How artificial intelligence is reshaping the financial services industry

Much has been written (including by us) about gen AI in financial services and other sectors, so it is useful to step back for a moment to identify six main takeaways from a hectic year. With gen AI shifting so fast from novelty to mainstream preoccupation, it’s critical to avoid the missteps that can slow you down or potentially derail your efforts altogether. Hyper-personalization – Banks and others are leveraging AI and non-financial data to better create and target highly personalized offerings. This is shifting the paradigm in FS from a reactive service to one that is truly intuitive and responsive.

Retail investors may soon rely on generative AI tools for financial investment advice

AI plays a key role in helping drive tailored customer responses, make safer and more accountable product and service recommendations, and earn trust by broadening concierge services that are available when customers need them the most. Robotic process automation (RPA), cognitive automation, and artificial intelligence (AI) are transforming how financial services organizations operate. Today, many organizations are still in the early stages of incorporating robotics and cognitive automation (R&CA) into their businesses.

1. However, the survey found that frontrunners (and even followers, to some extent) were acquiring or developing AI in multiple ways (figure 9)—what we refer to as the portfolio approach.
2. Guardrails to ensure ethics, regulatory compliance, transparency and explainability—so that stakeholders understand the decisions made by the financial institution—are essential in order to balance the benefits of AI with responsible and accountable use.
3. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution.
4. The insurance sector benefits from more efficient claims processing and risk assessments, as revealed during the EY collaboration with a Nordic insurance company to use AI in automating repetitive tasks in the claims process.

More broadly, gen AI could transform compliance and security measures, enabling firms to meet regulatory how are selling expenses figured out monthly requirements more efficiently while reducing the cost and effort involved in combating financial fraud and managing risk. Extract structured and unstructured data from documents and analyze, search and store this data for document-extensive processes, such as loan servicing, and investment opportunity discovery. Learn about Deloitte’s offerings, people, and culture as a global provider of audit, assurance, consulting, financial advisory, risk advisory, tax, and related services. This technology allows users to extract or generate meaning and intent from text in a readable, stylistically natural, and grammatically correct form. To boost the chances of adoption, companies should consider incorporating behavioral science techniques while developing AI tools.

As the technology advances, banks might find it beneficial to adopt a more federated approach for specific functions, allowing individual domains to identify and prioritize activities according to their needs. Institutions must reflect on why their current operational structure struggles to seamlessly integrate such innovative capabilities and why the task requires exceptional effort. The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Leading corporate and investment banks, for example, have built up expert teams of quants, modelers, translators, and others who often have AI expertise and could add gen AI skills, such as prompt engineering and database curation, to their capability set.

Val Srinivas is the banking and capital markets research leader at the Deloitte Center for Financial Services. He leads the development of our thought leadership initiatives in the industry, coordinating our various research efforts and helping to differentiate Deloitte in the marketplace. The regulatory environment for AI in banking is dynamic, posing challenges for both banks and regulators aiming to keep pace with technological advancements.

Close to half of the frontrunners surveyed had invested more than US$5 million in AI projects compared to 27 percent of followers and only 15 percent of starters (figure 5). In fact, 70 percent of frontrunners plan to increase their AI investments by 10 percent or more in the next fiscal year, compared to 46 good debt vs. bad debt percent of followers and 38 percent of starters (figure 6). A great operating model on its own, for instance, won’t bring results without the right talent or data in place.

Solutions

Rather than taking a siloed approach and having to reinvent the wheel with each new initiative, financial services executives should consider deploying AI tools systematically across their organizations, encompassing every business process and function. As the banking sector embraces the transformative potential of AI, acknowledging its inherent limitations becomes crucial. The nuanced challenges of AI’s integration — spanning the “black box” nature of decision-making processes to the ethical dilemmas posed by potential biases — necessitate a careful approach. While AI promises operational efficiency and strategic innovation, its deployment is not without hurdles. Additionally, GenAI is proving invaluable in the field of tax compliance within banking by automating the preparation of tax returns and enhancing fraud detection.

One year in: Lessons learned in scaling up generative AI for financial services

Explore what generative artificial intelligence means for the future of AI, finance and accounting (F&A). Banks can use AI tools to help protect against rising AI-enabled deepfakes and other fraud. Retail investors may soon financial ratios rely on AI-enabled advisory tools to help inform financial decision-making. To further demystify the new technology, two or three high-profile, high-impact value-generating lighthouses within priority domains can build consensus regarding the value of gen AI. They can also explain to employees in practical terms how gen AI will enhance their jobs.

Companies could also identify opportunities to integrate AI into varied user life cycle activities. While working on such initiatives, it is important to also assign AI integration targets and collect user feedback proactively. For developing an organizationwide AI strategy, firms should keep in mind that these might be applied across business functions. Starting purposefully with small projects and learning from pilots can be important for building scale. It is also no surprise, given the recognition of strategic importance, that frontrunners are investing in AI more heavily than other segments, while also accelerating their spending at a higher rate.