There is visible excitement in the market about newer models of generative AI (Artificial Intelligence). Across sectors, engineers are building products that can effectively utilize the power of Large Language Models (LLMs). The imperative is not just limited to integrating a chatbot into consumers and enterprise user journeys but about how it can fundamentally change business models and amplify product innovation. Financial services form the largest sector by market cap and are systemically important for the economy. Fintech companies have already adopted digitization and machine learning to rethink distribution and underwriting for retail users and small and medium businesses (SMBs). The addition of generative AI to this mix can enable a significant expansion of the addressable market for more sophisticated products.

At a very high level, the breakthrough in LLMs is impacting businesses and user flows on three dimensions: interfaces, decision-making, and operational efficiency. At this point, we’re seeing most businesses focusing on the third vector, largely increasing engineering and marketing team productivity by using functional copilots. In my view, the biggest impact we are likely to see in financial services is on the interface layer, i.e., the distribution of hard-to-understand, high barriers to trust financial products.

As a country, we continue to see extreme under-penetration of wealth and insurance products because the entry barrier to awareness and education is very high. More than 80% of Indians have bank accounts, and around ~2.5% of the population has invested in mutual funds. A majority of households prefer “safer” investments such as fixed deposits (FD) while ignoring potentially high-return instruments such as mutual funds. India’s insurance penetration is just 4.2% and out-of-pocket health expenses are 47% of the total health expenditure.

Both of these products require making an upfront payment for some value that might or might not yield in the long run. To make matters worse, most of these products are still packaged and sold in a way that makes it harder for a layperson to understand and make high confidence decisions, thereby forcing them to keep most of their wealth surplus in FDs or savings accounts. Consequently, they’re high trust, high friction product categories. This is not just restricted to Tier 2/3 cities or young professionals. I am aware of instances where senior working professionals, the category that is typically considered financially aware, allocate their disposable income to FDs and have a large amount of money sitting in extremely low-yield savings accounts.

The biggest question facing these products has always been: “How do I build trust?” This has traditionally been solved by people who spend time with potential customers in person, understand their context, take the time to build a relationship and educate them in a way they understand the value of managing their money more wisely. The cost of this high-touch sales process is, by definition, high. Hence wealth advisors have restricted their services to a customer segment with a higher income and consequently a higher capacity to spend and invest. These businesses largely grew through existing bank relationships or word of mouth. In fact, banks contributed ~55% to the first-year individual premiums of private life insurers in 2021-22, according to the IRDAI Annual Report. GenAI changes this by crashing the cost of education and dramatically expanding the addressable market.

Imagine an all-knowing wealth advisor, armed with all the information and fine print about all the financial products you can invest in. It talks to you in a language you understand, keeping your level of financial literacy and personal cash flow context in mind. It answers any questions you have with simple, unbiased responses and takes away the pain of reading through long documents filled with legalese. And companies don’t even need to recruit and train an army of agents across the length and breadth of the country to make this vision a reality. AI, as any software offering, scales infinitely and delivers this service in a cost-efficient manner, making it possible to sell financial products that were previously simply unviable. GenAI could do to wealth-tech and insure-tech what digitization did for credit and payments in the country.

Beyond consumer education, there’s potential to considerably level up product innovation by using this documented user feedback to build products that are more suited for the unique needs of the new segments adopting these services. We also expect to see improvements in underwriting, recommendation engines, and fraud management for financial services companies, however, those developments are likely more incremental in nature, in contrast with the disruption in consumer education and sales process that awaits us.

The financial opportunities emerging from GenAI’s usage tie well with the government’s endeavors to boost savings and investment. The government has sought to facilitate an insured society through life, accident, and health insurance schemes. The “Insurance for All by 2047” vision envisages electronic insurance distribution and sachet insurance products customized for individual needs. Simultaneously, the government has introduced measures such as the T+1 settlement cycle in the equity market and e-wallets in mutual funds to bolster quicker transactions. These measures resulted in aggressive growth in demat accounts. The total number of demat accounts in India touched 114 million in March 2023.

Digital delivery of financial services has become the key focal point of government initiatives. India strives to build a $1 trillion digital economy by 2025-26 and financial empowerment forms a critical component of this vision. GenAI can help bridge the accessibility gap and democratize financial services. The pandemic nudged a large part of the country to go digital. But it is the amalgamation of AI and fintech that could truly take India to the forefront of the digital revolution.

LeackStat 2023