Non-banking Finance Institutions: Big Data Assisting Small Borrowers

Non-banking financial companies (NBFCs) have developed a name for themselves as a strong rival to India's major banks and were instrumental in making financing available to the Micro, Small and Medium Enterprises (MSME) sector.  

Technology always has been a key facilitator in helping these financial institutions grow and foster consumer involvement and pleasure. In order to get ready for a future driven by technology, NBFCs are concentrating on innovation, and location intelligence has emerged as one of the main forces behind their growth strategy.

According to Ashwani Rawat, co-founder and director of Transerve, a rapidly expanding location intelligence company that uses geospatial technology to enable digital transformation and effective decision-making, location intelligence is a crucial component for NBFCs to analyse data spatially and gain better insights for any potential market discovery. The value chain of lead generation, customer onboarding, credit or loan disbursement, and collection has begun to be embraced by new-age NBFCs through the use of technology and collaboration ecosystems. The last-mile reach and delivery, fraud detection, regulatory compliance, alternative credit models, enterprise automation for accounting, treasury, and reconciliation for traditional NBFCs are just a few of the products and services that FinTech companies are using, according to Rawat.

Through the services it offers NBFCs, Transerve has been successful in establishing a niche for itself. Many modern NBFCs have worked with us to create products for today's consumers, according to Rawat. They are using analytics and big data with our assistance to offer 24/7 customer service and support.

He uses the leading app-based P2P lending platform as an illustration, demonstrating how it uses big data analytics to evaluate applicants based on their addresses. "This company can categorise applicants by utilising a Layer Data Access API that creates a "Risk" rating based on an applicant's address or home area. The approval procedure has been streamlined for low-risk applications, while more checks have been added for mid- and high-risk applications. This helps to reduce losses," he says.

According to Hemant Vishnoi, co-founder of EnKash, the limited penetration of financial services is caused by the fact that banks and NBFCs traditionally base their choices on human intelligence. Critical risk measures were being missed in the standard underwriting models due to limited data access. However, modern NBFCs are reducing risks by utilising data science and analytics, which may be used to analyse consumer behaviour and create a different credit model. The author continues, "New-age NBFCs are expanding their customer base with minimal acquisition costs by investing in new-age technologies and data intelligence.