Director, Fraud Strategy & Analytics

Applied Data Finance; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Applied Data Finance seeks a Director of Fraud Strategy and Analytics to own end-to-end fraud strategy, analytics, and operating rhythms across its subprime lending portfolio.

You will lead the strategy roadmaps, governance, and executive reporting, balancing loss reduction with approvals, funding, and customer experience. This role requires deep fraud typology knowledge and hands-on analytical fluency.

Qualifications

• Bachelor's degree in a quantitative field; advanced degree preferred.
• 10+ years in fraud strategy/analytics in consumer lending/fintech; 5+ years leading teams.
• Own fraud strategy function and governance.
• Deep understanding of fraud typologies across multiple categories.
• Hands-on SQL and Python or R; comfortable with cloud data warehouses and BI tools.
• Executive communication and vendor management.

Responsibilities

• Own fraud strategy roadmap across application, funding, and account/post-book fraud.
• Define and maintain the fraud risk management framework and risk appetite.
• Govern policy, rules, and decisioning thresholds end-to-end with cross-functional sign-off.
• Direct portfolio fraud monitoring and KPI tracking; drive action on drift.
• Oversee fraud model and scorecard performance; calibrate and escalate as needed.
• Lead third-party signal strategy and vendor evaluation/negotiation input.
• Partner with fraud operations, product, and data teams to evolve data sources and decisioning.

Skills

SQL

Python

R

Executive communication

Fraud analytics

Leadership

Vendor management

Regulatory knowledge

Decisioning execution

Education

Bachelor's degree in a quantitative field

Advanced degree a plus

Job description

We are looking for a Director of Fraud Strategy and Analytics to own the end-to-end fraud strategy, analytics, and operating rhythms across our subprime consumer lending portfolio.

In this role, you own the fraud strategy roadmap — policy and rule governance, scorecard and signal performance, portfolio monitoring, dispute and chargeback strategy, executive reporting, and fraud operations partnership — balancing loss mitigation against approval rates, unit economics, and customer experience. You set and defend the fraud risk appetite, build and lead a team of fraud risk data scientists and strategists, and act as the senior fraud strategy partner for the product, credit/risk, fraud operations, data engineering, and compliance teams, as well as external vendors. This is a strategy- and analytics-led role with oversight of fraud models, scorecards, third-party signals, and decisioning tools, and it calls for strong hands‑on analytical fluency to direct the work credibly.

Key Responsibilities:

• Fraud Strategy Roadmap: Own and evolve fraud strategy across application, funding, and account/post‑book fraud — covering identity, synthetic, first‑party, third‑party, account takeover, bust‑out, never‑pay, credit washing, mule/APP scams, and income/employment misrepresentation — balancing loss reduction with approval rates, conversion, funding, and customer experience to meet portfolio‑level KPIs.
• Fraud Risk Appetite & Framework: Define and maintain the fraud risk management framework, including fraud risk appetite (e.g., net fraud loss bps, fraud/AR ratio, first‑payment‑default fraud rate), tolerances, and escalation triggers; align the framework to enterprise risk and audit expectations.
• Policy, Rule & Decisioning Governance: Govern fraud policy, rules, and decisioning thresholds end‑to‑end — standards for proposal, review, approval, deployment, monitoring, retirement, and change control — with clear sign‑off across risk, compliance, and operations.
• Portfolio Fraud Monitoring: Direct portfolio fraud monitoring and operating rhythms — loss rates, capture rates, false‑positive rates, approval and funding impact, vintage and segment KPIs, disposition mix — and drive timely action when performance drifts.
• Model & Scorecard Oversight: Oversee fraud model and scorecard performance (PSI, score drift, KS, decay, gain/lift, override rates) and the cadence for recalibration, rule adjustment, and escalation; partner with data science on model strategy, while leading the strategy, deployment, and monitoring layer.
• Emerging Threats & Ring Detection: Set the agenda for emerging fraud trends, ring, and cross‑channel vulnerability detection, including graph and link analysis, velocity signals, and consortium intelligence; ensure findings are sized, prioritized, and translated into rule, policy, and operational responses.
• Third‑Party Signal Strategy: Lead third‑party fraud and identity signal strategy — KYC/CIP, identity verification, device intelligence, behavioral biometrics, consortium data, bank/transaction data, income/employment verification, and credit bureau fraud alerts — including vendor evaluation, benchmarking, commercial negotiation input, and onboarding/retirement decisions.
• Fraud Operations Partnership: Partner with fraud operations to align real‑time monitoring, queue strategy, SLA management, and case outcomes with analytics; convert investigator insight into rule, policy, staffing, and reporting changes.
• Data & Platform Partnership: Partner with product, data engineering, and decisioning platform teams to evolve fraud data sources, feature stores, decision engine capabilities, and monitoring infrastructure; represent fraud strategy in cross‑functional prioritization.
• Test‑and‑Learn Standard: Define the test‑and‑learn standard — champion/challenger, policy backtests, holdouts, and A/B tests — to quantify the impact of strategy changes on fraud, approvals, funding, and downstream credit performance.
• Regulatory & Compliance Alignment: Ensure fraud strategy is aligned with applicable US consumer lending regulations and guidance — including Regulation E, Regulation Z, FCRA, GLBA, UDAAP, BSA/AML/KYC/CIP, OFAC, and the Red Flags Rule — and partner with Compliance, Legal, BSA, and Internal Audit; support SAR narratives, regulatory exams, and audits.
• Executive Reporting: Produce and present executive‑level fraud reporting and deep dives — loss attribution, typology trends, decisioning outcomes, false‑positive and approval trade‑offs, and strategic options — for senior leadership and the Board/Risk Committee.
• External Representation: Serve as the senior fraud strategy expert for the company; brief executives, support audits and regulatory reviews, and represent fraud strategy externally with vendors, consortia, industry working groups, and law enforcement partners.
• Team Leadership: Lead, coach, and grow the fraud strategy and analytics team (strategists, analysts, and data scientists); set priorities, performance expectations, hiring plans, and career paths.

