# Fraud & FinCrime Data Analyst at Paysend

Paysend is hiring a Fraud & FinCrime Data Analyst. Level rates it Little AI ●○○○; you can [apply on Paysend](https://jobsbylevel.com/go/4971d420-c08e-4373-a00e-e2f409c55701?utm_medium=ai-agent).

AI Level 1, AI centrality 21 out of 100. Beograd, Serbia | Europe, Europe.

## Details

- Company: [Paysend](https://jobsbylevel.com/companies/paysend)
- AI level: AI Level 1 (score 21 out of 100)
- Location: Beograd, Serbia | Europe, Europe
- Posted: November 5, 2026
- Apply: https://jobsbylevel.com/go/4971d420-c08e-4373-a00e-e2f409c55701?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=fraud-fincrime-data-analyst-at-paysend-b563da

## Description

About The Role: We are looking for a technical analyst to join our FinCrime team as one of the owners of our rules framework, KPI and activity tracking, and the continuous improvement of our detection capabilities. You will identify gaps, propose solutions, and drive them to delivery alongside Product, Compliance, Network and Engineering, keeping us ahead of bad actors without hurting speed or user experience. What you'll co-own Rules lifecycle: design, build, test, tune and retire fraud and AML/TF detection rules, from hypothesis through backtesting and deployment to performance review. KPI framework: define, build and maintain metrics for detection effectiveness, false positives, losses, alert volumes and operational productivity. Activity tracking: reporting on rule changes, alert handling and team throughput, with dashboards that stakeholders actually use. Detection improvement: proactively find new typologies, data signals and controls, and turn them into live rules or models. What you'll do Build and improve detection logic: translate fraud and financial crime typologies into rules; use SQL and Python to backtest, simulate and measure impact before release. Monitor and analyze: track real-time and batch transaction data, spot anomalies and emerging attack patterns, and run deep-dives on loss drivers and rule performance. Drive automation: replace manual analysis and reporting with scalable pipelines, scheduled jobs and self-serve dashboards. Collaborate as a partner: Product and Engineering: define requirements, data needs and system enhancements. Compliance: make sure detection meets AML/TF, KYC and regulatory expectations, and support audits and partner due diligence. Network: align on Visa and Mastercard program requirements, thresholds and monitoring. Operations: use investigation feedback to refine rules and reduce noise. Support regulatory compliance: stay current on AML regulations, PSD2/SCA, and card scheme rules, and help embed them into controls. Take responsibility and escalate: document decisions and rule rationale clearly, and escalate material risks early. What You’ll Need To Be Successful In This Role: Experience in fraud or financial crime analytics, ideally in fintech, payments or high-volume B2C. Proven hands-on experience with rule-based detection: designing, tuning and evaluating rules, with clear trade-off thinking (catch rate vs. false positives vs. customer friction). Advanced SQL and experience with modern data platforms and large-scale data processing (distributed frameworks such as Spark or similar). Python for analysis and automation (pandas, notebooks, scheduled workflows), including experience working with large datasets. Solid understanding of fraud typologies and AML/TF concepts, and awareness of emerging trends. Knowledge of Visa and Mastercard tools, data and monitoring programs (AVS, 3DS, chargeback and fraud programs). Practical knowledge of payment methods (cards, tokenization, Apple Pay, Google Pay, etc.). Experience building dashboards in a BI tool (Looker, Tableau, Power BI or similar). Ownership mindset: you spot problems, propose solutions and see them through without waiting to be asked, while working as part of a team. Clear communication and documentation, able to explain data-driven decisions to technical and non-technical stakeholders. Nice to have Experience with rules engines or decisioning platforms and expression-based rule logic. Experience with advanced methods for anomaly detection (statistical, unsupervised or machine learning approaches) and applying them to fraud or financial crime use cases. Familiarity with how financial crime operations run day to day. Experience working with auditors or regulators, or supporting regulatory reviews and partner due diligence. What you'll achieve First 3 months Understand our products, detection framework, rules engine and data landscape. Learn our KPIs, tooling and key fraud and AML patterns. Build relationships across Product,

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/fraud-fincrime-data-analyst-at-paysend-b563da

Source: https://jobsbylevel.com/jobs/fraud-fincrime-data-analyst-at-paysend-b563da

## Cite this page

Level. https://jobsbylevel.com/jobs/fraud-fincrime-data-analyst-at-paysend-b563da.

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