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Financial crime - data analyst

Birmingham (West Midlands)
MSA Data Analytics Ltd
Data analyst
Posted: 6h ago
Offer description

Job Description

This newly created role presents an excellent opportunity for an experienced, systems-focused Financial Crime Analyst to make a meaningful impact in strengthening the first line of defence against fraud and financial crime within a FTSE 500 financial services organisation.

You will play a pivotal role in enhancing detection, prevention, and reporting capabilities across multiple Financial Crime systems. Working in a data-rich environment, you will combine technical expertise with analytical insight to identify emerging threats, optimise system performance, and ensure controls remain robust, efficient, and fully compliant.

Key Responsibilities

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

Skills & Experience

* Strong background in Financial Crime or Fraud Analytics within banking or financial services.
* 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.
* Experience 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, with the ability to translate technical findings into actionable business insights.

Qualifications

* Degree in Computer Science, Data Analytics, Mathematics, or a related field.
* MIS/CIS, AI, or Machine Learning qualification, or equivalent experience.
* Recognised SQL/Database certification (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.

Other

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

If you are an experienced Financial Crime Systems Analyst seeking a new challenge and the opportunity to influence how a leading financial institution protects its customers, please submit your CV along with 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.

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