More and more, Generative Artificial Intelligence (GenAI) is reshaping the financial services industry, giving banks, capital markets, and related firms several exciting, even revolutionary, capabilities.
Chances are, the last time you dealt with your financial institution, artificial intelligence was already involved. You may have had a question answered by a digital assistant, or received a personalized marketing offer, or even been the beneficiary of rapid market analysis. The fact is, tomorrow's financial service winners and losers may be determined, in large part, by how effectively they're able to deploy and scale GenAI applications today.
This may be a bold prediction. But it's based on a simple truth: AI itself has evolved.
Traditional AI, which excels at analysis and automation, has been in use for some time now. GenAI, a more recent arrival, is all about creating sophisticated new content, designed to imitate what a skilled human could produce. GenAI, capable of working with multiple modes of communication, such as voice, text, or audio, can summarize large volumes of documentation, write an opinion piece, develop software code, produce images, music, or video, prepare a sales presentation, define a set of data quality rules, and much more.
GenAI is not simply about data analysis, but about extrapolation. This capability has proved to be a game changer for meeting the challenges today's banks and capital markets are facing.
Today's challenges, tomorrow's opportunities
One of the biggest and most ubiquitous challenges confronting financial service firms is the matter of rising customer expectations. Today's consumers demand more personalized experiences, higher quality information, and faster responses. Compounding this, traditional organizations are battling new and more nimble competitors, including robot advisors and digital-first trading platforms, that can meet rising consumer demands and offer results with greater efficiency.
Meanwhile, costs for financial organizations are increasing, while profits from traditional income sources are down. According to Deloitte Global's 2024 banking and capital markets outlook1, a combination of higher interest rates, reduced money supply, tightened credit standards, more assertive regulations, increased competition, and a slowing global economy are putting pressure on the industry.
Yet, for each of these challenges, GenAI represents an answer.
Tomorrow's financial service winners and losers may be determined, in large part, by how effectively they're able to deploy and scale GenAI applications today.
GenAI is quite possibly the single biggest controllable opportunity for financial organizations to improve their competitiveness.
It's all in the execution
Enterprises with a foundation already in place, in the form of cloud infrastructure that offers readily-scalable computing power, will very likely have an early advantage. Not simply because they'll have the technology required to accommodate GenAI. Financial service institutions that work in the cloud will already be familiar with ways to assess and manage risks associated with third-party technology and solutions.
Similarly, organizations with robust data governance principles in place will already have the oversight, accountability, policies, quality improvement methods, and understanding of organizational data assets that can be applied to GenAI use cases.
An organizational culture that embraces technology, as well as the learning approaches needed to build key skills, is also essential. After all, customers are already comfortable with digital banking and self-service options. They expect their bank to be as well.
For financial service organizations about to embark on their GenAI journey, several guiding principles should remain top of mind. First, create a strategic blueprint, setting out how you'll prioritize and introduce GenAI use cases into your architecture, and noting what structures, skill sets, and processes you'll need to achieve your goals. When building an operating structure to support GenAI capabilities, put in place ways to track and measure value, outcomes, and ROI. Determine how to build fluency with GenAI across your business, with training, talent acquisition, and partnerships. Finally, establish ground rules for accountability and the ethical use of your GenAI tools.
Ground rules, in fact, are particularly essential because, as with other emerging technologies, there are risks with GenAI. It's critical to implement safeguards, in order to build trust into the equation.
GenAI is highly proficient at understanding assets across a deeper level, enabling financial advisors and portfolio managers to understand the unique situations, risk profiles, and goals of their new clients in seconds.
Trust through transparency
Trust, of course, is built on transparency. This, in turn, requires explainability, or in other words, the ability to understand how GenAI arrived at its recommendations, and what inputs and data the technology drew on to do so. Without this, effective controls and protections can't be built in. The good news is, most financial service organizations already have well-established governance capabilities in place. This provides control over data quality, supports traceability, and can serve to reduce unforeseen bias. Additionally, human staff should oversee AI processes and take action where necessary to address unwanted behaviours or outcomes.
It's also critical to adhere to a framework that establishes guard rails to govern how GenAI is used. For example, Deloitte’s Trustworthy AI™ framework includes a series of guiding principles to ensure GenAI trustworthiness and reliability. These include training large language models with data sets that are governed within the enterprise, in secure data center or cloud environments to reduce the probability of leaking proprietary company information; restricting the initial usage of GenAI to increase accuracy, then scaling when enough comfort exists; establishing an audit trail for the data that large language models are trained on; keeping humans involved in the process to validate and verify output accuracy; and maintaining a dedicated team to oversee the large language models and ensure biases don't creep in.
By following these principles, and embracing the possibilities of GenAI, financial organizations will be well positioned to meet the demands and challenges of tomorrow. The time to begin the journey is today.