Generative artificial intelligence (genAI) is emerging as one of the most transformative technologies in today’s rapidly evolving landscape, offering significant opportunities for the banking industry.

But to realize the full potential of genAI, banks must take a comprehensive approach, aligning the technology with business strategies, developing a robust IT infrastructure, and implementing rigorous risk controls, a new report by the Swiss Bankers Association (SBA) says.

Released in April 2025, the SBA report provides an overview of the potential of genAI in the Swiss banking industry, showcasing its general capabilities and limitations, and demonstrating its relevance for the sector.

The report highlights the need for a clear and well-integrated strategy that addresses strategic, organizational, and technological dimensions. This strategy will allow banks to fully capitalize on genAI’s capabilities. This not only enables them to stay competitive in a fast-evolving market but also position them to meet the evolving expectations of customers and employees while navigating regulatory and legal complexities effectively.

The potential of genAI

GenAI refers to a class of AI models that can generate new content, such as text, images, and audio, based on the patterns learned from large amounts of data they have been trained on.

These models are capable of remarkable proficiency in language-based tasks, including text generation, translation, summarization and question-answering, generating outputs that are highly coherent, contextually relevant and often indistinguishable from text written by humans.

They can be adapted to various industries, including healthcare, finance, education, and law, where they can assist with specialized tasks. In banking, they offer transformative value in several areas.

First, genAI can increase employee productivity and operational efficiency by automating repetitive tasks such as document summarization, report generation, and translation. This allows employees to focus on higher-value activities. At Julius Baer, for example, genAI supports the translation of corporate content and ensures communication aligns with the bank’s tone and terminology, the report says.

GenAI can also enhance customer experience. In particular, AI-driven chatbots and personalized financial recommendations provide clients with more responsive and tailored interactions. At SIX, for example, genAI is used to transcribe calls, analyze content, and identify problematic interactions, supporting agents and enhancing service quality, the SBA report says.

GenAI also provides banks with the opportunity to enhance existing financial products and services. AI-generated insights, for example, enable banks to create customized investment strategies and credit risk models, improving decision-making.

Finally, genAI can automate risk assessments, compliance reporting, and fraud detection. For example, JP Morgan Chase has developed a genAI-powered platform called COiN (Contract Intelligence) which is capable of analyzing thousands of complex legal and financial documents to identify potential risks, extract regulatory-relevant information for compliance purposes, and detect anomalies that may signal fraud. The system helps the bank save 360,000 hours of manual review annually, while simultaneously reducing compliance risk.

A framework for genAI implementation

To successfully implement genAI tools, banks must act across multiple dimensions, ensuring that AI initiatives are aligned with business strategy, governed effectively, supported by the right mindset and culture, underpinned by robust IT infrastructure, and secured by rigorous risk controls.

The SBA proposes a structured four-phase framework. In the first phase, the exploration phase, banks focus on building a foundational understanding of genAI and learn to identify opportunities where genAI might offer benefits.

In the second phase, the analysis and roadmap phase, banks assess the feasibility of genAI applications, prioritizing use cases, and creating structured project management plans. Proof of concepts should be conducted during this phase with broad involvement from various departments to initiate learning within these areas and to adhere to legal and regulatory requirements.

Third, the basics and implementation phase sees participants of a genAI project establish the underlying infrastructure and its governance.

Finally, the fourth and last phase, the scaling and continuous improvement phase, focuses on performance monitoring and quality maintenance as banks expand genAI applications across their operations.

Strategic, organizational and technological considerations

But before beginning genAI implementation, the SBA advises banks to establish a clear strategy.

This strategy should be integrated with the bank’s overall business strategy, ensuring that genAI initiatives support core objectives like increasing employee productivity, improving operational efficiency, enhancing customer experience, or expanding product and service offerings. It should include clearly defined goals and measurable outcomes to track progress.

AI should not be seen as the sole responsibility of the IT or data science teams, and cross-functional collaboration, especially between marketing, compliance, risk, and customer service, will ensure that genAI initiatives are aligned with diverse business needs.

Organization readiness is another critical consideration. Banks should consider whether or not they will need to hire new staff, or re-/up-skill existing workforce to align with the necessary capabilities to integrate genAI tools and applications. Workshops and training sessions can also be hosted to introduce genAI to employees, and educate staff on AI capabilities, their associated risks, and their potential impact on banking operations.

Once employees are up to speed on genAI risks and opportunities and a genAI implementation has been put on the agenda, a team can test initial use cases such as off-the-shelf tools.

On the technological front, a modern, scalable IT infrastructure is indispensable for any genAI implementation. Banks need to ensure that their technology stack can support the complexities of AI while integrating with existing systems. Key technical considerations include infrastructure costs, latency, performance and scalability, cybersecurity, data management and integration, and data residency and sovereignty.

Adoption of AI has increased significantly over the past years, with an especially strong acceleration in 2024. Between 2017 and 2023, global AI adoption remained relatively steady, hovering around 50%, according to the McKinsey Global Survey. However, in 2024, this figure jumped to 72%, with more than two-thirds of respondents in nearly every region reporting active AI usage.

In Switzerland’s banking sector, adoption reflects the global trend of accelerated growth in 2024. The EY Banking Barometer 2025 reveals that the share of banks that implemented their first AI-based applications doubled in 2024, rising from 7% a year prior to 14%.

Featured image by Frolopiaton Palm and wirestock on Freepik