Fintech lenders, ever the disruptors, are now increasingly seen as innovators and enablers. Robotics, machine learning, and automated data analysis are among the tools that have had the greatest impact on digital lending. Although the technologies needed to spur innovation are widely accessible and quite sophisticated, banks and NBFCs will still need to come up with innovative methods to employ these technologies to address the challenges that the Indian digital lending ecosystem is now faced
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What are the technology trends as a catalyst for digital lending innovation?
4 min read 31 Dec 2022
Prakash
Although the technologies needed to spur innovation are widely accessible and quite sophisticated, banks and NBFCs will still need to come up with innovative methods to employ these technologies to address the challenges that the Indian digital lending ecosystem is now faced with.
Although the technologies needed to spur innovation are widely accessible and quite sophisticated, banks and NBFCs will still need to come up with innovative methods to employ these technologies to address the challenges that the Indian digital lending ecosystem is now faced with.
Fintech lenders, ever the disruptors, are now increasingly seen as innovators and enablers. Robotics, machine learning, and automated data analysis are among the tools that have had the greatest impact on digital lending.
Fintech lenders, ever the disruptors, are now increasingly seen as innovators and enablers. Robotics, machine learning, and automated data analysis are among the tools that have had the greatest impact on digital lending. Although the technologies needed to spur innovation are widely accessible and quite sophisticated, banks and NBFCs will still need to come up with innovative methods to employ these technologies to address the challenges that the Indian digital lending ecosystem is now faced with.
The availability of data on fees or other expenses, similar loan rates, and products around the clock has made the lending industry a more sector. Lenders, consequently, may need to sharpen their competitive edge to meet their strategic objectives and shore up revenues. Digital onboarding using and Video Customer identification Processes (V-CIP) can speed up turnaround time, reduce unmerited expenses and drive new customer acquisition. The accuracy and integrity of customer data can be significantly improved by using artificial intelligence (AI) and facial recognition technology.
Traditional financial institutions' reluctance to lend to low-income, seemingly risky and credit-deficient segments has opened the door for new-age digital lenders to fill the gap in double quick time and connect with a sizable customer base (by employing cutting-edge technology and alternative credit assessment models). Particularly for small-ticket credits and advances, which are most popular among new-to-credit borrowers, credit evaluations and loan on digital platforms have faster response times compared with traditional loans. The transition from asset-based data to cash flow-based data, along with other auxiliary data from sources like telecom, utilities, and social media, combined with psychometric analysis to assess the ability and willingness to pay, is strengthening and routinely displacing conventional sources to serve the credit-starved segments of society.
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