Research Paper

Core Innovation

This paper introduces FraudFox, which uses Extended Kalman Filters to dynamically weight multiple fraud risk oracles and derives an optimal decision surface to allocate investigations under constraints. It uniquely adapts to adversarial fraudster behavior in real time, improving detection accuracy and operational efficiency.

Why It Matters

Fraud detection is critical for e-commerce platforms to minimize financial losses and operational costs. FraudFox improves decision-making by dynamically adapting to changing fraud patterns and balancing investigation capacity with fraud risk. This scalability and adaptability reduce false positives and losses, enhancing operational efficiency and customer trust.

Potential Customers & Pain Points

• E-commerce platforms – Need to reduce fraud losses and investigation costs
• Financial institutions – Require adaptive fraud risk scoring
• Payment processors – Need to optimize fraud detection under resource constraints
• Retailers – Want to balance fraud prevention with customer experience.

Business Model

SaaS subscription model targeting large e-commerce and financial enterprises with tiered pricing based on transaction volume and feature set.

Competitive Landscape

• Forter
• Riskified
• Sift
• Kount
• Fraud.net

Implementation Challenges

• Integration complexity with existing fraud systems
• Maintaining adaptation accuracy against evolving fraud tactics
• Balancing false positives with operational costs

Validation Strategy

• Pilot deployments with major e-commerce platforms
• A/B testing comparing FraudFox against existing fraud detection systems
• Measuring reduction in fraud losses and investigation costs over time