Financial Services
Redesign financial services operations for an AI-native world.
Artificial intelligence is creating opportunities across banking, financial institutions and fintech — from research and credit analysis to client service, compliance and operations. The challenge is no longer identifying what AI can do. It is deciding where AI creates measurable business value, how workflows should be redesigned, and where human judgment must remain in control.
I help banks, financial institutions and fintechs identify high-value AI opportunities, redesign workflows around AI and automation, and turn isolated experiments into scalable operating capabilities.
Business outcomes
AI can change how financial institutions grow, make decisions, operate and manage risk. I help organisations identify high-value opportunities, redesign processes around AI and automation, and turn isolated experiments into scalable operating capabilities.
Strengthen client relationships and commercial effectiveness.
Turn fragmented information into faster, better-supported decisions.
Redesign repetitive and information-heavy workflows.
Strengthen monitoring while keeping accountability with people.
By segment
Different financial institutions require different operating models. AI transformation should therefore start with the workflows that matter to each business.
Relationship managers spend significant time collecting information before and after client interactions. AI can connect portfolio information, CRM data, research, communications and internal knowledge to create a more effective relationship management workflow.
Credit processes combine financial documents, policies, analysis and human judgment. AI can reduce manual preparation while maintaining human ownership of the credit decision.
Investment teams operate in an environment of continuous information flows. AI can help analysts and portfolio managers process more information while investment judgment stays with professionals.
Fintech and technology-enabled financial organisations can use AI not only to improve individual tasks, but to redesign entire customer and operational journeys.
Geneva is one of the world's largest commodity-trading and trade-finance hubs. These workflows are highly document-intensive and compliance-heavy — a natural fit for AI-assisted extraction, checking and monitoring, with human accountability kept on every credit and risk decision.
AI-native workflows
The real value is not adding AI to an existing process. It is redesigning how humans, AI and software work together — end to end.
AI prepares. The relationship manager advises and decides.
The objective is not to automate accountability. It is to automate the work surrounding the decision.
Operating model
Many organisations ask “Where can we use AI?”. A more valuable question is: “If we designed this process today, how should humans, AI and software work together?” An AI-native workflow defines three roles clearly.
Automate the work. Not the accountability.
Methodology
Successful AI transformation requires more than selecting tools. It requires redesigning how work gets done — from strategy through implementation.
Map current processes, systems, information flows and decision points. Identify where people spend time searching, copying, reconciling, preparing and coordinating information.
Evaluate where AI, agents and automation create value — across business impact, implementation complexity, data availability, risk, human oversight and scalability.
Not every use case deserves to be built. Create a prioritised portfolio focused on the strongest combination of value, feasibility and strategic relevance.
Define what AI performs, what software automates, what data is required, what humans control and where validation takes place.
I design how AI connects to your existing environment — CRM, core applications, data and APIs — and orchestrate the right specialists. Delivery happens with your own teams and technology partners; my role is the operating-model design and the integration architecture, not becoming another tool to maintain.
Measure operational and business impact, then decide what should be improved, expanded or industrialised across the organisation.
Governance
Financial services cannot treat AI governance as an afterthought. The design decision that matters is the level of autonomy each workflow should have — and it is never the same everywhere. A research assistant and a credit-approval process should not operate with the same degree of independence.
AI drafts, extracts and summarises. The human does everything that matters; nothing leaves without review.
AI proposes a decision with its rationale and evidence. A human validates, adjusts or overrides.
Automation executes inside defined thresholds; anything outside them escalates to a person.
Regulated and high-impact decisions stay with people. Governance by design means matching autonomy to risk — with data confidentiality, traceability, human oversight and validation built into every workflow.
AI Opportunity Assessment
You do not need an organisation-wide AI programme to begin. Start by identifying where AI can create the greatest measurable impact — a structured review of your organisation.
Critical workflows
AI opportunities
By value, complexity, risk
Initial AI-native workflows
An AI transformation roadmap
AI transformation for financial services in Switzerland
I work at the intersection of executive leadership, finance, business transformation, technology and artificial intelligence. My approach starts with the operating model — not with a specific AI vendor.
I start with the operating model, not with the AI vendor.
The objective is to determine how AI, agents, automation and human expertise can work together to create more effective financial organisations.
Banking · Financial institutions · Fintech
Sergio Castagna