Required Qualifications:

Education & Experience:

• Bachelor's degree in a quantitative field (Statistics, Economics, Mathematics, Computer Science, Engineering, or related); advanced degree a plus.
• 10+ years in fraud strategy, fraud analytics, or fraud risk management in consumer lending, fintech, cards, or financial services, including 5+ years leading teams (managers and individual contributors).
• Function Ownership: Track record of owning a fraud strategy and analytics function — setting strategy, defining risk appetite, governing policies and rules, and delivering measurable improvements in fraud loss, approval rates, and customer experience.
• Typology Depth: Deep understanding of fraud typologies in consumer lending — identity, synthetic, first‑party, third‑party, account takeover, bust‑out, never‑pay, credit washing, mule/APP scams, and income/employment misrepresentation — and how they manifest across application, funding, and account data.
• Model & Scorecard Fluency: Strong working knowledge of fraud models and scorecards — how they are built, evaluated, and monitored — with the ability to set strategy, interpret outputs (KS, PSI, gain, drift), and direct technical partners; hands‑on model development not required.
• Hands‑On Analytical Skills: Proficiency in SQL and at least one of Python or R for independent data pulls, validation, and rapid analysis; comfort in a modern data environment (cloud data warehouses, BI tools, feature stores).
• Decisioning Execution: Demonstrated experience translating analytics into clear policy, rule, and threshold decisions and driving them through a decisioning platform end‑to‑end, including change control and monitoring.
• Vendor Management: Experience evaluating and managing third‑party fraud, identity, device, and consortium vendors, including commercial and performance trade‑offs and contract negotiation input.
• Regulatory Knowledge: Working knowledge of US consumer lending fraud‑adjacent regulation — Reg E, Reg Z, FCRA, GLBA, UDAAP, BSA/AML, KYC/CIP, OFAC, Red Flags Rule — and experience partnering with Compliance, BSA, and Internal Audit.
• Executive Communication: Excellent executive communication; able to translate fraud analytics and trade‑offs into recommendations for senior leadership, operations, and technical partners.

The Company is an equal opportunity employer. The Company makes employment decisions based on merit, and does not unlawfully discriminate based on age, race, creed, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth, breastfeeding and/or related medical conditions), sexual orientation, gender (including gender identity and expression), marital status, national origin, ancestry, citizenship, military or veteran status, physical or mental disability, legally protected medical condition, genetic information, family care status, military caregiver status, reproductive health making decisions, or any other category protected by federal or state law. The Company prohibits discrimination, bullying, or harassment of any individual on any category protected by state or federal law.