The recent Zürich edition of The Wealth Mosaic’s AI Toolkit roadshow featured an insightful keynote presentation by Vincenzo Chiochia, Director – Head of AI & Data, Banking Practice, Deloitte. The session explored how Artificial intelligence (AI) has taken centre stage in the banking sector, reshaping operations, enhancing customer experiences, and redefining how financial institutions engage with clients.

At Deloitte, I lead the banking sector's AI and data initiatives, collaborating with retail banks and fund managers on data management, governance, and intelligent applications. From Geneva to Zurich, our teams are seeing first-hand how AI is revolutionising the wealth industry.  

A recent survey reveals that 69% of banking executives anticipate AI to significantly impact their organisations, with 42% predicting a reduced reliance on personal investment advisers. However, this sentiment varies based on client demographics and geographical trends, particularly with private banks engaging with younger, tech-savvy wealthy individuals. Below, I explore some of the key points related to AI adoption today, and also, outline some of the key challenges that need to be overcome to continue on our collective journey of industry transformation leveraging AI and all the potential it brings.

Key trends in AI adoption
The adoption of AI in client engagement is increasingly shifting towards hybrid models that combine AI-driven tools with traditional personal interactions. This trend reflects a growing recognition of AI's potential to enhance relationship management without replacing the human touch. Over the next three years, 60% of relationship managers anticipate leveraging AI tools to improve client service, while 67% of firms plan to integrate digital solutions alongside face-to-face meetings. This dual approach ensures that AI supplements human expertise, offering a seamless blend of efficiency and personalisation in client interactions.

In these hybrid models, AI operates largely behind the scenes, empowering relationship managers with actionable insights and streamlined workflows. By automating preparatory tasks, such as analyzing client portfolios or tracking follow-ups, AI allows managers to focus on deeper, more meaningful engagement during meetings. Additionally, AI-driven tools can generate tailored insights, enabling firms to offer personalised advice that resonates with clients' unique needs and goals. As a result, the hybrid model is poised to redefine client engagement, combining the analytical power of AI with the relational strength of human interaction.

The rising demand for digital experiences
Investors are increasingly driving demand for advanced digital experiences, urging banks to prioritize innovative solutions that cater to their evolving expectations. A significant 68% of investors express a strong preference for banks that emphasise digital tools and platforms, highlighting a clear shift in client priorities. Among the technologies shaping this transformation, AI and cloud computing stand out as top investment areas, owing to their synergistic capabilities. Together, these technologies enable seamless data management, personalised client interactions, and scalable solutions that redefine how financial services are delivered.  

Generational dynamics further amplify this trend, particularly in regions like APAC, where younger wealthy individuals are reshaping the landscape of private banking. For Gen Y and Z investors, digital-first banking experiences are not just a preference but a baseline expectation. This demographic envisions a future with minimal reliance on traditional advisory services, with 60% predicting a significant shift by 2030. To stay competitive, financial institutions must adapt, embracing transformative technologies and strategies that resonate with this tech-savvy generation’s demands for convenience, speed, and personalisation.

Opportunities for AI application across the bank value chain  
AI offers transformative opportunities across multiple dimensions of banking:

• Efficiency and cost optimisation:
  Banks are leveraging AI to streamline operations, especially in back-office functions. For instance, generative AI can simplify client onboarding, enhance Know Your Customer (KYC) processes, and standardise document preparation, reducing inefficiencies and errors.

• Enhanced customer experience:
  Relationship managers use AI to generate personalised content, such as emails, videos, and portfolio performance reports. AI tools analyse client risk profiles, summarise investment positions, and tailor recommendations, enabling more meaningful client interactions.

• Risk and compliance:
  Generative AI supports regulatory compliance by analysing communication streams, summarizing interactions with regulators, and ensuring adherence to complex regulatory frameworks.

• Support functions:
  AI accelerates IT operations, facilitates code generation, and enhances HR, legal, and financial processes, improving organisational efficiency.

Challenges in AI adoption
Despite its potential, AI adoption in banking is not without hurdles. The main ones are listed below:

• Data privacy and security:
  Banks must address concerns about hosting sensitive client data in the cloud. While some institutions adopt on-premise solutions, others explore hybrid models to strike a balance.

