Gen AI To Boost Indian Financial Services, May Add $80 Billion To GVA By 2030: EY Report

Around 61% of financial service firms expect generative artificial intelligence to greatly improve the value chain's efficiency and market responsiveness, according to EY.

The report—The AIdea of India: Generative AI’s Potential to Accelerate India’s Digital Transformation—highlighted that financial firms are recognising the potential of gen AI, with 78% of survey respondents having either implemented the technology in at least one use case or have plans to pilot it over the next 12 months. 

The report also showed that gen AI's impact on the gross value added within financial services ranges from 22% to 26%. Hence, gen AI could potentially add $66–80 billion to the GVA by 2030.

“In India's fast-evolving financial services sector, gen AI is a game-changer, fostering innovation and competitiveness. It promises growth, efficiency and enhanced customer experiences. However, responsible adoption is key, ensuring ethical data use and transparency with customers,” said Abizer Diwanji, head, financial services, EY India. 

A majority of financial firms in the survey highlighted their focus on two areas: customer service and cost reduction. When asked about the areas of business that gen AI would impact, 94% of firms mentioned customer experience, followed by 78% citing cost reduction. Of the respondents, 61% believed it would have the most impact on driving innovation. 

“Globally, financial institutions foresee a timeframe of 5 to 10 years to fully leverage the potential of gen AI," said Pratik Shah, partner and financial services consulting leader at EY India. "In alignment with this, Indian financial services organisations are proactively forming specialised cross-functional teams and allocating dedicated funds for gen AI deployments.”

Firms are investing in areas that offer tangible and readily achievable benefits. When asked about executing their gen AI strategy, 83% of respondents said they plan to partner with external tech providers, whereas 67% expressed confidence in developing large language models in-house.

According to the report, financial institutions are generally exploring only seven to 10 crucial use cases on average. Of the respondents, 44% have emphasised that identifying use cases and skills gap are among the primary obstacles to gen AI implementation. The bigger challenge for financial organisations is the enhanced regulatory scrutiny surrounding governance and standards around building LLMs, data privacy, model validation and financial data cybersecurity.

During the survey, financial sector leaders cautioned that each strategy entails a certain amount of risk. Financial institutions have embarked on their gen AI journeys, but concerns regarding large-scale use cases, technological maturity and the risks prevail. According to the report, organisations’ functional and technical divisions should work together, aligning with leadership’s long-term strategy, to ensure successful gen AI implementation.