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

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 commoditised sector. Lenders, therefore, may need to sharpen their competitive edge to meet their strategic objectives and shore up revenues. Digital onboarding using Aadhar 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 disbursals 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.

In the lending space, customer acquisition has quite a number of innovations in terms of how lenders are reaching out to new segments and bringing down expenses. For instance, ML-based models are being used by digital lenders to assist them in tuning product features and customer interaction strategies to drive customer acquisition.

With the introduction of leading-edge tech tools, lenders now have real-time access to enormous volumes of digital data to pinpoint and reduce possible lending risks. Although ML-based alternative credit scoring models have amped up lending, they may inadvertently omit some customer segments due to model biases and inadequately trained data. This results from a lack of historical credit-cycle data on borrowers. Digital lenders also need to be vigilant about developing black-box ML models since it would be impossible to validate them through back-testing. This assumes significance as authorities are likely to intervene in a sensitive sector like lending to protect consumers’ interests. To pull things together, lenders would require a thorough understanding of how ML models have evolved and the ability to judiciously pick their specifications throughout a range of credit cycles.

In like manner, the off-balance sheet or “rent-an-NBFC” model, in which the lender offers certain credit enhancement features, such as a first loss guarantee up to a predetermined percentage of loans generated by it, has a higher potential for risk build-up. These entities are not yet under the regulatory purview of the Reserve Bank of India. Furthermore, with the financial institutions working with different fintech companies, a large number of unregulated market participants and fintech are assuming direct balance sheet exposures. To proactively analyze customer risk and control the hazard of financial malfeasance, banks and NBFCs have begun integrating digital touchpoints into their existing frameworks.

The current frameworks used by banks and NBFCs continue to operate in silos even though they have begun employing digital touchpoints; this results in less-than-optimal utilization of the intelligence obtained from numerous monitoring platforms. Connecting the numerous digital touch points for different risk categories may offer clients a comprehensive and insightful risk score (a one-view risk profile), enabling them to make informed decisions over the term of the loan. Real-time behavior recognition capabilities and rule engines may need to be upgraded to better detect anomalous transactions.

Although India still has a long way to go before formal finance is commonly used in India, there is a great opportunity for embedded lending and the cloud to penetrate the market now and democratize credit. The use of the cloud in digital lending presents businesses with seemingly endless potential. Increased remote access, a flexible subscription model, decreased data storage costs, etc. are among the key benefits of using the cloud. Automatic software upgrades have substituted for time-consuming, strenuous upgrading processes that have historically put a strain on the IT departments of lenders. With the cloud, banks have gained the agility to move their services off-prem, freeing up the majority of their capital investment for improving product offerings, and client experiences, and for the expansion of their lending businesses. Banks that switch to the cloud may be flexible enough to scale up as their companies grow, launch products more quickly, and enter new markets.

Author: Jyoti Prakash Gadia, Managing Director at Resurgent India

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