Transforming Banking with Generative AI: Innovation, Strategy, and Governance

The rapid evolution of artificial intelligence (AI) is reshaping the landscape of retail and commercial banking. Generative AI (GenAI), with its ability to transform operations and customer engagement, stands at the forefront of this revolution. As banks explore and invest in GenAI, they face the challenge of integrating this technology into their business models to drive competitiveness and innovation.

A recent survey (from EY) highlights the cautious yet optimistic stance banks are taking towards GenAI. While only a small percentage expect GenAI to fully automate their operations within the next decade, the majority are already dedicating resources to its exploration and deployment. This gradual adoption reflects a strategic approach to leveraging AI's potential while navigating the complexities of implementation.

This article explores five key areas where banks can harness GenAI to reimagine their business models, enhance technological capabilities, drive innovation, establish centers of excellence, and implement robust governance frameworks. By examining successful examples and survey data, we provide a roadmap for banks to capitalize on AI opportunities and build the bank of the future (see the full EY report and Servay ).

1. Future Business Model

To capitalize on AI opportunities, banks need to reimagine their future business models based on AI capabilities and then work backward to prioritize immediate use cases. AI-enabled capabilities can create new opportunities to monetize data, expand product and service offerings, and strengthen client engagement, enhancing the organization’s competitiveness.

Identifying Opportunities: Drawing from previous implementations of innovative technologies like blockchain and robotic process automation, banks should evaluate whether Generative AI (GenAI), existing technology, or a combination of both is the right solution for specific issues. Effective use cases will include "high-touch" activities traditionally managed by people, which utilize large datasets or require generative response logic. Regulatory considerations will also influence the prioritization of use cases, with authorities likely expecting firms to deploy advanced GenAI systems in areas like financial crime.

2. New Technology and Talent

Banks needing to update their technology could leverage this opportunity to overcome current architectural constraints by adopting AI. For AI to be effective, it must integrate with employees' operational expertise and industry knowledge. Due to the novelty of AI and limited tech capabilities in many banks, acquisitions or partnerships may be necessary to access the required skills and resources. AI’s ability to handle unstructured data facilitates data sharing with third parties through ecosystems. A significant portion (51%) of banks prefer partnerships as their go-to-market approach for GenAI use cases, rather than in-house development.

Identifying Opportunities: The initial step is identifying opportunities to modernize infrastructure, enhance data quality, and improve data flows. Banks might need to boost computing capabilities (e.g., server capacity, data storage, and computational power) to implement AI effectively. Additionally, creating "knowledge graphs" from existing institutional expertise will enable GenAI to extract valuable insights.

3. Rebalance the Innovation

Banks should not limit their vision for GenAI to automation, process improvement, and cost control, although these are logical initial priorities. GenAI can also impact customer-facing and revenue operations in ways current AI implementations often do not. For example, GenAI can support the hyper-personalization of offerings, driving customer satisfaction, retention, and confidence.

Banks pursuing industry verticalization, deposit retention strategies, and new revenue streams should consider these areas for initial GenAI use cases. GenAI can generate new insights from data on buying habits, trade patterns, and internal tax compliance, creating additional revenue streams.

Identifying Opportunities: Banks should evaluate use cases based on value creation and risk. In the short term, they should focus on the highest value opportunities while considering risk exposure. The AI investment portfolio should advance broader strategic objectives while achieving quick wins with clear value and minimal risk. Internally oriented use cases, such as content generation and workflow automation (e.g., knowledge management), are good starting points. Starting small and achieving quick wins allows banks to assess their capabilities, recognize key challenges, and evaluate partnerships or acquisitions for further scaling.

4. Establish a Dedicated Centre of Excellence

Financial institutions of all sizes can benefit from creating a GenAI Center of Excellence (CoE) to implement early use cases, share knowledge and best practices, and develop skills. As GenAI capabilities mature, organizations can adopt a "control tower" approach to develop vision and strategy, provide visibility into GenAI adoption, and strengthen governance models.

Identifying Opportunities: Larger banks further along in AI experimentation should establish a control tower function to provide direction, document a high-level roadmap for GenAI goals, and evaluate technology architectures and data sets. A control tower approach offers GenAI leadership and coordinates ongoing execution and deployments. Appropriate controls and metrics are essential, with adjustments made over time as business outcomes are tracked and needs change. Smaller and midsize organizations can start with a CoE to incrementally improve capabilities, spread best practices, foster knowledge sharing, and promote early use cases.

5. Governance and Controls

GenAI introduces new risks and heightens existing ones in banking operations. While AI governance processes and controls resemble legacy technologies, new risks require new models and frameworks for internal use cases and third-party tools.

Organizations must determine when and how employees can use GenAI and assess the distinct risks of internal and external use cases. GenAI’s impact on operations is another consideration. For instance, using GenAI for lending decisions could result in biased outcomes based on protected characteristics like gender or race. Banks must provide evidence to regulators to show why applications are denied and ensure fair consideration of applicants. Governance models must be designed to promote the responsible and ethical use of GenAI.

Identifying Opportunities: Initially, banks should establish guidelines and controls for employee usage of existing, publicly available GenAI tools and models. These guidelines should prevent employees from loading proprietary company information into these models. Additionally, top-level governance and control frameworks must be established for GenAI development, usage, monitoring, and risk management.

The transformational power of GenAI

Leveraging the transformational power of GenAI requires innovative thinking about the longstanding challenge of balancing innovation with maintaining operations. A significant majority of banks are already dedicating resources to GenAI exploration or deployment.

• 90% of respondents noted that their banks have dedicated resources to GenAI exploration or deployment

Success with GenAI necessitates future-back planning to set the vision, use-case prioritization, risk management, and governance. Banks must rethink their understanding of AI primarily as a technology for back-office automation and cost reduction. Considering how GenAI can transform front-office functions and the overall business model is crucial for maximizing technology’s ROI. Recognizing GenAI as part of an overall innovation agenda, combined with measured actions supported by a long-term strategy, will enable banks to create value for customers and shareholders while building the bank of the future.