Sr. Manager, Fraud Analytics and Dealer Monitoring

Job Overview

Sr. Manager, Fraud Analytics and Dealer Monitoring (260000GF) is responsible for developing, leading, and continuously enhancing enterprise fraud analytics and dealer monitoring capabilities across the full fraud lifecycle—prevention, detection, investigation support, and control optimization. The role designs and governs a comprehensive dealer performance and risk monitoring framework, establishes early warning indicators and escalation protocols, and delivers executive‑ready dashboards, reporting, and insights spanning credit performance, booking quality, funding exceptions, policy adherence, confirmed fraud, and loss outcomes.

Partnering closely with Sales, Consumer Credit/Origination, Servicing, Compliance, Investigations, Digital/Data teams, and OEM stakeholders, the manager translates complex, multi‑source signals into actionable strategies that reduce fraud and repurchase exposure, improve time‑to‑detect, and strengthen dealer network health. The role also measures control effectiveness, drives continuous tuning and emerging‑scheme response, and mentors an analytics team to deliver scalable, data‑driven solutions using modern analytics and AI/ML where appropriate.

• Analytics & Insights:
  • Lead the development, prioritization, and socialization of fraud‑prevention insights that connect dealer behaviors to downstream outcomes (e.g., early default, loss severity, repurchase exposure).
  • Oversee and drive the creation of executive‑ready analytics, reporting, and evidence‑based narratives by synthesizing signals across underwriting, funding, servicing, and investigations into actionable storylines.
  • Incorporate investigation, servicing, and other multi‑source risk signals into analytics deliverables to strengthen fraud detection, support case triage, and improve decision‑making.
  • Measure and communicate effectiveness of fraud controls and monitoring enhancements (hit rates, false positives, time‑to‑detect, prevented loss estimates) and drive continuous tuning based on observed drift and emerging schemes.
  • Partner with Digital/Data teams to advance scalable fraud analytics capabilities and support the use of modern analytics, machine learning, and AI where appropriate.
  • Design, implement, and oversee a comprehensive dealer monitoring framework covering credit performance, fraud risk, operational risk, compliance, and profitability.
  • Establish early warning indicators (EWIs) and escalation protocols to proactively identify dealers exhibiting adverse trends or emerging risks, including emerging fraud schemes, funding exception trends, brand‑specific and cross‑brand shifts in fraud typologies, dealer concentration, or customer behavior, and performance gaps or drift in fraud models, rules, and vendor scores.
  • Guide the team in developing dealer risk segmentation dashboards and monitoring routines that support governance, escalation, and decision‑making, leveraging large data sets and emerging technologies such as machine learning and AI.
  • Monitor dealer‑level and portfolio‑level performance across key metrics including delinquency, losses, booking quality, funding exceptions, confirmed fraud, and policy adherence.
• Cross‑Functional
  
  Collaboration:
  
  
  • Partner with Sales, Dealer Development, Consumer Credit/Origination, Servicing, Compliance, Investigations, and Digital/Data teams to strengthen fraud detection, case support, control design, and dealer monitoring effectiveness.
  • Act as a thought partner to business and field partners, balancing risk management with growth, portfolio performance, and dealer relationship objectives.
  • Represent the fraud analytics and dealer monitoring function in cross‑functional forums and present findings and recommendations to senior leadership and key stakeholders.
  • Lead, mentor, and develop a team of analytics professionals, fostering a culture of continuous improvement, analytical rigor, and business partnership, while setting clear goals, performance metrics, and development plans for team members.

• Minimum 8 years’ experience in operational analytics development, data analysis, and building data insights and supporting narratives.
• Minimum 3 years…