher Uptake of Data Analytics

Different BFSI verticals appear to be on a data analytics usage gradient. Verticals such as lending, insurance, wealth management or banking, have different adoption rates, with banking and insurance being at the forefront of data analytics implementation. Calculation of credit risk in lending and policy premiums in insurance uses the maximum amount of analytics.

The banking sector appears to be the latest entrant in the adoption curve. Collaborations with new-age start-ups will help in the seamless adoption of technology across the sector. Establishing innovation cells intrinsic to business will further encourage the usage of data analytics. This can be further aided by regulatory sandboxes that encourage the effectuation of data analytics across the gradient.

NASSCOM expects the data analytics market in India to touch $16 billion by 2025. It will only be through a fully data-driven strategy that the BFSI sector achieves enhanced returns on customer acquisition and retention, while transforming business operations, reducing risks and providing seamless omnichannel services.

Tushar Garimalla, Chief Growth officer, Capital Float