[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.

Figure 1: Expansion of Duplicate Claim Detection Range via Fuzzy Matching Implementation (Estimated Values)

2. Implementation and Evaluation of Fuzzy Matching Algorithms

To improve the accuracy of duplicate claim detection, the use of string similarity algorithms such as Levenshtein distance and Jaro-Winkler distance is essential. These allow for the quantitative measurement of "closeness" between OCR-generated text data, extracting items that exceed a threshold as "suspected duplicates."

Technical visualization of a Japanese data scientist analyzing string similarity scores and fuzzy matching clusters on a high-resolution display, optimizing the fraud detection threshold for corporate expense management.

It is common to use the F1 score (the harmonic mean of precision and recall) as a quantitative evaluation metric. Tuning to minimize false negatives (misses) while suppressing false positives (false alarms) is where AI engineers demonstrate their expertise. In particular, model construction is required that accounts for receipt formats unique to Japanese business practices and the accuracy of OCR analysis for handwritten characters.

3. Multidimensional Fraud Detection Using Image Hash Values

In addition to text-based detection, "Perceptual Hashing"—which compares the similarity of the images themselves—has gained attention in recent years. This method extracts features from the image of the receipt to generate a hash value. Even if the OCR misreads characters, "receipts that look the same" can be detected with high accuracy.

A conceptual diagram showing how perceptual hashing converts Japanese receipt images into unique digital fingerprints to identify duplicate physical documents even when text data varies slightly.

This multi-layered detection approach makes it possible to uncover malicious fraud, such as intentionally rewriting amounts and resubmitting, based on image layout features. From a data integrity perspective, the process of verifying evidence from both image and text aspects serves as powerful evidence for audit compliance.

4. Operational Automation and Future Outlook

Improvements in AI detection accuracy dramatically reduce the cost of visual inspections by accounting departments. As of 2026, the mainstream trend is a "hybrid workflow" where low-risk claims are automatically approved based on detected risk scores, and only high-risk ones are scrutinized by humans. This allows for resolving the trade-off between governance and operational efficiency.

FAQ

Q. Won't implementing Fuzzy Matching increase false positives (false alarms)?
A. It is true that lowering the threshold too much will increase false positives. Therefore, it is important to conduct simulations using past claim data and set the optimal threshold according to your company's risk tolerance.
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.