The BFSI industry is showing great interest in using generative AI as part of its technology ecosystem. Offering enhanced fraud detection, personalized Customer Experience (CX), and robust risk assessment, generative AI is set to significantly impact various aspects of the BFSI value chain.
In this report, we examine the key factors driving generative Al’s adoption in the BFSI industry. We elaborate on how it can help achieve business objectives and gain a competitive advantage in various business operations across BFSI firms. We also examine contemporary implementations and real-world examples of gen Al adoption in the industry.
The report also presents a framework for prioritizing use cases to enable organizations to identify the most impactful applications of gen Al across banking, capital markets, and insurance, as well as looks at the initiatives of technology and service providers operating in this domain.
Scope
Industry: BFSI
Geography: global
Technology: generative AI
Contents
In this report, we share:
An overview on trends, adoption drivers, and challenges related to generative AI in the financial services industry
A framework to assess and prioritize gen AI use cases across the financial services value chain
Potential risks associated with the adoption of gen AI in the financial services sector, mitigation strategies, and best practices to address these risks and ensure the responsible and ethical deployment of gen AI
Trends related to gen AI-related talent acquisition, training, and upskilling, as well as the associated costs and investment considerations
An industry landscape covering the technology and service providers specific to gen AI in BFSI
