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GenAI in Banking

Applying generative AI to Swiss banking

I build and evaluate generative-AI systems for regulated financial services - using NLP and LLMs to improve client-documentation quality, KYC, identity matching, and fraud detection, with risk management and compliance built in from the start.

Approach

Methodology

  1. 1

    Frame the regulated banking use case

    Start from a concrete compliance problem - client-documentation quality, KYC, identity matching, or fraud detection - and pin down the risk and regulatory constraints it has to satisfy.

  2. 2

    Build the NLP / LLM solution

    Apply text analytics and LLMs (Anthropic/Claude and OpenAI APIs) to assess and improve documentation, calling them against an explicit quality and compliance rubric.

  3. 3

    Embed risk and compliance controls

    Bake risk-management and compliance checks into the workflow so generated and assessed content stays auditable and defensible, not just fluent.

  4. 4

    Validate and communicate results

    Evaluate solutions to a research standard and communicate findings to stakeholders - from hackathon judges to peer-reviewed publication.

In Practice

Code Snippets

note_qa.pypython
def assess_contact_note(note, llm, rubric):
    """Quality-assure a client contact note against a compliance rubric."""
    prompt = build_prompt(note=note, rubric=rubric)
    review = llm.complete(prompt)  # Anthropic/Claude or OpenAI
    return {
        "completeness": review.completeness,
        "compliance_flags": review.flags,
        "suggested_edits": review.edits,
    }
quality_rubric.txttext
Assess this client contact note for a Swiss private bank.
Check completeness, factual support, and regulatory compliance.
Flag missing KYC details, unsupported claims, and risk indicators.
Return JSON: {"completeness": 1-5, "flags": [...], "edits": [...]}.
Results

Case Studies

RiskOn Hackathon 2025 - Julius Baer

Problem
Client contact notes vary in quality, creating compliance and risk exposure for the bank: 'Quality Assurance for Client Contact Notes - how can AI help?'
Approach
Built an NLP/LLM solution that applies text analytics to automatically quality-assure client contact notes, surfacing gaps and compliance risks for review.
Result
Team Winner of the RiskOn Hackathon 2025, sponsored by Julius Baer (Bank), Switzerland.

AI-driven risk management in Swiss banking

Problem
Swiss banks need stronger, AI-enabled approaches to client documentation, identity matching, and fraud detection across the KYC lifecycle.
Approach
Researched and designed AI-enabled solutions for KYC, identity matching, and fraud detection, grounding them in real risk-management and compliance requirements.
Result
Published as 'Advancing Risk Management in Swiss Banking' with the University of Zürich, Department of Finance (2026).