Financial services institutions are recognizing the potential of generative AI (GenAI) in optimizing operations, enhancing customer experiences and driving innovation. This article explores the impact of GenAI on the financial services sector, examining adoption trends, key use cases, challenges and the benefits associated with leveraging AI in this industry.

Optimization driving adoption

According to a Gartner Financial Services Research Panel survey, the financial services industry views GenAI as an optimization play, with a majority (49%) of senior business executives anticipating a moderate impact of this technology. Only a small percentage (2%) consider GenAI to have a disruptive impact in the short-term, highlighting the need for a step-by-step approach. 

Financial services institutions understand that they must “walk before they can run” and cautiously navigate the adoption process. This measured approach helps mitigate risks and ensures a smooth integration of GenAI into their operations.

Firms are discovering numerous benefits associated with AI adoption. According to Gartner’s 2023 AI Survey: CIOs and Technology Leaders View, the most prominent advantages include improved productivity and efficiency (75%), enhanced customer experience (60%) and reduced costs (54%). 

By harnessing the power of AI, financial services institutions can automate time-consuming manual tasks, streamline processes and free up resources to focus on more complex and value-added activities, such as more facetime with the customer.

Additionally, AI-driven insights enable personalized customer experiences, providing tailored recommendations and delivering relevant information in real-time. This enhanced customer experience strengthens customer loyalty and ultimately drives business growth.

Commenting on the significant opportunity ahead, Srinivasan Seshadri, Chief Growth Officer, Financial Services at HCLTech, said: “AI has the potential to transform financial services. For that to happen, financial institutions must transform across all layers of their capability stack. Organizations that recognize the value of AI and technology are moving toward a product-aligned operating model that combines talent, culture and ways of working to synchronize all layers of the stack. These institutions prioritize customer journey-led product development and bring people together to deliver solutions that customers value for sustainable growth.”

Key use cases in financial services

Financial services institutions are increasingly exploring various use cases for GenAI. One significant area of adoption is fraud prevention, where 13% of institutions are already implementing AI tools. 

According to Gartner’s 2023 Customer Experience and Trust Survey, ‘better security’ was the top reason why retail banking customers switched primary provider, before reasons such as ‘better interest rates’ which ranked in second place. 

Firms can leverage GenAI to proactively detect and prevent fraudulent activities. It's important to note that anti-money laundering (AML) and regulatory compliance are vital considerations in this domain, requiring tailored solutions and nuanced approaches.

Another prevalent use case for GenAI in financial services is code generation and conversion. By automating coding processes, institutions can save time, reduce errors and improve the efficiency of their software development operations. This enables faster deployment of applications and enhances the overall development lifecycle.

Contact center assistance is another area being explored. By leveraging AI-powered chatbots and virtual assistants, institutions aim to provide efficient and personalized support to customers, reducing wait times and improving overall customer satisfaction. However, financial services face unique challenges in this area, as compliance with regulations and maintaining customer security is crucial.

GenAI today

Several financial services institutions have successfully implemented GenAI in their operations, providing valuable insights into its potential. 

For instance, J.P. Morgan utilizes GenAI to analyze patterns in emails for fraud detection, enabling them to identify and prevent fraudulent activities effectively. Stripe, a leading payment platform, leverages GenAI to better understand customer usage patterns and provide customized support, while simultaneously combating fraudulent transactions.

Ally Bank takes advantage of GenAI to transcribe and summarize customer service calls, increasing efficiency and enabling quick issue resolution. Klarna, a popular e-commerce platform, has integrated ChatGPT plugin into their system to enhance the customer experience by offering personalized shopping advice and product recommendations.

In addition, Erste Bank takes a distinctive approach by using GenAI to build a personalized financial services companion, focusing on enabling customers to improve their financial health. This use case goes beyond internal operations and aims to provide customers with personalized learning resources to make informed financial decisions. The GenAI companion offers various formats, such as video and audio, to cater to individual preferences and learning styles.

Moving from pilot to production

Transitioning from pilot projects to full-scale production with GenAI use cases can present challenges for financial services institutions. One significant challenge is providing a view on the return on investment (ROI) of AI implementation. Pilot projects may not require strong outputs, but when it comes to broader integration, institutions need to demonstrate tangible benefits, such as increased revenue, improved efficiency and effective risk mitigation. Legal and compliance risks also play a role in decision-making, as institutions must ensure that AI technologies adhere to regulatory frameworks.

Determining what success looks like when implementing GenAI is a crucial component. Research is underway to provide comprehensive metrics and frameworks that can guide institutions in measuring the impact of GenAI in their operations. By leveraging these metrics, financial services institutions can better understand the value generated by GenAI and make informed decisions for future implementations.

Looking ahead to an AI future

Looking ahead, GenAI is predicted to become more prevalent in the financial services industry. Vendors in this sector expect increased adoption, with more institutions leveraging AI to enhance customer and employee experiences and optimize operations. However, whether this technology is going to disrupt the industry and drive financial services firms toward true business model transformation is yet to be determined.

Navigating GenAI implementation requires the expertise and support of GenAI-specific vendors. These vendors, such as HCLTech, play a crucial role in ensuring that financial services institutions stay updated on the latest advancements in GenAI and can explore new use cases effectively. Collaborating with such vendors helps institutions streamline their AI adoption process and stay ahead in the rapidly evolving landscape of GenAI.

GenAI holds immense potential for transforming the financial services industry. By embracing GenAI, financial services firms can not only optimize their operations and enhance customer experience but also expand beyond traditional value propositions and create new revenue streams. 

A measured approach to adopting GenAI allows institutions to navigate challenges, mitigate risks and unlock the full potential of AI in financial services. As the industry continues its exploratory phase, collaboration with GenAI-specific vendors will play a vital role in staying ahead of the competition and delivering exceptional customer experiences.