{"paper_id":"40d817a5-bfcf-4680-bc63-2fbc500ccc5b","body_text":"Financial Anomaly Transaction Detection Using Autoencoder-Based Models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 February 2026 V1 Latest version Share on Financial Anomaly Transaction Detection Using Autoencoder-Based Models Authors : Shixiong Xu , Naxi Chen , Pengfei Pan 0009-0008-1984-9916 , and Ziyue Wang Authors Info & Affiliations https://doi.org/10.22541/au.177187997.78014742/v1 251 views 140 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Financial fraud and anomaly transaction detection have become crucial research topics in the field of financial technology. With the rapid development of electronic payments and online transactions, financial institutions need to process massive amounts oftransaction data daily, which contains various types of abnormal behaviors and fraudulent patterns. Traditional rule-based detection methods often struggle to adapt to evolving fraud techniques and sufer from high false positive rates. In recent years, the rapid development of deep learning technology has provided new solutions for financial anomaly detection. As an unsupervised learning model, autoencoders can automatically learn latent feature representations of normal transactions and identify anomaly patterns through reconstruction errors. This study proposes an autoencoder-based financial anomaly transaction detection model that automatically extracts transaction features by constructing a multi-layer neural network structure and uses reconstruction errors for anomaly determination. We conducted extensive experiments on real financial transaction datasets, validating the superior performance of the proposed method in terms of detection accuracy, recall, and F1-score. Experimental results demonstrate that this method can efectively identify various anomaly transaction patterns, providing strong technical support for risk management in financial institutions. Supplementary Material File (financial anomaly transaction detection using autoencoder-based models.pdf) Download 301.38 KB Information & Authors Information Version history V1 Version 1 23 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords ccs concepts: • computing methodologies → neural networks unsupervised learning • computer systems organization → anomaly detection autoencoder, anomaly detection, financial fraud, deep learning, transaction monitoring • general and reference → evaluation Authors Affiliations Shixiong Xu View all articles by this author Naxi Chen View all articles by this author Pengfei Pan 0009-0008-1984-9916 View all articles by this author Ziyue Wang View all articles by this author Metrics & Citations Metrics Article Usage 251 views 140 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shixiong Xu, Naxi Chen, Pengfei Pan, et al. Financial Anomaly Transaction Detection Using Autoencoder-Based Models. Authorea . 23 February 2026. DOI: https://doi.org/10.22541/au.177187997.78014742/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {\"doi\":\"10.22541/au.177187997.78014742/v1\",\"type\":\"Article\"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob(\"bG9jYXRpb24=\"),_bnb=atob(\"b3JpZ2lu\"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(\" \")); $.get(\"/resource/lodash?t=\"+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML=\"window.__CF$cv$params={r:'9fe217992ab941e2',t:'MTc3OTE4MzEyMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);\";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();","source_license":"CC-BY-4.0","license_restricted":false}