Wealthtech sector takes to AI models to rewire business

The rapid development around large language models (LLMs) and artificial intelligence is finding early adoption in the fast-growing wealthtech sector in India. As tech startups in this sector are looking to go beyond robo advisory and algorithm-based trading which have already become the norm, innovative founders are experimenting with advanced AI models like ChatGPT to find better ways of processing market information and servicing clients.

“We have built an AI-powered recommendation engine, and it is reaching a level of efficiency where it is possible to provide services through voice-based commands from users. We are incorporating generative AI into various processes, including prospect identification, recommendations, and content dissemination for learning and development for our advisors,” said Manu Awasthy, chief executive officer of Centricity.

Centricity will have a 20-member team dedicated to working on various generative AI tools, Awasthy added. Lightspeed India Partners backed Centricity primarily caters to the investment needs of high-net-worth individuals.

Industry insiders said that while AI is being used across multiple functions by startups, these are still very early days. There is a shortfall in data around conversations between relationship managers and investors. The lack of access to such data means AI powered investment advisors will be difficult to build right away, they said.

“In the US, a lot of work is happening around learning non-linear regression models using AI and the development of Large Language Models (LLMs) specific to the finance domain, where unstructured financial reports and documents are being fed into AI. In India, the work on LLMs is primarily around the applications of generic LLMs, like conversational AI, as of now,” said Satya Gautam Vadlamudi, cofounder of Elystar Investment Management, a Mumbai-based Sebi registered advisory firm.

IndMoney is using AI across multiple fields like consuming large volumes of market reports and news pieces to convert them into coherent and easy to consume snippets on mobile devices and generating financial insights from market trends and research documents.

“There are some internal use cases for genAI as well. One major use case is the productivity part of the team members. We encourage developers to use the AI copilots, mostly Github copilots,” said Dhruv Pathak, chief technology officer, IndMoney.Bengaluru-based wealthtech startup Stable Money is looking to use AI to make interactions with the customers smoother and more efficient.

“Frequently asked questions are what we need to solve using GenAI where people can get their queries resolved through conversations, but the rest of the things like KYC and such will remain core to the app which I think will be tough to solve through GenAI,” said Saurabh Jain, cofounder, Stable Money

Mumbai based discount broking platform mStock, backed by South Korean financial major Mirae, is also looking at AI to bring in standardisation in its responses to customers and is looking to bring in timeliness in responses.

“We are implementing a lot of language models in the servicing side of things, but will we get AI based fund managers? I think that will take some time,” said Arun Chaudhry, chief business officer, mStock.

There is a massive scope of AI in financial services, but there are multiple areas which require better understanding like data security and client confidentiality.

In a consultation paper, released on August 6, Sebi said investment advisors must add a disclaimer in case they are using AI to suggest investment products to their customers. The regulator has suggested that data security and compliance aspects of the business will need to be managed by the registered advisors only.

Also read: AI adoption in key Indian sectors touches 48% in FY24