Written by For example, a client who frequently traveled internationally faced the risk of unauthorized transactions on his credit cards. By implementing an AI-driven monitoring system, we were able to detect and respond to a fraudulent attempt almost immediately, preventing significant financial loss. This proactive approach not only protects clients’ assets but also enhances their trust in financial advisory services. The question now is what will financial services do next and how soon will they apply AI across the entirety of their organizations and more broadly with customers. Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time.
The Future Of AI In Financial Services
The integration of AI into the cybersecurity framework of the banking sector encapsulates the technology’s dual nature as both a potential risk factor and a critical defensive tool. By embracing an integrated approach that emphasizes security by design, ethical development practices and collaborative innovation, banks can harness AI’s full potential to fortify their cybersecurity defenses. This balanced strategy ensures that the sector can navigate the complexities of AI integration, leveraging its capabilities to create a more secure and resilient financial ecosystem. Generative AI (gen AI) burst onto the scene in early 2023 and is showing clearly positive results—and raising new potential risks—for organizations worldwide. Two-thirds of senior digital and analytics leaders attending a recent McKinsey forum on gen AI1McKinsey Banking & Securities Gen AI Forum, September 27, 2023; more than 30 executives attended.
Robotic process automation in financial services
- This level of detailed, personalized planning was previously time-consuming and complex, but AI has made it more accessible and accurate.
- The most well-thought-out application can stall if it isn’t carefully designed to encourage employees and customers to use it.
- Banks are responding by implementing robust data security measures, anonymizing data where feasible, and securing explicit customer consent to AI use.
- In this regard, EY has demonstrated its commitment to responsible AI development with its platform, EY.ai, launched in September 2023 with an investment of US$1.4 billion.
Companies can also look at making best-in-class and respected internal services available to external clients for commercial use. Value delivery could either include customizing offerings to specific client preferences, or continuously engaging through multiple channels via intelligent solutions such as chatbots, virtual clones, and digital voice assistants. Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action.
The largest players are aggressively investing in developing their AI infrastructure and scaling use cases to capture more value. Daniel Pinto, JPMC’s President and COO, recently estimated that gen AI use cases at the bank could deliver up to $2 billion in value. Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach.
May 29, 2024In the year or so since generative AI burst on the scene, it has galvanized the financial services sector and pushed it into action in profound ways. The conversations we have been having with banking clients about gen AI have shifted from early exploration of use cases and experimentation to a focus on scaling up usage. The technology is now widely viewed as a game-changer and adoption is a given; what remains challenging is getting adoption right. So far, nobody in the sector has a long-enough track record of scaling with reliable-enough indicators about impact. Yet that is not holding anyone back—quite the contrary, it’s now open season for gen AI implementation and the learnings that go with it. The transformative development of AI in banking — from enhancing operational efficiency and customer service to navigating regulatory changes and cybersecurity threats — demands a comprehensive and strategic approach.
Looking ahead, the role of AI in financial services is expected to expand further, with ongoing advancements in machine learning, natural language processing and predictive analytics. These technologies will enable even more sophisticated financial products and services, tailored to meet the unique needs of each client. AI’s ability to process and analyze large datasets at unprecedented speeds has also revolutionized risk management and fraud detection.
Future-proofing through scalability and integration
Certain services may not be available to attest clients under the rules and regulations of public accounting. It is no surprise, then, that one in two respondents were looking to achieve cost savings or productivity gains from their AI investments. Indeed, in addition to more qualitative goals, AI solutions are often meant to automate labor-intensive tasks and help improve productivity. Thus, cost saving is definitely a core opportunity for companies setting expectations and measuring results for AI initiatives. An early recognition of the critical importance of AI to an organization’s overall business success probably helped frontrunners in shaping a different AI implementation plan—one that looks at a holistic adoption of AI across the enterprise. The survey indicates that a sizable number of frontrunners had launched an AI center of excellence, and had put in place a comprehensive, companywide strategy for AI adoptions that departments had to follow (figure 4).
By leveraging EY.ai’s comprehensive platform, expertise and ongoing advancements, banks can embrace the transformative potential of AI in a secure and responsible manner. In wealth management, AI is unlocking personalized advice and risk assessment opportunities. These advancements represent a new frontier where AI intersects with core financial operations, propelling the sector into an era of unprecedented innovation and efficiency. We observed a similar pattern in terms of the skills gap identified by different segments in meeting the needs of AI projects (figure 12). While a higher number of implementations undertaken could partly explain this divergence, the learning curve of frontrunners could give them a more pragmatic understanding of the skills required for implementing AI projects. Financial institutions that have never utilized multiple options why and how auditors assess internal controls to access and develop AI should consider alternative sources for implementation.
This is shifting the paradigm in FS from a reactive service to one that is truly intuitive and responsive. It now handles two-thirds of customer service interactions and has led to a decrease in marketing spend by 25%. Rather than reactively engaging when customers have a request or issue, it could eventually anticipate and proactively reach out to customers before they even know something is wrong.
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