Detect fake damaged-on-arrival photos for e-commerce refunds

Detect fake damaged-on-arrival photos for e-commerce refunds targets a $18.0B = 3,000,000 online merchants x $6,000 ACV. Rationale: broad pool of global e-commerce sellers who can subscribe to base image-forensics + rules engine to reduce refund/return fraud. total addressable market with medium saturation and a year-over-year growth rate of 15% CAGR in fraud-detection and returns-management spend driven by rising e-commerce volume and fraud sophistication.

Key trends driving demand: Rising e-commerce returns volume -- more returns increase exposure to fraudulent photos and DOA claims, making scale automation necessary.; Proliferation of easy image-editing and generative tools -- higher realism of fakes forces merchants to move from manual heuristics to forensics.; Platform liability and marketplace policies -- marketplaces tighten seller protections and dispute processes, increasing demand for verifiable evidence.; Shift to mobile-first shopping -- potential to embed trusted-capture at point of returns initiation to collect higher-quality evidence..