Detecting Synthetic Proof of Address
Blog post from Didit
Excluded from normalized aggregate trends after staff review: 3056 posts were attributed to March 2026; 671 shared March 14, 2026. The preceding six-month median was 13.5 posts.
Review evidence: 3,056 posts in March 2026; 671 shared March 14, 2026; preceding six-month median 13.5. Reviewed August 9, 2026.
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Synthetic identity fraud often combines stolen personal data with fabricated details, including falsified proof-of-address documents, to open accounts, obtain credit, and facilitate financial crime; the FTC reportedly estimated related losses at $59 billion in 2021. Utility bills and bank statements are especially vulnerable because their verification methods may rely on basic OCR and pattern matching, while generative AI can now create or alter highly convincing documents that evade visual inspection. Effective detection requires layered controls such as document anomaly analysis, AI-based manipulation and deepfake detection, verification with issuing institutions, cross-checks against public and credit data, and geolocation validation. Didit presents its platform as a unified fraud-prevention solution that combines these methods with customizable workflows and iBeta Level 1-certified liveness detection to reduce fraud risk, support compliance, and limit manual reviews.
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