Hiring nowFinancial services risk and compliance AI

Financial Crime AI Specialist

Also called AML ML Scientist · Fraud ML Scientist

A Financial Crime AI Specialist builds and evaluates models that surface suspicious activity inside governed investigation workflows. They help a regulated firm find real risk while keeping investigator workload and regulatory exposure under control.

What they do

The work starts with transaction, customer and alert data, and an understanding of how investigators actually handle cases. The specialist builds detection models for fraud or money laundering patterns, then designs the evaluation that shows whether they help. That means testing against case outcomes, not only model scores. They tune thresholds with investigators, review false positives, and document how each model behaves and where it fails. They work with model risk, compliance and engineering teams so that what gets built can be validated, explained to a regulator and run inside existing case-management systems.

What a strong hire delivers

In the first months, a strong hire delivers a working detection model tied to a defined investigation workflow. They also deliver an evaluation method the business trusts, with clear measures of useful case yield and false-positive burden. Alongside that sit documentation that model risk can review, a view of data gaps and privacy constraints, and a practical plan for monitoring performance once the model is live.

How you can tell they are good

Ask them to walk through a detection model they built and how they proved it helped investigators. Strong people talk about case outcomes, label quality, drift and threshold choices. They can explain a false-positive trade-off to a non-technical compliance lead. Be wary of anyone who quotes only accuracy or AUC, ignores how investigators work, or treats validation as someone else's job.

Why it is hard to fill

The titles are inconsistent. The same work appears as AML data scientist, fraud modelling lead or financial crime analytics. Most candidates are strong in one area only: modelling, investigations or model validation. Few have done all three in a regulated setting. Banks hold many of them and rarely let them go easily, and machine learning talent from other sectors often lacks the regulatory instinct.

Where to find them

Look in bank and payment firm financial crime analytics teams, fraud strategy and detection groups, and model risk or model validation functions. Good candidates also come from financial ML teams at fintechs, consultancies that serve regulated clients, and vendors of monitoring systems. Typical titles include fraud data scientist, AML analytics manager, quantitative analyst and model validator who has moved into building.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Financial Crime AI Specialist

  1. Describe a fraud or AML model you took into production. How did you show it improved investigations?

    A strong answer names the workflow, the outcome measures and the comparison used, such as case yield against a baseline. Worry if they cite only offline metrics, cannot say what investigators thought, or never saw the model live.

  2. How do you handle labels when most alerts are closed without a confirmed outcome?

    They should discuss label noise, selection bias from past rules, sampling for review and careful use of proxies. Worry if they treat closed alerts as clean negatives or ignore how earlier detection shaped the data.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building detection capability for financial crime in a regulated setting. We need a specialist who can model suspicious activity, prove it helps investigators and work comfortably with compliance and model risk. The work sits inside governed investigation workflows, so judgement matters as much as technique.

What you will do

  • Build and improve fraud or AML detection models
  • Design evaluations based on investigation outcomes
  • Tune thresholds with investigators to control false positives
  • Prepare documentation for model validation and review
  • Monitor live model performance and drift
  • Work with engineering to fit models into case-management systems

What you will bring

  • Shipped a detection or risk model in a regulated financial setting
  • Strong grounding in probability and statistics
  • Experience evaluating models against case or investigation outcomes
  • Familiarity with financial model validation expectations
  • Able to explain trade-offs to compliance and operations teams
  • Awareness of privacy and data protection in model design

Nice to have

  • Experience in model risk or validation
  • Experience with case-management systems
  • Knowledge of AI risk management practice

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Financial model validationAdvancedChallenge financial-model assumptions and limitations
  • Probability and statisticsAdvancedQuantify variability and statistical evidence
  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks
  • Privacy engineeringWorkingLimit disclosure and unauthorised personal-data use

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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