{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:54:32Z","timestamp":1776088472101,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>The age we are living in today is the age of digital advancement, and more needs to be done to tackle issues that have called for the creation of robust and efficient fraud recognition systems. This paper presents a hybrid model using the Proximal Policy Optimization (PPO) technique with improved artificial bee colony (ABC) optimization for detecting credit card fraud. Fraud detection belongs to one of the key difficulties in the accuracy of classification, particularly when it is required to deal with imbalanced classification\u2014the majority class overwhelming the minority class leads quite often to its misclassification. Our architecture surmounts this challenge by enhancing the training of the reward mechanism of the PPO algorithm that increases the success rate of recognition of the minority class. It not only extends the definition of classification but also integrates it in an Artificial Neural Network (ANN) based architecture as a sequential decision-making task. More importantly, the rewards given for the right sampling with more emphasis on the minority class in turn enhance the capturing capabilities of the model concerning fraudulent transactions. Also, the ABC algorithm of cooperative communication features between the population is used since it can do better at the initial weight levels, which is important in solving problems that are highly complex and of high-dimensional space. The efficiency of this optimization algorithm is tested on credit card databases collected by Universit\u00e9 Libre de Bruxelles. A comparison of the empirical results obtained against performance evaluation metrics shows that the algorithm is high in accuracy and, thus, effective in fraudulent transaction detection for e-commerce.<\/jats:p>","DOI":"10.31449\/inf.v50i1.8099","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:05:54Z","timestamp":1776085554000},"source":"Crossref","is-referenced-by-count":0,"title":["E-Commerce Fraud Detection: An Integrated Approach with Mutual Learning-based Artificial Bee Colony and Proximal Policy Optimization Algorithms"],"prefix":"10.31449","volume":"50","author":[{"given":"Yuanyuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,13]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8099\/6600","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8099\/6600","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:05:54Z","timestamp":1776085554000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/8099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,4,13]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i1.8099","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}