AML Risk and Controls Officer

This is a fully remote position, open to applicants in Poland, +2 more countries.

📋 Description

• Detect financial crime behaviors that are overlooked by Transaction Monitoring controls.

• Convert verified anomalies into formalized scenarios within Finom's FinCrime Scenarios Library.

• Examine sources such as police reports, partner inputs, industry typologies, anomaly reports, and model outputs.

• Collaborate with the AML Risk Manager to create mitigation strategies, which may include new rules, adjustments to thresholds, and enhancements to model features.

• Conduct back-testing of rules using historical data to evaluate alert volume, hit rates, and false positive occurrences.

• Compile rule specifications, testing results, and acceptance criteria while coordinating with Engineering for deployment.

• Oversee live rules, adjusting, escalating, or decommissioning them as necessary when alert volume or precision fluctuates.

• Validate internally developed Machine Learning models prior to production and track their performance in real-time.

• Sustain the FinCrime Scenarios Library and provide reports on control coverage and the rate of false negatives.

• Enhance Transaction Monitoring and CDD Risk Engines.

• Create training materials for Transaction Monitoring and Quality Assurance teams.

• Propel departmental process enhancements and draft or review requirements for financial crime systems.

• Initiate and take ownership of ad hoc departmental projects.

• Clearly present findings and recommendations to colleagues in Product, Engineering, Operations, and other departments.

⛳️ Requirements

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

• Comprehensive understanding of AML regulations and money laundering typologies within transaction data, including mule accounts, layering through rapid pass-through payments, structuring, and the misuse of business accounts.

• Proficient in SQL, including the ability to perform joins across large transaction datasets.

• Familiarity with data analytics and visualization tools like Databricks, Metabase, or Power BI.

• Knowledge of detection rule testing and metrics, including false positives, false negatives, hit rate, precision, recall, and back-testing.

• Strong evidence-based thinking and meticulous attention to detail.

• Capability to work independently with minimal supervision in a dynamic start-up environment.

• Practical experience with AI tools for investigation and analysis is a significant advantage.

• Proficiency in Python or similar scripting languages for analysis and automation is a notable plus.

• Experience in validating or monitoring Machine Learning models employed in AML detection is a strong advantage.

• Background in a fintech, neobank, or payment institution catering to SMEs is a strong plus.

🏝️ Benefits

• Access to resources and opportunities for ongoing personal and professional growth.

• Flexibility to travel and the option to work remotely or in a hybrid model throughout Europe.

• Stock Options Program available for all team members.

• Continuous support and care from the organization.

• A modern, welcoming, and environmentally conscious corporate culture.

• Work & Swim Program: one month in a corporate apartment in Cyprus.