In addition to his global role, David is the co-organizer of Accenture’s FinTech Innovation Lab, a mentorship program bringing together fintech start-ups and leading financial institutions, with labs in the U.K., U.S., and Asia-Pacific. Follow him for continued coverage around how financial services firms and fintechs are embracing technology, AI and data to reinvent their operations and deliver a more personalized customer experience. The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact.

Significant challenges could lie ahead

As financial services companies advance in their AI journey, they will likely face a number of risks and challenges in adopting and integrating these technologies across the organization. Our survey found that frontrunners were more concerned about the risks of AI (figure 10) than other groups. Many organizations have gone digital and understanding gaap vs ifrs learned new ways to sell, add efficiencies, and focus on their data. Going forward, they will need to personalize relationship-based customer engagement at scale. 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. One European neobank, bunq, is already using generative AI to help improve the training speed of its automated transaction monitoring system that detects fraud and money laundering.

Successful gen AI scale-up—in seven dimensions

Only by following a plan that engages all of the relevant hurdles, complications, and opportunities will banks tap the enormous promise of gen AI long into the future. 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.

Applications: How AI can solve real challenges in financial services

The good news here is that more than half of each financial services respondent segment are already undertaking training for employees to use AI in their jobs. The ability to analyze vast amounts of data quickly can lead to unique and innovative product and service offerings that leapfrog the competition. For instance, AI has been used in predictive analytics to modernize insurance customer experiences without losing the human touch. Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized. We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized.

Artificial intelligence (AI) in finance helps drive insights for data analytics, performance measurement, predictions and forecasting, real-time calculations, customer servicing, intelligent data retrieval, and more. It is a set of technologies that enables financial services organizations to better understand markets and customers, analyze and learn from digital journeys, and engage in a way that mimics human intelligence and interactions at scale. Fintechs remain at the forefront of harnessing gen AI and many of their use cases and solutions are impacting financial services. For example, Synthesia utilizes an AI platform to create high-quality video and voiceover content tailored for financial services, while Deriskly provides AI software aimed at optimizing compliance in financial promotions and communications. Therefore, this synthesis of the evolving landscape should not be the end, but rather a compelling call to action for banks globally.

1. 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).
2. ​Financial services are entering the artificial intelligence arena and are at varying stages of incorporating it into their long-term organizational strategies.
3. Using gen AI can help address some of the most acute talent issues in the industry, such as software developers, risk and compliance experts, and front-line branch and call center employees.
4. While working on such initiatives, it is important to also assign AI integration targets and collect user feedback proactively.
5. From our survey, it was no surprise to see that most respondents, across all segments, acquired AI through enterprise software that embedded intelligent capabilities (figure 9).

Making the right investments in this emerging tech could deliver strategic advantage and massive dividends. These include navigating the complex terrain of data privacy and the socio-economic implications of automation, such as job displacement. Furthermore, ensuring that AI systems operate with fairness and transparency remains a paramount concern, highlighting the need for robust governance frameworks. The evolution of AI in banking has been nothing short of revolutionary, moving from foundational concepts to the creation of sophisticated, innovative applications.

These advancements represent a new frontier where AI intersects with core financial operations, propelling the sector into an era of unprecedented innovation and efficiency. Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application. MSCI is also partnering with Google Cloud to accelerate gen AI-powered solutions for the investment management industry with a focus on climate analytics. While existing Machine Learning (ML) tools are well suited to predict the marketing or sales offers for specific customer segments based on available parameters, it’s not always easy to quickly operationalize those insights.