The financial services industry has navigated constant waves of transformation, from the digital revolution to the move to cloud services to the provisioning of big data and now the ever-evolving field of artificial intelligence (AI).

With the cost to train and deploy AI models reducing dramatically, the AI market is primed for rapid growth. It is estimated that the generative AI market was valued at US$208 billion in 2023 and is expected to reach $1 trillion total market value within the next five years. Investment flows into AI startups in the past six months are estimated at more than $10 billion, up nearly 50% from the start of 2023. One unexpected beneficiary of this growth has been the island nation of Anguilla, who owns the .ai domain. Registrations of the .ai domain have surged in the past 12 months, netting the island a healthy revenue stream. Generative AI is touching the world in unexpected ways, and the much anticipated fourth industrial revolution is now upon us.

According to FIS® research conducted in May 2023, 52% of executives already utilizing generative AI will increase their investment in generative AI over the next 12 months, with 39% of executives maintaining those investments. Client focus is also shifting to AI, shown by a huge increase in their tendency to invest in it. AI has transformed from being solely a topic discussed by tech employees into a top agenda item for senior executives.

Demystifying AI

AI has been around for years in various forms, but the latest evolution of AI – generative AI – is capturing the imagination of many. When talking about generative AI, there is one name we can’t escape and that’s ChatGPT. It is touted as the fastest-growing app in internet history, amassing 1 million users in five days. To put it in perspective, it took X (formerly Twitter) two years to achieve this milestone.

But what exactly is generative AI, how does it compare to machine learning (ML) and predictive AI, and where does it fit in the evolving hype cycle of AI?

Let’s dig a bit deeper.

ML is about developing algorithms that enable computers to find patterns or make decisions based on datasets. This makes it possible for computers to learn from experience and improve over time. It is a broader concept that covers both generative AI and predictive AI.

Predictive AI is a method of data analysis capable of predicting and forecasting future incidents or outcomes. It allows the user to see patterns and approaching trends, or to predict risks and the best solutions to mitigate them. An example of this is robotic process automation, which uses software robots and artificial intelligence to automate processes like data extraction/entry and other repetitive tasks.

Generative AI is a form of artificial intelligence designed to create new content in the form of text, images, code and more, based on input or prompts provided. ChatGPT and other similar tools are based on large language models. They are trained on massive amounts of text, images and other data online, which they can use to model outputs and responses to appear as if they are human-generated.

While the use of AI in financial services poses numerous benefits, such as enhanced productivity and innovation, improved risk management, personalized customer experiences (CXs) and more, it also carries certain risks. Some of these include data privacy, intellectual property and copyright issues, quality and reliability, and inherent bias in data models. It also raises ethical questions for consideration. Below are some core implications that need to be considered when developing generative AI use cases for financial services:

• Transparency and fairness
• Data privacy and security
• Regulatory compliance
• Market manipulation and fraud
• Overreliance on AI and unintended consequences
• Cost of AI ownership

Generative AI is here to stay. Financial institutions are already seeing the benefits of AI tools internally to improve processes and accelerate work output and externally to develop new product offerings and provide richer CXs for end-consumers.

For financial institutions to successfully utilize generative AI, they need to make the required investments into the technology, either by developing internal teams and securing expert resources to build AI-based products and services, or by partnering with third parties that can provide the capability as a service. Financial institutions that partner with third parties should question them about the underlying training data, how inputs and outputs are moderated to retrain models, what safeguards are in place to minimize bad actors and what capabilities exist to fine tune models appropriately.

Generative AI will have an impact on all parts of an organization. Because of this, financial institutions must take a top-down approach. Eventually, most large organizations will have a chief AI officer to set the standards and frameworks to fully deploy AI tools across the organization. Employing this top-down approach, financial institutions must educate their employees on the safest, most practical ways to use generative AI tools in order to increase efficiency and minimize risks. AI builders who develop new AI tools, as well as the employees who use them, must be educated on best practices for the responsible use of AI.

FIS is actively involved in utilizing these technologies and we are exploring a variety of innovative use cases for our clients. The future of financial services is happening right now, and it is an exciting time to be part of this developing technology.