Need for data-driven smart solutions in the financial sector in India


by Analytics Insight


January 10, 2022

Smart data-driven solutions can deliver immense benefits to the Indian financial sector.

The world is producing and consuming digital information at a staggering rate. In 2018, we created, captured, copied, and consumed approximately 33 zettabytes (ZB), or the equivalent of 33 trillion gigabytes, of data. This figure increased to 59 ZB in 2020 and is expected to reach a staggering 175 ZB by 2025.

This massive explosion of digital data, coupled with a multiple increase in computing power, has fueled the growth of “data science”, where scientific and mathematical methods are used to extract actionable insights from a large volume of data collected. This information is particularly useful for the financial sector, in particular in the following areas:

Digital loan

With the spread of the COVID-19 pandemic, digital lending has gained momentum. According to RBI data, the share of digital loans in total loans of non-bank financial companies (NBFCs) in India increased from just 0.68% in FY2017 to 60.53% in FY2017. fiscal year 2020. In digital lending, all processes related to loan origination, approval, disbursement, and collection are done online and remotely, primarily through mobile apps. Digital lenders can deploy data science to create robust risk algorithms that use data from a myriad of structured and unstructured sources, including social media. Data points collected for consumer loans include location, age, gender, income, job type, etc. For B2Blending, the algorithm uses firmographic, identity, financial, compliance, legal and financial data. The availability of a wide range of data points helps lenders better understand the behavior of their borrowers, thereby ensuring lower credit risk. Loan quality improves dramatically when data-driven models are deployed, as loans are approved based on objective data verifications. Data-driven digital lending solutions help lenders identify and engage with the right customer profiles throughout the borrower lifecycle, improving lender profitability.

Risk management

Risk is an intrinsic component of financial activity. Therefore, it is essential to identify and quantify risk factors before making loan, credit or investment decisions. Data science-driven risk analysis helps organize and analyze unstructured data, which forms the bulk of a company’s risk information, and significantly reduces the likelihood of human error.

For example, if a bank needed to perform risk analysis of a potential commercial borrower before lending, smart data-driven tools can quickly analyze large amounts of internal and external borrower data to provide insight into company and its risk profile. , as well as the background of its directors or owners. Data-driven risk models can highlight a company’s financial weaknesses, 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 the case of loans that have already been disbursed, data science-based credit risk monitoring tools can monitor and provide early warning signals (EWS) of any deterioration in business health. These data-driven EWS tools provide dynamic credit scores that automatically change based on new data points that the tools have collected. Lenders can take quick action to reduce their exposure to a company in the event that emerging data about it is negative and its credit rating has dropped.

Similar tools can also help underwriters in the insurance industry.

Financial inclusion through Fintech

Financial inclusion is one of the major challenges facing India. It also presents a tremendous opportunity for growth by improving access to finance for traditionally marginalized sections of the population. In the past, the lack of access to this segment of the population, the insufficient data concerning them to design suitable products and the resulting lack of confidence kept the organized financial sector on the sidelines.

However, data and technology have changed all of that with the ability to leverage unstructured and alternative data to better understand the behavior of various socioeconomic segments and demographic groups.

India is one of the few major economies in the world to have built Digital Public Goods (DPG). Popularly known as the “India Stack”, the series of volunteer-run software platforms are central to the Indian government’s digitization agenda and financial inclusion goals. The JAM trinity – Jan Dhan, Aadhaar, Mobile – has, in a few strokes, brought a large swath of the population into the traditional financial industry, while putting it on the Digital India bandwagon. The Direct Benefits Transfer (DBT) enabled by the JAM trinity is changing the face of finance in rural India.

India’s new DPG architecture has laid the foundation for more inclusive financial integration with services in regional languages, tailored insurance products and personalized services at the individual level. After the massive success of the Unified Payment Interface (UPI) which allows money to be transferred in less than 6 seconds, many more exciting innovations are on the horizon.

This has only been possible because government, regulators, the financial industry and new era fintech 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 an increase in fraud. According to RBI data, India recorded more than 229 bank frauds per day in fiscal year 2021. There is also a massive amount of frauds involving UPI transactions, most of which go unreported. However, the good news is that data science-based technology tools can analyze vast swaths of Know Your Customer (KYC) data and payment transaction data to identify patterns in fraudulent transactions and flag suspicious activity. This helps banks, NBFCs and fintechs to mitigate fraud as well as for anti-money laundering (AML) activities. Smart tools are particularly important for the payments industry to prevent and detect fraud.

Conclusion

With the Indian government’s massive push for financial inclusion as well as the digitization of payments, data analytics has a huge role to play in increasing revenue, improving customer experience, streamlining costs and predicting risk. . There has been a huge explosion of data in the Indian financial services sector, and the adoption of data analytics is important to make banking and financial services more convenient and egalitarian while adapting them to user needs. This will help achieve financial inclusion and digitalization goals while accelerating growth and improving the profitability of the financial sector.

Author

Mohan Ramaswamy, co-founder and CEO of Rubix Data Sciences

Founder and CEO of Rubix, Mohan Ramaswamy has a global experience of over 22 years, working with leading multinationals. Prior to founding Rubix, Mohan led the LexisNexis business for India and South Asia, growing the company into one of the most respected brands in the Indian legal information world. He spearheaded the organic and inorganic growth of LexisNexis and also executed several high-profile projects, including with the Prime Minister’s Office (PMO).

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

Mohan is a Mechanical Engineer and holds an MBA from TA Pai Management Institute, Manipal (TAPMI).

His interests include tennis and travel.

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