We are the Swiss Leader in Online Banking, providing trading, investing, and banking services to over 650,000 clients through our secure digital platforms.
At Swissquote, we embrace diversity and are committed to equal opportunity. We welcome candidates from all backgrounds, experiences, and perspectives to join our team and contribute to our shared success.
Job Description
* As a member of the Middle-Office department, you will play a critical role in identifying, analyzing, and mitigating financial crime threats through advanced forensic investigations and data-driven insights. Leveraging your expertise in digital forensics, threat intelligence, and data analysis, you will develop, enhance, and maintain cutting-edge Fraud and Anti-Money Laundering (AML) detection solutions. This involves collaborating with cross-functional teams to design and implement robust detection frameworks, proactively address emerging risks, and ensure the effectiveness of analytical tools. Staying ahead of evolving technologies and threat landscapes, you will contribute to strategic decision-making and drive innovation in financial crime prevention.
* Maintain a comprehensive view of customer profiles and behaviors by integrating diverse data sources such as digital traffic, payment patterns, personal details, and external intelligence. This helps identify anomalies, detect fraudulent activities, and strengthen financial crime prevention measures.
* Analyze and define data requirements within the fraud and financial crime threat intelligence lifecycle, transforming raw data into actionable insights to optimize detection frameworks, improve risk assessment, and develop targeted mitigation strategies.
* Drive the development and continuous improvement of advanced Fraud and AML detection and prevention solutions.
* Collaborate with IT, software engineering, and data teams to identify, map, and integrate new data sources for enhanced threat detection and forensics capabilities.
* Develop and fine-tune detection rules and models to support proactive threat hunting, anomaly detection, and financial crime mitigation.
* Contribute to designing, implementing, optimizing, and executing operational and administrative controls to strengthen fraud defenses.
* Automate fraud processes to reduce manual workloads and improve workflow efficiency. Design and track performance metrics for detection systems, refining KPIs and KRIs to measure effectiveness.
* Create and maintain detailed documentation for detection tools, data flows, and related systems to ensure transparency and consistency.
* Apply a systematic and analytical approach to problem-solving, demonstrating strong communication and collaboration skills, and taking ownership to drive results.
* Conduct risk management activities, including comprehensive fraud and risk assessments for new initiatives and supporting business teams in mitigation strategies.
* Ensure all product and customer risks are mapped and mitigated through the detection use case library and Fraud & AML platform capabilities.
* Perform threat modeling to identify vulnerabilities, assess risks, and design effective countermeasures to enhance fraud defenses.
* Assist clients and partners on fraud-related issues and inquiries.
* Lead fraud incident management, including forensic analysis, evidence documentation, and reporting.
* Collaborate with IT and Operations teams for swift incident resolution.
* Participate actively in team activities to support the overall success of the Fraud function.
Qualifications
* Bachelor’s or Master’s degree in Data Science, Computer Science, Forensic Accounting, or related fields.
* Experience as a Product Owner or Project Manager, with a successful track record in delivering complex projects.
* Extensive experience (5-8+ years) in reporting, data analytics, visualization, querying, and interpreting complex datasets, using tools like Snowflake, Tableau, Elastic Stack, etc.
* Deep understanding of AML and fraud prevention frameworks, with hands-on experience in threat modeling, e-discovery, and digital forensics.
* Experience with threat intelligence lifecycle, including collection, analysis, and dissemination of actionable intelligence.
* Advanced knowledge of data mining, visualization, and machine learning applications for fraud detection.
* Proficiency in internal fraud investigations, prevention, and mitigation strategies.
* Experience with relational and non-relational databases, writing/executing queries (e.g., SQL, ES|QL), and scripting languages like Python, Perl, Bash.
* Excellent communication skills in English, capable of engaging with diverse stakeholders and vendors.
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