of banks surveyed in the EY Banking Barometer 2026 in Switzerland and Liechtenstein are already working on the implementation of AI projects.
Source: EY Banking Barometer 2026Apply AI in banking with real impact.
From client advisory and wealth management to KYC, compliance, lending, Microsoft Copilot and AI agents: we help financial institutions identify relevant use cases, enable their people and bring AI safely into real business processes.
From advisory and operations to risk, compliance and new agentic workflows.
For every AI use case already deployed, FINMA observes two more in development. Applications are increasingly spreading across the entire value chain.
Source: FINMA 2026of EU banks observed by the EBA already use AI. Relevant areas include customer support, profiling, fraud and AML/CFT.
Source: EBA 2025AI creates value when it improves real banking processes.
Instead of starting with individual tools, we look at tasks and decisions across the banking value chain. This makes it possible to prioritise opportunities by business value, feasibility, risk and organisational readiness.
01
Client & Advisory
Relationship management, customer service, advisory, next best action and client preparation.
02
Onboarding, KYC & AML
Client files, KYC reviews, screening, transaction analysis and case preparation.
03
Lending & Credit
Loan applications, document analysis, credit memo preparation and decision support.
04
Payments & Operations
Payment investigations, document processes, exceptions, internal workflows and operations.
05
Risk, Legal & Compliance
Regulatory change, policies, controls, legal research, governance and documentation.
06
Wealth & Asset Management
Investment research, portfolio insights, wealth planning and advisor support.
From concrete tasks to measurable business value.
Strong AI use cases do not start with the technology. They start where teams spend significant time searching for information, reviewing documents, handling manual handovers, preparing work or performing recurring controls.
Client & Advisory
High potential
Relationship Manager Copilot
Problem
Preparation, documentation and follow-ups around client meetings take significant time.
AI Approach
Structure client information, prepare conversations, summarise meetings and draft follow-ups or next-best actions.
Business Value
More time for clients and more systematic preparation and follow-up.
KYC & Onboarding
High potential
KYC & Client Onboarding Assistant
Problem
Client files contain many documents, data points and review steps that are repeatedly brought together manually.
AI Approach
Extract documents, check completeness, consolidate information and highlight open points for specialists.
Business Value
Less manual preparation and more structured reviews.
AML & Financial Crime
Decision support
AML & Transaction Investigation
Problem
Alerts, transactions and background information need to be analysed and documented across multiple sources.
AI Approach
Summarise cases, highlight patterns and relevant information, and prepare investigation steps for specialists.
Business Value
More efficient case preparation with clear human accountability.
Lending & Credit
High potential
Credit & Lending Assistant
Problem
Loan applications, annual reports and supporting documents need to be reviewed and condensed into a decision-ready basis.
AI Approach
Analyse documents, highlight relevant metrics and risks, identify missing information and prepare credit memo drafts.
Business Value
Faster information preparation and more focus on professional judgement.
Risk & Compliance
Governed
Regulatory & Compliance Assistant
Problem
Regulatory changes, internal policies and control requirements create ongoing analysis and documentation work.
AI Approach
Structure changes, compare policies, prepare gap analyses and support documentation work.
Business Value
Faster preparation of reviews and regulatory follow-up work.
Knowledge & Operations
Foundational
Banking Knowledge Assistant
Problem
Knowledge is distributed across policies, product information, process manuals, intranets and other internal sources.
AI Approach
Search approved knowledge sources in context, derive answers and show sources transparently.
Business Value
Faster access to knowledge and fewer recurring internal questions.
Wealth Management
Advisor support
Wealth & Investment Assistant
Problem
Investment research, market information and client portfolios need to be continuously structured for advisory and preparation.
AI Approach
Summarise research, structure relevant market information, prepare advisory materials and support advisor workflows.
Business Value
More time for advisory with clear accountability remaining with the advisor.
Agents & Automation
Emerging
AI Agents for Banking Workflows
Problem
Many banking processes consist of recurring steps across documents, systems, knowledge sources and roles.
AI Approach
Agents retrieve information, execute defined steps, check prerequisites and hand results over to employees.
Business Value
Fewer manual handovers and better orchestrated processes with clear control points.
In banking, AI must do more than work. It must remain controllable.
The closer AI gets to clients, credit decisions, wealth advisory, compliance or payment processes, the more important clear accountability, verifiable outputs, data controls and transparent operating models become. Governance should therefore be part of use case design from the start.
Regulatory requirements depend on the specific use case, institution, models used and affected processes. Our work does not replace legal or regulatory advice.
Client Data & Privacy
Which data may be processed by which tool, and how are confidentiality and data quality ensured?
Human Oversight
Where does AI support work, and where must review, approval or decision-making remain with a human?
Explainability & Model Risk
How are outputs reviewed, validated and made appropriately explainable in sensitive applications?
