FULLTIME
Director, Fraud Strategy and Analytics

Applied Data Finance
Not specified · onsite · Posted 13d ago
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Section · 01
About this role
Role Summary Senior leader accountable for fraud strategy, analytics, and operating rhythms across a consumer lending portfolio. You will own the end-to-end fraud strategy roadmap—policy and rule governance, scorecard and signal performance, portfolio monitoring, executive reporting, and fraud operations partnership—balancing fraud loss mitigation against approval rates and customer experience. You will lead and grow a team of fraud risk data scientists, set the analytical agenda, and serve as the senior fraud strategy partner to product, credit/risk, fraud operations, data engineering, and external vendors. The role is strategy- and analytics-led with oversight of fraud models, scorecards, third-party signals, and decisioning tools.
Key Responsibilities • Own and evolve the fraud strategy roadmap across acquisition and account fraud, balancing loss reduction with approval rates, conversion, and customer experience to meet portfolio-level KPIs. • Govern fraud policy, rules, and decisioning thresholds end-to-end—standards for proposal, review, approval, deployment, monitoring, and retirement—with clear sign-off across risk, compliance, and operations. • Direct portfolio fraud monitoring and operating rhythms—loss rates, capture rates, false positive rates, approval impact, vintage and segment KPIs—and drive timely action when performance drifts. • Oversee fraud model and scorecard performance (PSI, score drift, KS, decay) and the cadence for recalibration, rule adjustment, and escalation; partner with data science on model strategy, but lead the strategy, deployment, and monitoring layer. • Set the agenda for emerging fraud trend, ring, and cross-channel vulnerability detection; ensure findings are sized, prioritized, and translated into rule, policy, and operational responses. • Lead third-party fraud and identity signal strategy—identity verification, device intelligence, consortium data, bank/transaction data—including vendor evaluation, benchmarking, contract/commercial input, and onboarding/retirement decisions. • Partner with fraud operations to align real-time monitoring, queue strategy, and case outcomes with analytics; convert investigator insight into rule, policy, and reporting changes. • Partner with product, data engineering, and decisioning platform teams to evolve fraud data sources, decision engine capabilities, and monitoring infrastructure; represent fraud strategy in cross-functional prioritization. • Define the test-and-learn standard—champion/challenger, policy backtests, and holdouts—to quantify the impact of strategy changes on fraud, approvals, and downstream credit performance. • Produce and present executive-level fraud reporting and deep dives—loss attribution, typology trends, decisioning outcomes, and strategic options—for senior leadership. • Serve as the senior fraud strategy expert for the company; brief executives, support audits and regulatory reviews, and represent fraud strategy externally with vendors and partners. • Lead, coach, and grow the fraud strategy and analytics team, setting priorities, performance expectations, and career paths.
Qualifications • 10+ years in fraud strategy, fraud analytics, or fraud risk management in consumer lending, fintech, or financial services, including 3+ years leading teams. • Track record of owning a fraud strategy and analytics function—setting strategy, governing policies and rules, and delivering measurable improvements in fraud loss, approval rates, and customer experience. • Deep understanding of fraud typologies in consumer lending (identity, synthetic, first-party, third-party, account takeover) and how they manifest across application and account data. • Strong working knowledge of fraud models and scorecards—how they are built, evaluated, and monitored—with the ability to set strategy, interpret outputs, and direct technical partners without owning hands-on model development. • Demonstrated experience translating analytics into clear policy, rule, and threshold decisions and driving them through a decisioning platform end-to-end. • Experience evaluating and managing third-party fraud and identity vendors, including commercial and performance trade-offs. • Excellent executive communication; able to translate fraud analytics and trade-offs into recommendations for senior leadership, operations, and technical partners. • Bachelor’s degree in a quantitative field (Statistics, Economics, Mathematics, Computer Science, Engineering, or related); advanced degree a plus.
Preferred Qualifications • Consumer lending experience, or other high-fraud-risk credit products. • Familiarity with US consumer lending regulations and risk management practices. • Experience standing up or materially upgrading a fraud strategy function, including operating. rhythms, governance, and team build-out. • Exposure to graph or network analysis for fraud ring detection and to real-time decisioning platforms.
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Section · 02
Skills
Section · Company
About Applied Data Finance

Applied Data Finance
FinTech
500
employees
2014
12 years old
Chennai, Tamil Nadu
India
₹13.1L PA avg
Avg at Applied Data Finance
About
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10 reviews
Culture
5.0
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5.0
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5.0
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