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Harnessing Generative AI for Competitive Edge in Financial Services

Five Ways GenAI is Fundamentally Reshaping Banking and Capital Markets

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.

Increased efficiency, and reducing operating costs, is perhaps GenAI's most well-known benefit. Whether it's automating repetitive tasks such as data entry or analysis, processing vast amounts of information with greater precision and fewer errors than humans, producing operational content such as meeting minutes or summaries, or conducting faster searches of complex data using natural language, GenAI is saving financial service firms time and money, and enabling more effective resource allocation.

"GenAI is quite possibly the single biggest controllable opportunity for financial organizations to improve their competitiveness," says Andy Lees, Partner, Financial Services Google leader, Deloitte UK. That's because GenAI enables banks and other firms to tackle challenges of scale in a way that, previously, would have required many extra employees. In fact, according to Deloitte Luxembourg's June 2024 report, Changing the game: the impact of artificial intelligence on the banking and capital markets sector2, if a particular function can be done better or faster by adding a hundred more trained people, it’s likely that GenAI represents a more efficient way forward.

Yet it's important to remember that GenAI is not intended to replace humans. Rather, the technology augments an existing workforce by increasing processing capacity and quality, while freeing people to focus on relationships and customer facing roles, where human emotional intelligence matters.

For example, Stanford Digital Economy Lab scholars recently studied3 the impact of a GenAI tool that was deployed at a busy call center. They found that call agents with access to GenAI assistance increased their productivity by almost 14%, with the biggest impact on less experienced workers. In addition, agents with two months of tenure who used the GenAI tool were able to perform as well as agents with six months of tenure who didn’t have that access. The productivity benefits decreased for more experienced employees, which demonstrates that GenAI can make less experienced staff more effective, with, correspondingly, less ramp up time.

GenAI can deliver efficiency in another important way: generating and optimizing software code. This not only reduces writing time and improves code quality, it can also accelerate software releases. In turn, this gives financial service organizations the ability to deliver new products, in response to customer needs, faster. Thus, GenAI represents a customer service advantage.

Customer service, in fact, is another area in which GenAI promises to deliver high impact. As anyone who has ever opened an investment account can attest, new client onboarding involves a lot of filling out and signing of documents, an arduous process for both financial service institutions and their customers. Once a client is on board, there's still the matter of understanding and managing their assets, and identifying the best opportunities for their particular portfolio – an increasingly challenging task as asset classes expand and become more complex. Yet today's consumers, investors, and corporate customers expect a fast and smooth onboarding experience, plus the best advice and asset management available, quickly.

That's where GenAI can play a role. "GenAI is highly proficient at understanding assets across a deeper level," says Neil Tomlinson, Global Banking and Capital Markets leader, Deloitte Global, "enabling financial advisors and portfolio managers to understand the unique situations, risk profiles, and goals of their new clients in seconds, versus the six to nine months it would have taken in the past." This is because GenAI is adept at searching through and interpreting reports, filings, news, interest rate patterns, risk types, tax positions, and many other forms of information much faster than a human could. The old saying, "time is money," has never been more applicable. This is just one way that GenAI can significantly impact customer engagement.

GenAI is proving instrumental in making digital agents, colloquially known as chatbots, more personal as well. Today's GenAI-powered agents are summarizing conversations intelligently, offering similarly conversational responses, acting with human-like empathy, and answering an increasingly complex range of customer requests. The result has been reduced customer wait times, and less need for human intervention as digital agents learn how to answer more, and more complex, questions.

GenAI can take personalization a step further. With the ability to analyze customer preferences and behaviors, a GenAI-powered digital agent can recommend financial products and services that are tailored to individual customer needs. Ultimately, that digital agent could customize pricing in real-time, delivering competitive offers to target customers, such as preferential lending rates, based on an enhanced measurement of their credit risk.

This "hyper-personalized" marketing is already driving value. Deloitte Luxembourg's June 2024 report, Changing the game: the impact of artificial intelligence on the banking and capital markets sector2, notes that a leading UK-based bank has achieved a five-fold increase in click-throughs for customized, personalized lending offers made with GenAI.

Often, marketing offers come under regulatory scrutiny for matters such as mis-selling and misinformation. For multinational organizations, cultural differences across regional markets can lead to product misunderstandings, which can create additional regulatory challenges. However, GenAI can help mitigate these regulatory risks, by creating marketing materials across geographies that contain the appropriate tone, language, and cultural references, while also supporting consumer understanding of each product, in each locale.

Enabling understanding, in fact, is another of GenAI's strengths. This is particularly valuable for financial service organizations, which are not only information intensive, but often have data stored in multiple locations, in the cloud and within local legacy systems.

For banks and capital markets, this GenAI capability represents a double benefit. To understand and predict market trajectories and make prudent buying and selling recommendations, analysts must sift through an array of company filings, transcripts, reports, news, interest rate updates, and risk profiles – a time consuming process, precisely when speed matters. At the same time, querying multiple databases from multiple locations adds further hurdles to retrieving relevant information quickly.

With GenAI technologies such as Google's Vertex AI Search, and Google Conversational AI, financial service staff can do more than query multiple databases, and pull relevant insights in near real-time. GenAI can also serve as a natural language research assistant that can synthesize and search through call transcripts, estimates, regulatory filings, economic reports, and many other sources, then offer summarized responses, and even answer follow-up questions. Suddenly, complex data becomes accessible and useful, in time to make a difference.

"Applying GenAI to market analysis can reliably support and supplement human analysts," says Lees. "This accelerates their work while detecting trends, and delivering, potentially, more accurate market predictions."

Accuracy is also a key requirement when it comes to compliance and security, one more area in which GenAI is making an impact. The technology's ability to see patterns and notice out of the ordinary activity makes it well suited to fraud detection and loss prevention. For example, Deloitte Luxembourg's June 2024 report, Changing the game: the impact of artificial intelligence on the banking and capital markets sector2, highlights a leading UK-based bank's success in using GenAI technology to reduce fraud by 6% as a share of the UK banking industry, while also cutting account opening fraud by 90% since 2019.

That same capability can favourably impact compliance activity as well. After all, a significant amount of financial service organizations' marketing, onboarding, customer service, and regulatory reporting involves repetitive content creation. When this work is completed by humans, the potential for errors often exists. GenAI, on the other hand, can process repetitive content faster, and with fewer inaccuracies, while also checking things like localized marketing content in different languages for regulatory matters within each jurisdiction.

Clearly, GenAI represents a competitive advantage for those organizations ready to go all in on the technology. The question then becomes, how?

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.