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2026-07-15 · 8 min read

Bank Statement Fraud Detection: Metadata, Balance Chains, and Scores

How bank statement fraud detection APIs catch edited PDFs: metadata inspection, balance-chain validation, font consistency, and an aggregate 0–100 tamper score.

Edited statements usually break in three places: metadata, math, and layout. A fraud API should check all three and explain itself with a signal list — not a black-box number.

Metadata inspection looks at PDF creator/producer (Canva, Photoshop, and online editors are red flags), creation vs modification dates (mod-before-creation means tampering), and stripped metadata (no creator at all).

Balance-chain validation recomputes the running balance row-by-row from the opening balance and flags every mismatch with its deviation. Clean statements reconcile across all rows; edited ones break exactly where the numbers were changed.

Font and layout checks count distinct fonts across pages — mixed fonts suggest copy-paste edits. Round-number and date-order anomalies add the final layer.

The output is an aggregate 0–100 fraud score (plus a trust score) with a clean / review / suspicious verdict and every triggered signal. Route clean straight through, review the middle, and kick suspicious to manual underwriting.

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Bank Statement Fraud Detection: Metadata, Balance Chains, and Scores — Bank Statement Analyser