Barely a few years ago, people without credit history were ineligible for loans. However, the advent of fintech firms backed by AI-powered tools has completely changed the lending landscape. Although two million people remain unbanked or under-banked globally, artificial intelligence is helping predict their behaviour and assess creditworthiness based on online activity.

Besides individuals, this is especially beneficial for MSMEs (micro, small and medium enterprises) that were earlier outside the purview of organised lending. With more than 63 million small enterprises spread across India, these businesses contribute 24.63 per cent to India’s overall GDP. As per a BlinC Invest report, India faces a credit gap of ₹25 trillion, which makes scaling and expanding operations most challenging for MSMEs without quick access to institutional finance.

All of this is changing as fintechs deploy AI algorithms. By swiftly scrutinising massive amounts of data, AI assists in pinpointing specific behaviour patterns and preferences of loan applicants while assessing their risk profile. Lenders then make well-informed lending decisions while anticipating the needs of individual customers. In the case of businesses, the captive data is used to offer customised financial products catering to their distinct needs.

In this way, AI-enabled solutions are ensuring easy access to credit while making bill payments seamless and accelerating the turnaround times for loan disbursal. Speedy sanctions of credit are now the norm as digital lenders have automated manual processes with AI algorithms. What’s more, AI algorithms can be programmed to offer pre-approved loans to eligible customers. AI automated processes have also eliminated several barriers, including cumbersome paperwork.

Extensive collaboration

Today, fintechs and traditional lenders such as banks and NBFCs are collaborating extensively in lead generation and co-lending. The symbiotic relationship provides banks with new customers at zero operating costs while fintechs gain from a wider platform and enhanced lending capacity. This collaborative model has generated some innovative products and services.

While legacy lenders can fund new-to-credit customers with the analysis of their buying behaviour trends, new-age players leverage countless data points to assess the creditworthiness of loan applicants. Likewise, small entrepreneurs in hinterland regions are benefiting from enhanced cash-flow lending.

Though these benefits are laudable, one must also be aware of the unique risks posed by AI. The RBI has warned banks, NBFCs and fintechs to recalibrate pre-set algorithms that use AI and ML (machine learning) tools to safeguard against the attendant risks. The central bank has advised caution in using pre-set algorithms, given the inherent risks from human bias in such systems.

Therefore, AI models must be tested, retested and recalibrated periodically to avoid issues such as exclusions and biases due to machine decision-making. If precautions are not taken, undue systemic risks may build up in the system, as information gaps lead to dilution of underwriting norms.

Nonetheless, since the industry is aware of the concurrent risks, efforts are underway to address them. For example, as part of the risk management framework to manage the human bias issue in AI/ML systems, financial institutions could create systemic safeguards that detect and eliminate or reduce the biases. Similarly, AI/ML models must be monitored constantly to counter the increased threat of cybercrime, including the manipulation of data to trigger incorrect decisions.

Additionally, AI/ML can cause new privacy problems. For instance, AI/ML could unmask anonymised data via inferences, recall information about individuals and leak sensitive data either by inference or directly. Consequently, legal and regulatory frameworks are needed to address privacy and security issues. If these challenges are addressed, AI-driven systems can offer financial entities significant cost savings, more efficiency gains, penetration of new markets, better risk management and enriching experiences for customers.

Finally, by providing faster credit to individuals and enterprises in rural regions, AI can drive faster financial inclusion pan-India. Simultaneously, it can promote greater compliance as well as unmatched operational efficiencies in 2024 and beyond. Without a doubt, the best of AI is gradually unfolding.

The writer is CEO and Co-founder, Biz2X

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