Data-powered smart solutions can provide immense benefits to the Indian finance industry.

The world is producing and consuming digital information at an astounding rate. In 2018, we created, captured, copied, and consumed an estimated 33 zettabytes (ZB)—the equivalent of 33 trillion gigabytes—of data. This expanded to 59 ZB in 2020 and is forecasted to reach a staggering 175 ZB by 2025.

This massive explosion in digital data, along with a manifold increase in computing power, has powered the growth of 'data science', where scientific and mathematical methods are used to extract actionable insights from a large volume of collected data. These insights are of particular use for the finance industry, especially in the following domains:

Digital Lending 

With the spread of the COVID-19 pandemic, digital lending has gathered steam. As per RBI data, the share of digital loans in total Non-banking Financial Company (NBFC) lending in India has grown from a mere 0.68% in FY2017 to a massive 60.53% in FY2020.  In digital lending, all processes related to loan origination, approval, disbursal, and recovery are done online and remotely, mostly through mobile apps. Digital lenders can deploy data science to create robust risk algorithms that use data from myriad structured and unstructured sources, including social media.  Data points collected for consumer lending consist of location, age, gender, income, type of employment, and so on.  For B2Blending, the algorithm uses firmographic data, identity, financial, compliance, legal, and financial data. The availability of a large range of data points helps lenders better understand the behaviour of their borrowers, ensuring lower credit risk. The quality of lending improves significantly when data-driven models are deployed, as loans are approved on the back of objective data checks. Data-driven digital lending solutions help lenders to identify and engage with the right customer profiles through the borrowers' life cycle, thus improving the lenders' profitability.

Risk Management

Risk is an intrinsic component of the finance business. Therefore, it is essential to identify and quantify risk factors before taking lending, credit, or investment decisions. Data-science drove risk analytics help organise and analyse unstructured data, which forms the bulk of a business' risk-related information, and drastically reduce the probability of human error.

For instance, if a bank were to conduct the risk analysis of a potential commercial borrower before lending, data-backed smart tools can quickly analyse a vast quantity of the internal and external data about the borrower to provide insight into the business and its risk profile, as well as the track record of its directors or owners. Data-driven risk models can highlight the financial weaknesses of a business and provide a Credit Score and recommend credit limits. Based on the credit score generated by the risk assessment model, the lender can decide whether the business is creditworthy.

Even in cases of already disbursed loans, credit-risk monitoring tools based on data science enable surveillance and provide Early Warning Signals (EWS) about any deterioration in the business' health. Such data-driven EWS tools provide dynamic credit scores that change automatically on the basis of the new data points that the tools have gathered. Lenders can take quick action to reduce their exposure to a business in the event that the emerging data about it is negative and its credit score has dropped.

Similar tools can help underwriters in the insurance industry as well.

Financial Inclusion Through Fintech

One of the main challenges that India faces is financial inclusion. It also presents a tremendous opportunity for growth by improving access to finance for the traditionally marginalised sections of the population. In the past, the lack of access to this section of the population, the inadequacy of data about them to design suitable products, and the resulting lack of confidence kept the organised financial sector away.

However, data and technology have changed all that with the ability to leverage unstructured and alternate data to better understand the behaviour of various socio-economic segments and demographic groups.

India is among the few major economies of the world that have built Digital Public Goods (DPG). Popularly known as the 'India Stack', the series of volunteer-driven software platforms are central to the Indian Government's digitisation programme and financial inclusion targets. The JAM trinity—Jan Dhan, Aadhaar, Mobile—have, in a few strokes, brought a large section of the population into the mainstream finance industry, while putting them on the Digital India bandwagon. The Direct Benefit Transfer (DBT) enabled by the JAM trinity is changing the face of finance in rural India.

India's novel DPG architecture has laid the foundation for more inclusive financial integration with services in regional languages, tailormade insurance products, and services customised at the individual level. After the massive success of the Unified Payments Interface (UPI) that enables money to be transferred in under 6 seconds, many more exciting innovations are on the horizon.

This has been possible only because the government, regulators, financial sector and new-age fintechs have come together to harness the power of data, analytics and technology.

Fraud Management

The flip side of digital transformation in the payments industry is the increase in the quantum of frauds. As per RBI data, India saw over 229 banking frauds per day in FY 2021. There is a massive amount of fraud involving UPI transactions as well, most of it unreported. However, the good news is that data-science based technology tools can analyse vast swathes of Know Your Customer (KYC) data and payment transaction data to identify patterns of fraudulent transactions and flag suspicious activity. This helps banks, NBFCs, and fintechs in fraud mitigation as well as for Anti-money Laundering (AML) activities. Smart tools are especially important for the payments industry to prevent and trace fraud.

Conclusion

With the Indian Government's massive push for financial inclusion along with digitisation of payments, data analytics has a huge role to play to boost revenue, improve customer experience, streamline costs, and predict risks. There has been a tremendous explosion of data in the Indian financial services industry, and the adoption of data analytics is important to make banking and financial services more convenient and egalitarian while tailoring them to users' needs. This will help meet financial inclusion and digitisation goals while accelerating the growth and enhancing the profitability of the financial sector.

Author

Mohan Ramaswamy, Co-Founder & CEO of Rubix Data Sciences

Founder and CEO of Rubix, Mohan Ramaswamy has an overall experience of 22+ years, working with leading MNCs. Prior to setting up Rubix, Mohan headed the LexisNexis business for India and South Asia, transforming the company into one of the most respected brands in the Indian Legal Information world. He drove organic and inorganic growth at LexisNexis and also executed several prestigious projects, including with the Prime Minister's Office (PMO).

Before LexisNexis, he was the Chief Operating Officer of Dun & Bradstreet India, a subsidiary of the world's leading business information company. He helped establish the Dun & Bradstreet brand in India and was a part of the core team that helped set up SMERA Ratings Ltd (now Acuite Ratings & Research Ltd), India's first rating agency focused on SMEs.

Mohan is a mechanical engineer and has an MBA from the TA Pai Management Institute, Manipal (TAPMI).

His interests include tennis and travel.