Fraud and FinCrime Data Analyst

Paysend

. Own the fraud and AML/TF detection rules lifecycle from hypothesis through backtesting, deployment, performance review, tuning, and retirement .

Posted 11/5/2026full-timeBelgrade • SerbiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in fraud and AML/TF detection rule lifecycle management, utilizing SQL and Python for analysis and automation. Proficient in building KPIs, dashboards, and scalable data processing solutions while ensuring compliance with regulatory standards.

Highest-signal resume keywords

Fraud Analytics ExperienceAdvanced SQL ProficiencyPython for Analysis and AutomationExperience with Rules EnginesKnowledge of AML/TF Concepts

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Fraud Detection RulesData AnalysisRule-Based Detection DesignAnomaly Detection MethodsLarge Dataset ProcessingStatistical AnalysisMachine Learning ApproachesKPI DevelopmentBacktesting and SimulationData Signal Identification

Soft Skills

Clear CommunicationDocumentation SkillsStakeholder EngagementProblem-SolvingCollaboration

Tools & Technologies

SQLPythonSparkLookerTableauPower BIDashboardsRules EnginesDecisioning PlatformsFinancial Crime Monitoring Tools

Industry Keywords

AMLTFKYCFraud TypologiesPayment MethodsVisaMastercardFintechB2CRegulatory Compliance

Tech Stack

Tools & technologies

PandasPythonSparkSQLTableau

About the role

Key responsibilities & impact

Own the fraud and AML/TF detection rules lifecycle from hypothesis through backtesting, deployment, performance review, tuning, and retirement
Define, build, and maintain KPIs for detection effectiveness, false positives, losses, alert volumes, and operational productivity
Report on rule changes, alert handling, and team throughput using stakeholder-facing dashboards
Identify new fraud and financial crime typologies, data signals, and controls and turn them into live rules or models
Translate fraud and financial crime typologies into rules using SQL and Python
Backtest, simulate, and measure detection impact before release
Monitor real-time and batch transaction data, identify anomalies and emerging attack patterns, and analyze loss drivers and rule performance
Replace manual analysis and reporting with scalable pipelines, scheduled jobs, and self-service dashboards
Define requirements, data needs, and system enhancements with Product and Engineering
Ensure detection meets AML/TF, KYC, and regulatory expectations and support audits and partner due diligence with Compliance
Align with Visa and Mastercard program requirements, thresholds, and monitoring with Network teams
Use investigation feedback from Operations to refine rules and reduce noise
Stay current on AML regulations, PSD2/SCA, and card scheme rules and embed them into controls
Document decisions and rule rationale clearly and escalate material risks early
In the first 3 months, understand products, detection frameworks, rules engines, data landscapes, KPIs, tooling, and key fraud and AML patterns; build relationships and ship initial tuning improvements
By 6 months, co-own the end-to-end rules lifecycle, launch KPI and activity tracking, and deliver automation removing recurring manual work
By 12 months, contribute to detection effectiveness and reporting, deliver measurable improvements, and introduce detection approaches and automation strengthening compliance

Requirements

What you’ll need

Experience in fraud or financial crime analytics, ideally in fintech, payments or high-volume B2C
Proven hands-on experience designing, tuning, and evaluating rule-based detection
Advanced SQL
Experience with modern data platforms and large-scale data processing, including distributed frameworks such as Spark or similar
Python for analysis and automation, including pandas, notebooks, and scheduled workflows
Experience working with large datasets
Solid understanding of fraud typologies and AML/TF concepts
Awareness of emerging fraud and financial crime trends
Knowledge of Visa and Mastercard tools, data, and monitoring programs, including AVS, 3DS, chargeback, and fraud programs
Practical knowledge of payment methods, including cards, tokenization, Apple Pay, and Google Pay
Experience building dashboards in a BI tool such as Looker, Tableau, Power BI, or similar
Clear communication and documentation skills, with ability to explain data-driven decisions to technical and non-technical stakeholders
Experience with rules engines or decisioning platforms and expression-based rule logic
Experience with advanced anomaly detection methods, including statistical, unsupervised, or machine learning approaches, applied to fraud or financial crime use cases
Familiarity with day-to-day financial crime operations
Experience working with auditors or regulators, or supporting regulatory reviews and partner due diligence

Benefits

Comp & perks

Opportunities to learn, develop new skills, take on new challenges, and advance your career across functions and geographies
Opportunity to shape solutions, challenge the status quo, and see results firsthand
Collaboration with talented colleagues from diverse backgrounds and disciplines
Culture encouraging experimentation, curiosity, action, and continuous improvement
Opportunity to contribute to global money-movement products and services used by millions of customers