Third-Party & Outsourcing Risk
How are external models, cloud platforms, providers and dependencies addressed in governance and operations?
Security & Access
Which roles, permissions, data access rules and security mechanisms apply to AI applications and agents? AI Security Testing helps assess attack surfaces in chatbots and agents.
Auditability & Monitoring
How are usage, changes, controls and responsibilities documented and monitored in operation?
Banking expertise remains essential. AI capability comes on top.
Effective AI adoption is not just a technology project. Relationship managers, operations, risk & compliance, legal, IT & data teams and leaders need different skills, examples and guardrails for their day-to-day work.
Role-based Enablement
01
AI Literacy & Responsible AI
Foundations, opportunities, limitations, privacy, risks and responsible use in a regulated environment.
02
Role-specific Banking Use Cases
Relevant applications based on real tasks from advisory, operations, KYC, compliance or lending.
03
Tools, Prompting & Microsoft Copilot
Productive use of approved tools with practical exercises from everyday banking work.
04
Workflows & Agents
Move from individual prompts to repeatable ways of working, automations and agentic workflows.
05
Adoption, Governance & Scale
Embed champions, internal standards, governance and continuous improvement across the institution.
From skills and use cases to governance and execution.
Depending on where you are today, we can start with enablement, a concrete use case, Microsoft Copilot or a strategic question. These building blocks can be combined, but they do not need to begin as a large transformation programme.
People
AI Enablement & Training
Role-based AI training with banking examples, tools and concrete tasks from day-to-day work.
Corporate AI Training →Opportunities
AI Use Case Discovery
Systematically analyse banking processes and prioritise opportunities by business value, feasibility, data readiness and risk.
AI Use Case Workshop →Productivity
Microsoft Copilot
Bring Copilot readiness, governance, role-based use cases, training, adoption and internal champions together.
Microsoft Copilot →Organisation
AI Advisory & Governance
Shape strategy, operating model, AI governance, portfolio management and adoption for scalable AI use.
AI Advisory →Execution
AI Agents & Workflows
Prototype prioritised use cases, test technical feasibility and turn them into productive workflows or agents.
Banking experience beyond the workshop room.
Our experience spans AI enablement and use case discovery through to AI advisory and product work in financial services. We combine sector understanding with practical AI expertise.
17'000+
people trained
150+
clients
Since 2019
AI Enablement & Transformation
Bank Frick
AI Strategy, Use Cases & Roadmap
Strategic AI exploration was translated into concrete use cases and a stronger foundation for further AI innovation.
View References →St.Galler Kantonalbank
AI Advisory & Use Case Implementation
Strategic and operational support focused on realistic feasibility and concrete AI use cases.
View References →Team Experience
AI Product Leadership in Swiss Banking
The team's experience includes AI product leadership at UBS as well as conversational banking, AI search and automation in banking environments.
Common questions about introducing AI in banks and financial institutions.
Which AI use cases are particularly relevant for banks?
Relevant applications include client advisory, KYC and AML, lending, compliance, wealth management, knowledge management, operations and agentic workflows. Which use case makes sense depends on business value, data readiness, feasibility, risk and organisational readiness.
How can AI support relationship managers?
AI can structure client information, prepare meetings, summarise notes, draft follow-ups and prepare relevant next steps. Responsibility for advice and client decisions remains with the relationship manager.
How can AI be used in KYC and AML?
AI can extract and structure documents, consolidate information from files, highlight anomalies, summarise alerts and prepare reviews. Depending on the application, clear controls, permissions and human oversight are essential.
How can AI support lending and credit processes?
AI can summarise loan applications, annual reports and other documents, highlight relevant information, identify missing data and prepare credit memo drafts. Credit decisions remain within the institution's defined accountability and control processes.
What role can AI agents play in banking?
AI agents can coordinate multi-step tasks across documents, knowledge sources and systems. They can execute defined steps, retrieve information, check prerequisites and hand results to employees for review.
What matters from a governance perspective when using AI?
Key topics include client data, privacy, security, explainability, human oversight, model risk, third-party and outsourcing risk, auditability and monitoring. The specific setup depends on the use case and regulatory context.
Which AI skills do banking employees need?
Alongside banking expertise, AI literacy, safe use of approved tools, critical evaluation of AI outputs and an understanding of data, risk and responsible use are becoming more important. Role-based training connects these capabilities with real tasks.
How should a financial institution get started with AI?
A practical starting point combines clear business goals, an overview of existing initiatives and the prioritisation of relevant use cases. Skills, governance, data and technical conditions should be considered in parallel. Not every institution needs to start with a large transformation programme.
Where can AI create the greatest value in your financial institution?
Tell us briefly where you are today. We will help you assess relevant use cases, capability needs, governance questions and the right next step.
Nina Habicht
Founder & CEO · Dreamleap
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