What are the technology trends as a catalyst for digital lending innovation?

Ever the disruptors, fintech lenders are now increasingly seen as innovators and enablers. Robotics, machine learning and automated data analytics are among the tools that have had the biggest impact on digital lending. While the technologies needed to drive innovation are widely available and quite sophisticated, banks and NBFCs will still need to develop innovative methods to use these technologies to address the challenges that India’s digital lending ecosystem is now facing.

24/7 availability of data on fees or other expenses, similar loan rates and products has made the lending industry more of a commodity industry. Consequently, lenders may need to strengthen their competitiveness in order to achieve their strategic goals and increase revenue. Digital implementation through Aadhar and Video Customer Identification processes (V-CIP) can speed up turnaround time, reduce unnecessary expenditure and drive new customer acquisition. The accuracy and integrity of customer data can be significantly improved through the use of artificial intelligence (AI) and facial recognition technology.

The reluctance of traditional financial institutions to lend to low-income, perceived risky and credit-deficient segments has opened the door for new-age digital lenders to double-quickly fill the gap and connect with a large customer base (through advanced services). technology and alternative credit scoring models). Especially for small-ticket loans and advances, which are most popular among new borrowers, credit evaluations and loan disbursements on digital platforms have faster response times compared to traditional loans. The shift from asset-based to cash-flow-based data, along with other complementary data from sources such as telecommunications, utilities and social media, combined with psychometric analysis to measure ability and willingness to pay, is strengthening and regularly displacing traditional sources . to serve the credit-deprived sections of the society.

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In lending, there is quite a bit of innovation in customer acquisition in terms of how lenders reach new segments and reduce expenses. For example, digital lenders are using ML-based models to help them adjust product features and customer interaction strategies to drive customer engagement.

With the latest technology tools in place, lenders now have real-time access to vast amounts of digital data to accurately identify and mitigate potential lending risks. Although ML-based alternative credit scoring models have increased loan origination, they may inadvertently miss some customer segments due to model bias and under-trained data. This is due to the lack of historical credit cycle data on borrowers. Digital lenders should also be wary of developing black-box ML models as they would be impossible to validate through backtesting. This is important because authorities are likely to intervene in a sensitive sector such as lending to protect the interests of consumers. To sum things up, lenders would need a thorough understanding of how ML models have evolved and the ability to intelligently choose their specifications over different credit cycles.

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Similarly, the off-balance-sheet, or “rent-an-NBFC” model, where the lender offers certain credit-enhancing features, such as a first-loss guarantee up to a predetermined percentage of the loans it originates, has more potential. for increasing risk. These entities are not yet regulated by the Reserve Bank of India. In addition, as financial institutions work with various fintech companies, a large number of unregulated market participants and fintechs bear direct balance sheet risks. To proactively analyze customer risk and control the risk of financial malfeasance, banks and NBFCs have started integrating digital touchpoints into their existing frameworks.

The current frameworks used by banks and NBFCs continue to operate in silos even though they have started using digital touchpoints; as a result, the information obtained from many monitoring platforms is used less than optimally. Connecting the many digital touchpoints for different risk categories can offer customers a comprehensive and insightful risk score (a single-view risk profile), allowing them to make informed loan term decisions. Real-time behavior recognition capabilities and rules engines may need to be upgraded to better detect anomalous transactions.

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While India still has a long way to go before formal finance is widely used in India, there is a great opportunity for embedded lending and the cloud to enter the market now and democratize credit. Using the cloud in digital lending gives businesses seemingly endless potential. Increased remote access, flexible subscription model, reduced data storage costs, etc. is one of the main advantages of using the cloud. Automatic software upgrades have replaced the time-consuming, tedious upgrade processes that have historically put a strain on lenders’ IT departments. With the cloud, banks have been able to move their services off-premises, freeing up most of the capital investment to improve product offerings and customer experience, as well as to expand their lending business. Banks that move to the cloud can be flexible enough to expand as businesses grow, launch products faster and enter new markets.

By Jyoti Prakash Gadia, Managing Director, Resurgent India

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