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AI Fraud Claims Detection Specialist

Carter Andersen

Full-time · Job · Chicago

About the role

Insurance fraud costs the industry $80 billion per year in the US alone. For our clients — regional P&C insurers processing 15,000 claims per month — fraud detection is the difference between a sustainable loss ratio and a company that loses money on every policy. The current detection system flags 3.2% of claims for investigation. Industry data suggests 8-10% of submitted claims involve some form of fraud. We're catching less than half of it. You'll lead the fraud detection model redesign. The problem is multi-layered: first-party fraud, staged accidents, provider billing fraud, and organized ring activity all look different and require different signals. You'll build a multi-model architecture that runs specialized detectors in parallel and aggregates scores through a meta-learner, using features from claims documents, claimant history, network relationships, and external data signals. This is a senior IC role with significant autonomy. You'll define the detection architecture, own the training pipeline, and work directly with the Special Investigations Unit to understand what human investigators actually need from your model outputs.

Responsibilities

  • Design multi-model fraud detection architecture for P&C insurance claims
  • Build specialized detectors for different fraud typologies: first-party, staged accidents, provider fraud
  • Develop network analysis features capturing relationships between claimants, providers, and attorneys
  • Create explainability outputs that guide Special Investigations Unit prioritization
  • Build active learning loops incorporating investigator feedback into training data
  • Monitor recall against confirmed fraud rates and retrain on emerging patterns

Requirements

  • 6+ years in ML engineering with fraud detection or financial crime domain experience
  • Experience with graph features and network analysis for fraud ring detection
  • Strong Python, SQL, and statistical modeling skills
  • Deep understanding of precision-recall trade-offs in high-stakes classification
  • Ability to collaborate with investigative domain experts

Benefits

  • Fully remote with flexible schedule
  • Comprehensive health benefits
  • $4,000 annual professional development budget
  • Performance bonus tied to detected fraud recovery
  • Equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn sql spark

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Level

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Language

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Salary Range

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AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn sql spark statistics

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