By Yogendra Singh, Partner, Protium
Big data, a complex dataset sourced from different data channels, offers fresh opportunities to overcome seemingly insurmountable business challenges.
Globally, the financial sector is undergoing a significant transformation driven by the expansion of big data. By the end of 2024, global data generation is projected to surge from nearly 120 Zettabyte (ZB) in 2023 to 147 ZB. In India, the financial sector is also witnessing profound changes propelled by this growth. The influx of data has elevated big data into a critical asset, driving innovation, enhancing decision-making processes, and revolutionizing lending practices.
Big data comprises vast and intricate data sets, which are often sourced from new data channels. These datasets are too extensive for traditional data processing software to offer opportunities to address business challenges that were previously unattainable. Traditionally, lenders relied on limited sources such as financial statements, credit scores, and historical data to evaluate businesses’ creditworthiness.
However, the digital transformation era, supported by technology-driven systems and algorithms, has simplified lending processes. Utilizing data and analytics, digital platforms swiftly evaluate the creditworthiness of businesses and disburse funds within minutes.
But what makes big data essential for digital lending?
Big Data represents an expansive repository of information that continues to expand every day. It is distinguished by its substantial volume, velocity, and diverse variety of data, all subjected to thorough processing and analysis. In the financial sector, big data analytics is critical, empowering lenders to integrate an extensive array of data points. These can include transactional data from the point of sale, industry-specific data, biometric information, and alternate financial data. This integration enriches the credit evaluation process, enhancing its accuracy and effectiveness.
The utility of big data expands beyond mere credit assessment, encompassing predictive analysis, customized lending, and fraud detection. Notably, customized lending is crucial, as it enhances customer retention and helps in offering bespoke financial services to meet business preferences and needs.
Moreover, predictive analytics is being used in the financial sector to forecast cash flow fluctuations for businesses. This allows financial institutions to anticipate periods of cash surplus or shortfall for their clients and provide proactive financial solutions, such as flexible repayment schedules or lines of credit, to assist them manage their cash flow effectively. Additionally, by identifying potential cash flow challenges in advance, financial institutions can mitigate risks associated with late payments or defaults and maintain healthy relationships with their clients.
A significant advantage of big data in lending is its ability to collect real-time and unconventional data sources such as point-of-sale transactional data, customer sentiment analysis, and industry-specific data. Furthermore, by leveraging alternative data sources and employing data analytics techniques, lenders can extend their service reach and incorporate more businesses into the official credit system.
Adoption of big data by Indian MSMEs/ Indian MSME embracing big data
The global big data market is expected to have a size of $229.4 billion by 2025, with a CAGR of 10.6%. In parallel, the Indian market is expected to reach $2.17 billion in 2024—testimony to the growing importance of data across various sectors, notably in MSME lending. However, MSMEs encounter a significant financing challenge in demonstrating financial responsibility due to their limited or non-existent credit histories. This contributes to the existing credit gap of INR 22.6 lakh crore.
To tackle this, NBFCs and fintechs utilize Big Data analytics methods to evaluate entrepreneurs’ creditworthiness by tapping into alternative data sources beyond conventional Bureau Scores. With India’s fintech market poised for significant growth, projected to reach INR 11.36 trillion by 2028, the surge in digital lending driven by rapid technological advancements is noteworthy. This is especially crucial for MSMEs needing swift access to funds to capitalize on opportunities or overcome unexpected hurdles.
Moreover, technological advancements have the potential to revolutionize debt collection practices through significant trends in AI and Big Data analytics, enhancing ROI and promoting ethical interactions between borrowers and lenders.
Integrating Big Data analysis with AI-enabled services can expedite the lending process by efficiently identifying and addressing borrowers’ pain points, such as lengthy paperworks, prolonged approval processes, and uncertainty regarding eligibility criteria. These technologies provide lenders with comprehensive insights into borrowers’ debt history, facilitating streamlined debt collection processes and fostering better borrower-lender relationships.
All in all, as AI-driven technology and data analytics continues to evolve, it holds promise for advancing financial inclusion, driving economic growth, and empowering MSME borrowers with essential supporting tools that could fuel their success.