Meta-universe Financial Transaction Anomaly Detection and Risk Prediction based on Machine Learning

DOI

10.2991/978-94-6463-540-9_14How to use a DOI?

Keywords

Machine learning; ensemble learning; data analysis; anomaly detection

Abstract

As blockchain, virtual reality, and artificial intelligence rapidly advance, the Metaverse is shifting from sci-fi to actuality. This evolution not only promises to transform human existence but also stands to profoundly influence financial transactions. Representing the next-gen Internet, the Metaverse strives to establish a fully immersive, temporally dynamic, self-sufficient virtual environment for human interaction across leisure, professional, and social domains. This paper delves into the analysis of blockchain financial transaction datasets within an open Metaverse environment, aiming to detect anomalous data and fraudulent activities. Employing a spectrum of machine learning models and deep learning methodologies, including support vector regression, linear regression, random forests, neural networks, and XGBoost, this study seeks to analyze and predict abnormal transactions and fraudulence. Furthermore, it aims to assess the risk associated with transactions within the Metaverse and establish a comprehensive Metaverse transaction risk scoring model. The findings underscore the efficacy of employing Random Forest and XGBoost models in crafting risk scoring models within the Metaverse context.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Muxuan Li
PY  - 2024
DA  - 2024/10/16
TI  - Meta-universe Financial Transaction Anomaly Detection and Risk Prediction based on Machine Learning
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 117
EP  - 129
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_14
DO  - 10.2991/978-94-6463-540-9_14
ID  - Li2024
ER  -