AML Risk and Controls Officer

Internal job title: AML Investigator (2LOD)

The Role

Finom Payments serves small and medium-sized businesses across Europe, and every customer transaction passes through our Transaction Monitoring controls. As AML Investigator in the Second Line of Defence, you find the financial crime behaviour those controls miss, turn it into detection logic, and prove with data that the logic works in production.

You work side by side with the AML Risk Manager, who owns the effectiveness of the AML Risk Engine in the Transaction Monitoring domain. You bring the investigative insight and the data analysis. Together you decide which rules and models to build or change, and you measure their results.

What You Will Own

You are accountable, together with the AML Risk Manager, for three outcomes:

• Forced closures: making sure the decision to close an account is taken correctly, and improving the methodology behind that decision.

• False negative rate: the share of financial crime cases that took place but were not covered by our controls.

• Control coverage of the FinCrime Scenarios Library: the share of typologies in the library covered by a live, tested and monitored control, with the uncovered ones prioritised by risk.

Responsibilities

Detect

• Analyse external sources (police, partners, industry typology reports) and internal sources (anomaly reports, model outputs) to identify anomalies in the transactional behaviour of Finom customers.

• Turn each confirmed anomaly into a documented scenario in Finom's FinCrime Scenarios Library, which covers the full range of AML risks.
  
  

Build and monitor

• Design mitigation measures for each scenario with the AML Risk Manager: new rules, threshold changes and model features.

• Back-test every new or changed rule on historical data before release. Measure alert volume, hit rate and false positives.

• Prepare each release with a rule specification, test results and acceptance criteria, and work with Engineering on deployment.

• Monitor every live rule against its test targets, and tune, escalate or retire it when precision or alert volume drifts.

• Validate the results of internally developed Machine Learning models before release to production, and monitor their performance once live.

• Maintain the library and report its control coverage and the false negative rate.

• Take part in improving the Transaction Monitoring and CDD Risk Engines.
  
  

Enable the organisation

• Develop training materials for the Transaction Monitoring and Quality Assurance teams, based on real cases and current scenarios.

• Identify and drive process improvements within the department, and draft and review system requirements for new features relevant to financial crime.

• Initiate and own ad hoc projects within the department.

• Take on other duties reasonably required for Finom's operational efficiency, whether you identify them or your manager assigns them.

Desired Experience and Skills

Required

• Minimum 2 years in the Second Line of Defence at a regulated financial institution, or 3 years in Transaction Monitoring in a senior role or a Process Owner role.

• Thorough knowledge of AML regulation and of money laundering typologies in transaction data, such as mule accounts, layering through fast pass-through payments, structuring, and misuse of business accounts.

• Confident, hands-on SQL: you write your own queries to investigate cases, back-test rules and monitor rule performance, including joins across large transaction tables.

• Experience with data analytics and visualisation tools such as Databricks, Metabase or Power BI, to build and maintain monitoring dashboards.

• Understanding of how detection rules are tested and measured: false positives, false negatives, hit rate, precision and recall, and back-testing.

• Structured, evidence-based thinking, attention to detail, and the ability to work on your own initiative with little supervision in a fast-moving start-up environment.
  
  

Strong plus

• Practical use of AI tools in investigation and analysis, for example to summarise cases, draft queries, cluster patterns or document rules, with the judgement to check their output.

• Python or similar scripting for analysis and automation.

• Experience validating or monitoring Machine Learning models used in AML detection.

• Experience in a fintech, neobank or payment institution serving SMEs.

How You Work

Finom rejects the "Compliance says no" mentality. We want a colleague who empowers the business instead of limiting it: someone who weighs risk against customer impact, challenges the status quo, and proposes alternatives to a flat refusal.

You communicate with colleagues at every level of seniority. You present your findings and recommendations in plain, concise language to people who are not compliance specialists, such as Product, Engineering and Operations.