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Raisin · 2 weeks ago

Senior Manager Fraud Strategy and Analytics US

New York, NY

Full-time

Hybrid

Senior Level, Lead/Staff

8+ years exp

Raisin is the world's leading platform for savings and investment products, connecting consumers with banks. The Senior Manager Fraud Strategy and Analytics will enhance fraud detection capabilities and develop models to mitigate risks while ensuring a seamless customer experience.

BankingFinanceFinancial ServicesFinTechMarketplace

Growth Opportunities

Responsibilities

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-end 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

Support AML/KYC strategy 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 escalate unusual activity in accordance with regulatory expectations

Qualification

Fraud risk managementMachine learning modelsFraud analyticsPythonSQLAML/KYC frameworksFraud typologiesCommunicationStrategic mindsetLeadership experience

Required

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 of AML/KYC frameworks and how they intersect with fraud controls

Experience influencing decisions across Product, Engineering, Compliance, and Operations

Excellent communication skills with the ability to present insights to executive leadership

Strategic, analytical mindset with a focus on balancing fraud prevention, operations scalability, and customer experience

Leadership experience- formal or informal- and ability to scale influence across teams