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In today’s fast-paced business environment, access to timely credit is crucial for small businesses to thrive. Thanks to the advent of new-age digital lenders, small business owners in India can now receive loans in as little as 2 days—a sharp contrast to the traditional loan processing timeline. The availability of various types of digital transaction data for businesses has significantly reduced the time it takes to access funds, making borrowing faster and more efficient.
Traditionally, securing a loan could take anywhere from two weeks to a month, relying heavily lengthy processes. However, the lending landscape is being revolutionized with the integration of analytics and enhanced decision-making scorecards. These scorecards utilize digital transaction data and information from ecosystem enablers such as DigiLocker and the Account Aggregator framework, drastically shortening the loan approval process.
While these advancements allow for quick loan processing, companies are increasingly adopting hybrid models too. In such cases, digital scorecards are combined with physical checks and manual intervention to ensure a thorough risk assessment. This blend of automation and human oversight enables lenders to maintain a robust evaluation process.
With these evolving practices, what once took weeks can now be done in mere hours or within 2 days. The ease and speed with which small businesses can now access credit are not only empowering entrepreneurs but also transforming the financial ecosystem by making capital more accessible.
Data and technology have been the two key enablers of digital lending. Digital lending processes, built on various types of consumer data, have significantly expedited loan disbursement for micro, small, and medium enterprises (MSMEs).
Data, coupled with advanced technology, has not only eased lending to MSMEs but also improved access to credit. For instance, a trail of digital payment transactions from MSMEs helps lenders better understand their financial health and assess creditworthiness. Such data-enabled mechanisms are playing an important role in financial inclusion by extending loans to underserved segments.
Digital Lending
Technological advancements and data integration are revolutionizing the lending landscape, making it easier for customers to apply for loans through seamless, end-to-end digital journeys. The digital environment has evolved significantly in recent years, thanks to innovations such as Account Aggregator, DigiLocker, eKYC GST portal. These tools not only reduce friction in customer onboarding but also ensure faster turnaround times (TATs) by providing verified data from trusted sources, all while enhancing the overall customer experience.
Digital lending empowers small businesses by fast-tracking loan approvals and disbursements, ensuring that funds are transferred to customers’ bank accounts quickly. This quick access to capital is vital when urgent working capital is required. AI and ML-based credit scoring models leverage a wide array of data, including credit bureau reports, GST filings, and banking records. These advanced models offer a holistic view of customer risk, enabling lenders to make more accurate decisions and identify high-potential borrowers, even accounting for the seasonality of businesses. As a result, previously underserved or excluded MSMEs now have greater access to credit.
According to the NeoGrowth’s NeoInsights Business Confidence Survey 2024, 60% of surveyed MSME owners look to invest in technology and therefore the demand for seamless and faster digital loan processes is only increasing. Lenders must also support MSMEs through digital adoption and reinforce trust by maintaining strong data security throughout the process.
Data-Based Informed Decision-Making & Digital Underwriting
AI and ML-driven early warning models are transforming how financial institutions manage risk, enabling them to take proactive measures and streamline collections. By identifying potential risks early, these models allow lenders to mitigate defaults and minimize losses effectively.
AI/ML-powered underwriting processes have also revolutionized loan approvals, offering speed and precision. By analyzing transaction data from digital payment modes, lenders can quickly review credit histories, assess business cash flows, and evaluate repayment capacity. This comprehensive assessment of a business’s financial health and creditworthiness allows for more informed lending decisions.
Additionally, NBFCs are leveraging AI/ML-based credit scorecards and customer data analysis to make smarter, data-driven lending choices. These tools provide insights into a customer’s repayment behaviour and business transactions, resulting in more accurate loan assessments, reduced default risks, and optimized loan portfolios—enhancing overall operational efficiency.
Tailored Products and Services:
AI and ML-powered analysis of customer data enables lenders to gain deep insights into the unique needs of each MSME borrower. By understanding their cash flow patterns, business dynamics, and risk profiles, NBFCs can offer customized loan products that align with the specific financial requirements of MSMEs. Fintech platforms, leveraging advanced digital capabilities, can tailor products to meet the precise cash flow demands of MSMEs, offering customisation basis the business needs and cycles.
With end-to-end digital loan journeys, digital lenders provide minimal documentation and quick turnaround times, making the process seamless for MSME borrowers. Flow-based lenders also introduce unique repayment options, such as daily repayment models with multiple payment modes, further simplifying access to credit and enhancing the borrowing experience.
Conclusion
The evolution of digital lending, driven by AI, ML, and vast datasets, has empowered lenders to deliver faster, more efficient loan solutions to MSMEs. By utilizing digital transaction data, credit histories, and cash flow patterns, lenders can make well-informed decisions that reduce risks and enhance financial inclusion for small businesses.
However, as advanced as digital lending has become, manual intervention still plays a critical role, while AI-driven models and data analytics provide speed and convenience, human oversight ensures a deeper, more nuanced evaluation that algorithms may miss. The combination of data and manual checks creates a more robust lending process—one that not only accelerates access to credit but also maintains thorough risk assessments.
As digital lending continues to evolve, a balanced approach of data-enabled automation and manual diligence will ensure the best outcomes for both lenders and MSMEs, driving growth and financial inclusion for the unserved and underserved.
