The Good of GST: Increased Use of Data for Good
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Since its implementation on July 1, 2017, the Goods and Services Tax (GST) has delivered several major economic benefits. Not only has it introduced more regularization within the market, but has also enabled greater transparency and accountability. By subsuming various indirect taxes, it has also made interstate trade more efficient to stimulate economic activity within India.
Another major benefit of GST is the increase in digital adoption within the Indian business landscape for bookkeeping and transactional purposes. On the one hand, this facilitates smoother GST filing and compliance for enterprises. On the other, it creates huge volumes of enterprise-related financial data.
This increased data access is also important, for it helps fintech players in extending financial inclusion to market segments which have been underserved and unserved by traditional BFSI companies.
How Fintech Players are Stimulating the Economy Through Data
Gauging borrower creditworthiness has always been a big pain point for the BFSI industry. Fintech players have been addressing this challenge by complementing conventional lending parameters with non-traditional data points. They leverage cutting-edge technologies such as artificial intelligence, machine learning, and data analytics to create more accurate and holistic borrower profiles.
Such a tech-led intervention eliminates the dependency on credit scores or previous credit histories for gauging creditworthiness and enables access to capital to a much larger segment of borrowers. This, in turn, is helping to bridge the massive credit gap that the country is currently facing. The GST rollout will further bolster this large-scale financial inclusion; greater compliance by Indian MSMEs will provide lenders access to high-quality, standardized financial data in real-time.
The MSME sector – one of the largest contributors to the national economy – is poised to be the biggest beneficiary of this development. With enhanced access to data making true flow-based lending possible, fintech platforms will be able to better cater to MSME credit requirements with minimal documentation. The higher quality of data will also drive unparalleled hyper-personalization, enabling fintech companies to create customized lending products tailored to the specific capital requirements of every potential borrower.
Smart Data versus Big Data: Creating differentiation through analysis instead of aggregation
The true worth of data lies not in its volume, but in the business value that it can drive. It is an unfinished product which needs to be processed and analyzed to generate contextual insights which can make the decision-making process more accurate and value-oriented.
This is the most essential difference between traditional BFSI players and fintech companies. The former possess huge volumes of financial data and yet have been unable to extract concomitant value from this most powerful of business resources. On the other hand, the latter has made smart use of the data that is made available to them to optimize business processes across the board, create market differentiation, and deliver better value to all stakeholders. The dedicated focus on smart utilization of data is the prime reason why fintech companies have been able to improve their RoIs, improve business agility, and minimize loan default rates.
Using data to identify the right customer and the right segment, at the right time
Fintech companies leverage underwriting models which take into consideration both traditional and non-traditional data points to create more accurate borrower profiles. This approach not only allows them to identify creditworthy borrowers who might be overlooked by traditional BFSI institutions but also enables them to offer lending products customized to the specific requirements of every prospective borrower.
New-age fintech platforms have also stolen a march over traditional lending institutions when it comes to capitalizing on unfulfilled market opportunities. Currently, several consumer segments in the country have been underserved or completely unserved by traditional BFSI players, including students, MSMEs, first-time borrowers, etc. This massive white space within the Indian economy is being addressed by fintech platforms through optimal data utilization. Access to insights from both traditional and non-traditional data points allows these new-age lenders to more accurately identify emerging credit demand across different sectors and from different consumer demographics, and cater to it with better products and services.
Data as an enabler: How data can help in the application, quantification, and identification of loans
In addition to optimizing the business operations of lenders, increased data access is also extremely beneficial to borrowers. Fintech platforms often use information such as the loan purpose and amount to assist prospective borrowers and guide them to the most relevant product based on their needs. This significantly streamlines the discovery process for the end-user and helps them zero in on the lending product that offers the most favourable terms.
Fintech platforms also use data to build relevant statistical models. These models are then applied to new loan applications and existing accounts to gain lightning-fast insights into the applicant’s credit behaviour. Doing so not only helps in identifying and rewarding good behaviour to drive customer loyalty but also allows fintech service providers to unmask bad credit behaviour. Armed with these insights, fintech players can take appropriate action – such as providing borrowers with loan top-ups or alerting the collections team – and root out fraudulent activity. Such data-driven operations are amongst the key factors why loan default rates on top fintech platforms are much lower than that of traditional BFSI companies.
There is no doubting that data is the lifeblood powering global business operations today. The BFSI industry, amongst the largest creators and aggregators of data, holds an extremely powerful resource in its hand in the form of data. Given the difference that it can make, utilizing this resource to its optimal potential must become the top priority for players within the domain, be it new-age fintech platforms or traditional institutions.