Senior Manager Fraud Strategy and Analytics

Senior Manager Fraud Strategy and Analytics US

New York, New York, United States

Raisin is the world's leading platform for savings and investment products. Founded in 2012, Fin Tech connects consumers with banks in the EU, the UK and the US. This gives consumers better interest rates and banks a diversified form of refinancing. Our vision is to offer savings and investments without barriers and thus open up the global +150 trillion euro market.

Raisin works with over 300 banks. Today, the platform holds over 75 billion euros in assets from around one million customers which have accrued over 5 billion euros in interest with their investments.

The Risk Operations team safeguards Raisin’s US business by monitoring, detecting, and mitigating risks across the customer lifecycle. We manage fraud prevention, investigations, payments risk, and AML/KYC oversight. The team partners closely with Product, Engineering, Compliance, and Customer Service to ensure safe and frictionless customer experiences. As a multi‑bank deposit marketplace targeting rapid growth, Raisin faces complex, evolving fraud challenges across onboarding, identity verification, bank linking, and money movement.

Risk Operations is at the center of designing strategies, controls, and analytical foundations that protect the platform at scale.

Role seniority – This role directly reports to the Head of Risk Operations and is a senior expert role at Raisin US.

As Fraud Strategy & Analytics Senior Manager, you build and scale Raisin U.S.’s fraud detection capabilities, combining strategy, analytics, and hands‑on data science to enhance fraud posture while optimizing customer experience. You will work cross‑functionally to develop models, design detection rules, analyze fraud telemetry, and inform decisions across the business. This role is foundational to building Raisin’s next‑generation fraud modeling and analytics framework without compromising customer experience.

• Own the roadmap for fraud models across the customer lifecycle.
• Design, implement, and deploy machine learning models (supervised and unsupervised) for anomaly detection, identity risk, behavioral analytics, and transaction monitoring.
• Build features from device, behavioral, graph, identity, ACH/payment telemetry, and network data.
• Partner with Engineering and Data to ensure high‑quality data pipelines for real‑time model scoring.
• Translate model outputs into practical decision strategies (rules, thresholds, step‑up flows).
• Evaluate and pilot next‑generation fraud technologies (e.g. behavioral biometrics, graph networks).

• Own and execute Raisin’s U.S. fraud strategy across onboarding, account linking, and payment flows.
• Design and refine fraud policies, rules, controls, and escalation processes.
• Lead fraud investigations end‑to and translate findings into durable controls.
• Build fraud KPIs, dashboards, and performance reporting for leadership.
• Quantify fraud exposure, loss trends, and efficiency metrics to inform strategic decisions.
• Analyze payment flows and ACH telemetry to identify systemic risks.
• Measure control effectiveness, false positives, and customer friction.
• Provide data‑driven insights and recommendations to leadership.
• Financial crime in partnership with Compliance.
• Support AML/KYC by integrating fraud insights into onboarding and monitoring workflows.
• Maintain customer risk profiling frameworks and enhance monitoring of high‑risk customers.
• Work with Compliance and partner banks to lift unusual activity per regulatory expectations.

• 8+ years in fraud risk management, fraud analytics, fraud data science, AML/financial crime, or payments risk (fintech, digital banking, payments a strong plus).
• Strong technical skills:
  Python, SQL, and experience with feature engineering, model development, and fraud analytics.
• Proven ability to design, deploy, and monitor machine learning models in production.
• Deep familiarity with U.S. fraud typologies: identity fraud, mule activity, ATO, synthetic IDs, ACH fraud, bank linking fraud, and payment abuse.
• Understanding AML/KYC frameworks and how they…