Data & Systems Analyst (Financial Crime)

This newly created role represents a fantastic opportunity for an experienced systems-based Financial Crime Analyst to make a real impact in strengthening the first line of defence against fraud and financial crime within a FTSE 500 financial services provider.

The role will play a pivotal part in enhancing detection, prevention, and reporting capabilities across multiple Financial Crime (FC) systems. Working in a data-rich environment, the successful candidate will combine technical expertise with analytical insight to help identify emerging threats, optimise system performance, and ensure controls remain robust, efficient, and compliant.

Key Responsibilities

• Lead and support the development and optimisation of fraud detection and prevention strategies across all Financial Crime systems.
• Interrogate and analyse large and complex data sets using advanced SQL and data management tools to uncover patterns, trends, and anomalies.
• Develop, track, and report on key fraud metrics, presenting findings and recommendations to senior stakeholders.
• Design and implement data models and machine learning scoring models to drive predictive insights and enhance fraud detection accuracy.
• Optimise and fine-tune Financial Crime system rule sets in line with risk appetite, balancing risk mitigation with operational efficiency.
• Collaborate with cross-functional teams in Technology, Risk, and Compliance to deliver data-driven improvements and strategic initiatives.
• Maintain robust governance and controls over the use of fraud detection systems, ensuring full compliance with regulatory and internal standards.
• Stay current with evolving financial crime trends, industry best practices, and emerging technologies to continuously strengthen defences.
• Engage with external partners, technology vendors, and industry peers to explore new detection techniques and system capabilities.

Skills

• Strong background in Financial Crime or Fraud Analytics within a banking or financial services environment.
• Advanced SQL skills with proven ability to manage and analyse large-scale, complex datasets.
• Experience with database platforms such as SQL Server, Oracle, SAS, or Access.
• Familiarity with data visualisation tools (e.g. Power BI, Tableau) for insight generation and reporting.
• Knowledge of AI and Machine Learning models for risk scoring and predictive analytics.
• Expertise with Financial Crime platforms such as ACI PRM (Proactive Risk Manager), SIRA, SONAR, ThreatMetrix, Mitek, and CIFAS.
• In-depth understanding of payments fraud, AML, CTF, KYC, and internal fraud practices.
• Excellent analytical and problem-solving skills, with high accuracy in data investigation and interpretation.
• Strong communication skills, capable of translating technical findings into actionable business insights.

Qualifications

• Degree in Computer Science, Data Analytics, Mathematics
• MIS/CIS, AI, or Machine Learning, or equivalent experience.
• Recognised SQL / Database qualification (Microsoft, Oracle, or equivalent).
• Relevant certification in Financial Crime, Fraud, or Cybercrime is desirable.
• Exceptional candidates without formal qualifications but with equivalent experience will also be considered.

In return

• Competitive salary between £60,000 and £70,000, dependent on experience.
• Hybrid working – two days per week in the West Midlands office.
• Ongoing professional development and access to advanced Financial Crime training.
• The opportunity to shape a newly established role within a growing and innovative team.
• A collaborative and supportive environment focused on continuous improvement and data-led decision making.

How to Apply If you’re an experienced financial crime systems analyst looking for a new challenge and the opportunity to influence how a leading financial institution protects its customers, please send your CV and a short cover letter outlining your experience and motivation for the role.

Applicants must have full UK working rights - sponsorship is not available for this position.