• Workforce adaptation:
  Employees may perceive AI as a threat. Effective change management and training programmes are essential to position AI as a tool that enhances, rather than replaces, their roles.

• Regulatory risks:
  Banks must establish robust governance frameworks to manage AI-related risks, including model reliability, data security, and compliance.

What are the building blocks for AI success?
To harness AI’s potential while managing risks, banks should focus on eight critical areas:

• Operating model: Define roles, including the Chief AI Office, and clarify governance structures

• Value realisation: Track AI initiatives using clear KPIs to measure impact and prioritise resources effectively

• Use case delivery: Assemble skilled teams to implement AI projects

• Platform development: Choose between cloud-based or on-premise solutions, ensuring data security and scalability

• Ecosystem partnerships: Collaborate with FinTechs, tech partners, and other external organisations

• Learning and development: Equip employees with skills in data science, machine learning, and prompt engineering

• Operational excellence: Ensure seamless integration of AI tools into existing workflows

• Change management: Promote AI as an enabler and provide a clear vision for its integration into the workforce

Strategic steps to accelerate AI adoption
Banks must take pragmatic steps to stay ahead:

• Secure leadership support: Gain C-level buy-in to prioritize AI as a strategic advantage

• Prioritise investments: Identify key focus areas – be it back-office efficiency or client-facing innovation – and allocate resources accordingly

• Establish governance: Implement a minimum viable governance model to manage risks and ensure regulatory compliance

• Scale thoughtfully: Start with pilots, refine use cases, and scale successful initiatives while monitoring their impact

Conclusion: The future of AI in banking
AI is set to be a defining factor in banking competitiveness, shaping client preferences and employee choices. By addressing challenges and adopting a structured approach, banks can unlock AI’s full potential. The road may be complex, but with the right strategy, AI will transform the banking landscape, offering unparalleled opportunities for growth and innovation.

Boilerplates: 

• Name: Vincenzo Chiochia
• Title: Director – Head of AI & Data, Banking Practice
• Company: Deloitte
• Email: vchiochia@deloitte.ch
• LinkedIn: https://www.linkedin.com/in/vincenzochiochia/

About the author:
Vincenzo is a Director at Deloitte Switzerland in the AI and Data team, where he leads the banking practice. He also oversees collaboration with non-for-profit institutions. He has over two decades of experience in the management of large projects, design of data driven organisations, as well as analysis of very large datasets leveraging ML and AI. As a researcher and UZH faculty member, he was involved in breakthrough scientific discoveries at the CERN Large Hadron Collider, such as the Higgs boson and a new heavy baryon.

Before joining Deloitte, Vincenzo was Head of the Big Data Analytics practice at Accenture. Previously, he held the role of Assistant Professor in Experimental Physics at Zurich University and project manager at CERN, working on the Compact Muon Solenoid (CMS) experiment.

He holds a Master’s degree in Physics from Padua University (Italy) and a Ph.D. in Physics from Hamburg University (Germany). He spends any free time engaging in sports, yoga and street photography.

About Deloitte:
Deloitte provides industry-leading audit and assurance, tax and legal, consulting, financial advisory, and risk advisory services to nearly 90% of the Fortune Global 500® and thousands of private companies. Our people deliver measurable and lasting results that help reinforce public trust in capital markets, enable clients to transform and thrive, and lead the way toward a stronger economy, a more equitable society, and a sustainable world. Building on its 175-plus year history, Deloitte spans more than 150 countries and territories. Learn how Deloitte’s approximately 460,000 people worldwide make an impact that matters at www.deloitte.com

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About The Wealth Mosaic AI Toolkit Roadshow:
Ahead of the planned publication of the second of our Toolkit Reports – this time, focused on AI –  we have so far hosted an initial event in Zurich, with plans to host further events in New York in January 2025, and thereafter in Singapore. These are free to attend for any form of wealth manager and provide technology vendors an opportunity to sponsor or demo.

Join us at our AI Toolkit Roadshow events in 2025: 
•    New York – discover more and register here 

About The Wealth Mosaic AI Report:
The AI Toolkit Report is the second in a series of toolkit reports focused on key technology themes and tools impacting global wealth management. Each toolkit report focuses on a key industry theme, segment or geography.

The report will be published in Q1 2025.