What are the technology trends that are catalysts for innovation in digital lending?

Fintech lenders, once disruptors, are now increasingly seen as innovators and enablers. Robotics, machine learning, and automated data analysis are the tools that will have the greatest impact on digital lending. While the technologies required to stimulate innovation are widely available and quite sophisticated, banks and NBFCs need to come up with innovative ways to leverage these technologies to address the current challenges facing India’s digital lending ecosystem.

Data on fees or other charges, similar lending rates and product availability around the clock make the lending industry a more commoditized one. As a result, lenders may need to enhance their competitive advantage to achieve their strategic goals and increase revenue. A digital onboarding process using Aadhar and the Video Customer Identification Process (V-CIP) can speed up turnaround time, reduce inappropriate spending and drive new customer acquisition. The accuracy and completeness 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, seemingly risky and credit-deficit segments opens the door for new-age digital lenders that can fill the void in double-quick time and build with a large customer base Connect (by employing cutting-edge technology techniques and alternative credit assessment models). Especially for microfinance and advances, which are most popular with new credit borrowers, credit assessment and loan disbursement response times are faster on digital platforms compared to traditional loans. The shift from asset-based to cash-flow-based data, and other ancillary data from sources such as telecommunications, utilities, and social media, combined with psychometric analysis to assess ability to pay and willingness to pay, is augmenting and often replacing traditional source services for social classes that lack credit.

In the lending space, customer acquisition has a lot of innovation in how lenders can reach new segments and lower fees. For example, digital lenders are using machine learning-based models to help them fine-tune product features and customer engagement strategies to drive customer acquisition.

With the introduction of cutting-edge technological tools, lenders now have real-time access to vast amounts of digital data to pinpoint and mitigate possible loan risks. Although ML-based alternative credit scoring models increase loans, they may inadvertently miss some customer segments due to model bias and insufficient training data. This is due to a lack of historical credit cycle data on borrowers. Digital lenders also need to be wary of developing black-box ML models, as it is impossible to backtest them to validate them. This is significant as authorities may intervene in sensitive sectors such as lending to protect the interests of consumers. To put things together, lenders need a solid understanding of how ML models evolve and the ability to choose their specifications wisely over a series of credit cycles.

In a similar fashion, off-balance sheet or “leased NBFC” models where lenders offer certain credit enhancements, such as first-loss guarantees up to a predetermined percentage of the loans they generate, have a higher potential for risk accumulation. These entities are not yet under the supervision of the RBI. In addition, a large number of unregulated market players and fintechs are taking direct balance sheet risk as financial institutions partner with different fintech companies. To proactively analyze customer risk and control the dangers of financial fraud, banks and NBFCs have started integrating digital touchpoints into their existing frameworks.

The current framework used by banks and NBFCs continues to operate in silos even though digital touchpoints have begun to be adopted; this results in an inability to fully leverage the intelligence gained from numerous surveillance platforms. Numerous digital touchpoints connecting different risk categories can provide customers with a comprehensive and insightful risk score (single-view risk profile), enabling them to make informed decisions on loan tenure. Real-time behavior recognition capabilities and rules engines may need to be upgraded to better detect anomalous transactions.

While India still has a long way to go before formal finance is ubiquitous in India, embedded lending and cloud now have a great opportunity to penetrate the market and democratize credit. The use of the cloud in digital lending opens up seemingly endless potential for businesses. Increase remote access, flexible subscription models, reduce data storage costs, and more. is one of the main advantages of using the cloud. Automated software upgrades replace the time-consuming, laborious upgrade process that has historically stressed lender IT departments. With the cloud, banks have the flexibility to offload their services on-premises, allowing most of their capital investment to improve product offerings and customer experience, as well as expand their lending business. Banks that move to the cloud may be nimble enough to scale up, launch products faster and enter new markets as the company grows.

By Jyoti Prakash Gadia, Managing Director, Resurgent India

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