That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding. The right operating model for a financial-services company’s gen AI push should both enable scaling and align with the firm’s organizational structure and culture; there is no one-size-fits-all answer. An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively.
Rob is a principal with Deloitte Consulting LLP leading the Operating Model Transformation market offering for Operations Transformation. He also leads Deloitte’s COO Executive Accelerator program, designing and providing services geared specifically major types of recording transactions for the COO. He serves at the forefront of insurance industry disruption by helping clients with digital innovation, operating model design, core business and IT transformation, and intelligent automation. Rob specializes in helping insurers redesign core operations and serves as a lead consulting partner for two commercial P&C insurers.
Expanding impact across facets of banking
Banks are combating these issues by investing in high-quality data collection and preparation practices to reduce bias. Furthermore, the adoption of human oversight and explainability tools help ensure the responsible use of AI, enabling the early identification and correction of issues before they affect customers. The regulatory environment for AI in banking is dynamic, posing challenges for both banks and regulators aiming to keep pace with technological advancements.
This portfolio approach likely enabled frontrunners to accelerate the development of AI solutions through options such as AI-as-a-service and automated machine learning. At the same time, through crowdsourced development communities, they were able to tap into a wider pool of talent from around the world. Gen AI, along with its boost to productivity, also presents new risks (see sidebar “A unique set of risks”).
How banks are using generative AI
While analytics at banks have been relatively focused, and often governed centrally, gen AI has revealed that data and analytics will need to enable every step in the value chain to a much greater extent. Business leaders will have to interact more deeply with analytics colleagues and synchronize often-differing priorities. In our experience, this transition is a work in progress for most banks, and operating models are still evolving. Therefore, this synthesis of the evolving landscape should not be the end, but rather a compelling call to action for banks globally. It is time to seize the moment and make strategic investments in GenAI, ensuring that these powerful technologies serve as the cornerstone for a new age of financial services that is equitable, ethical and exemplary in its efficiency and innovation. In every facet, from consumer banking to the precision required in tax compliance and legal operations, AI is a testament to our innovative spirit and commitment to progress.
- EY is a global leader in assurance, consulting, strategy and transactions, and tax services.
- In every facet, from consumer banking to the precision required in tax compliance and legal operations, AI is a testament to our innovative spirit and commitment to progress.
- 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.
- Harnessing AI paves the way for a promising banking future, ready to meet the demands of a rapidly changing world.
- Investments in executive education will equip them to show employees precisely how the technology and the bank’s operations connect, thereby generating excitement and overcoming trepidation.
- But scaling up is always hard, and it’s still unclear how effectively banks will bring gen AI solutions to market and persuade employees and customers to fully embrace them.
Explore what generative artificial intelligence means for the future of AI, finance and accounting (F&A). Explore the free O’Reilly ebook to learn how to get started with Presto, the open source SQL engine for data analytics.
Latest Insights
Active engagement between banks and regulatory bodies is critical to the aim of establishing transparent and effective frameworks that guide the ethical and responsible use of AI. This effort focuses on eliminating bias in algorithms and enhancing the explainability of AI’s decision-making processes, which are essential to maintaining public trust and transparency. The disruptive power of GenAI extends beyond banking to wealth management, insurance and payments, transforming customer engagement, transaction processing and fraud detection. Strategic advisor mainly within the financial services industry, focused on AI and digital innovation. That said, financial institutions across the board should start training their technical staff to create and deploy AI solutions, as well as educate their entire workforce on the benefits and basics of AI.
Key Use Cases Driving AI Adoption
These encompass ensuring data privacy and security, navigating an evolving regulatory landscape, and the meticulous work required to mitigate potential biases and inaccuracies inherent in AI predictions. This acknowledgment of AI’s limitations dovetails with the broader landscape of challenges that banks face, including cultural resistance and strategic alignment. Progress toward leveraging AI’s full potential thus involves not only technological adoption but also adaptation to the ethical, legal and social dimensions of AI use. As financial institutions chart this course, their focus extends beyond mere technological implementation to include fostering an AI-driven ecosystem that is ethically responsible, transparent and inclusive. By integrating AI technologies, banks are setting new benchmarks for operational efficiency, client engagement and sustainable growth. This comprehensive approach to innovation sees AI advancements integrated thoughtfully across all banking operations, thereby forging a sector that is more resilient, agile and centered around the needs and expectations of its clients.