A Study on the Multimodal Fraud Transaction Detection Model Based on Financial Transactions and Signature Activities 


Vol. 15,  No. 2, pp. 169-179, Feb.  2026
https://doi.org/10.3745/TKIPS.2026.15.2.169


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  Abstract

The importance of detecting abnormal transactions and detecting signature forgery or alteration is increasing due to the proliferation of non-face-to-face financial transactions. Existing technology has not sufficiently reflected the dynamic characteristics of signature behavior by relying on single modal data. This study proposes a multimodal artificial intelligence model (MAIFDM) that integrates and analyzes transaction data, signature images, and handwriting behavior time series. MAIFDM combines time-space attention learning, context embedding learning, and time-series correlation mismatch learning to fuse the features of the three modules and then determines whether there is an abnormality through the Mahalanobis distance and adaptive dynamic threshold. As a result of the experiment, MAIFDM showed superior performance with F1-score 0.907 and AUC 0.942 compared to the existing model, proving that it is an effective model for multimodal data learning and fraudulent transaction detection.

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[IEEE Style]

C. Sung, K. Park, T. Park, "A Study on the Multimodal Fraud Transaction Detection Model Based on Financial Transactions and Signature Activities," The Transactions of the Korea Information Processing Society, vol. 15, no. 2, pp. 169-179, 2026. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.169.

[ACM Style]

Chan-sik Sung, Kwan-yeol Park, and Tae-yang Park. 2026. A Study on the Multimodal Fraud Transaction Detection Model Based on Financial Transactions and Signature Activities. The Transactions of the Korea Information Processing Society, 15, 2, (2026), 169-179. DOI: https://doi.org/10.3745/TKIPS.2026.15.2.169.

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