[2026 Latest] Advanced Duplicate Claim Detection via Fuzzy Matching and Quantitative Evaluation of Detection Accuracy

In strengthening corporate governance, fraud detection in expense reimbursement is always a critical challenge. In particular, "duplicate claims"—where the same receipt is submitted multiple times—occur frequently whether intentional or accidental, and cases that are difficult to detect with traditional exact-match searches are on the rise. This article explains the latest detection logic combining AI-driven OCR analysis with Fuzzy Matching, along with its quantitative evaluation methods.

A sophisticated digital dashboard showing AI-driven expense auditing, featuring data visualizations of fuzzy matching algorithms and fraud detection heatmaps in a Japanese corporate fintech environment.

1. The Structure of "Ambiguous Duplicates" in Expense Reimbursement

Traditional expense reimbursement systems only flagged alerts when the date, amount, and payee were an "exact match." However, in actual cases of fraud or error, instances of bypassing exact matches frequently occur due to OCR misreads or subtle differences during manual entry. Examples include variations in notation such as "ABC Co., Ltd." versus "ABC Corp." or OCR misrecognition of "1" and "7."

According to the latest survey data, the potential occurrence rate of duplicate claims in companies prior to AI implementation is estimated to reach approximately 0.8% to 1.5% of all submissions. When converted to a monetary basis, large enterprises face an annual loss risk on the scale of tens of millions of yen.

Q. Can detection still occur if the receipt photo is blurry?
A. While OCR accuracy will decrease, similarity detection using image hashing is highly likely to identify duplicates based on layout and color characteristics.
Q. What kind of ROI (Return on Investment) can be expected from implementation?
A. For companies with 1,000 or more employees, ROI can be achieved within one year through direct cost savings from preventing fraud and errors, as well as a reduction of over 50% in manual review hours.

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Summary

Duplicate expense claim detection has evolved from traditional "exact matching" to "fuzzy duplicate" detection using Fuzzy Matching and image hashing. This enables the visualization of risks hidden behind OCR misreads or subtle input discrepancies, strengthening corporate governance. Selecting the optimal algorithm based on quantitative evaluation is essential for accounting operations in the DX era.

Published: June 5, 2026 / By: Osamu Yasuda

WRITTEN BY
Osamu Yasuda

Osamu Yasuda

Senior Managing Director & COO

Meets Consulting Inc.

References

  • [1] Information Processing Society of Japan: Latest Trends in Duplicate Document Detection Using Natural Language Processing (2025)
  • [2] Financial Services Agency: Guidelines for AI Utilization in Corporate Governance (2026)
Disclaimer: This article is for informational purposes only and is not intended as a substitute for professional advice. It does not guarantee specific results.