Context Aware AI for Multi-Modal Fraud Detection Using IP Pattern and Human Interaction Behavior

DOI

10.2991/978-94-6239-616-6_46How to use a DOI?

Keywords

Financial fraud detection; Deep learning; Device pattern analysis; IP anomaly detection; KYC validation; Identity fraud; BERT; Autoencoder; Multi-Layer Perceptron (MLP)

Abstract

Financial fraud continues to threaten digital banking and online payment systems, particularly through device misuse, IP manipulation, and identity-related inconsistencies. Existing detection methods, including blacklist-based systems, clustering algorithms like DBSCAN, and optimization techniques such as genetic algorithms, have achieved limited success. They often rely on static rules, struggle with evolving fraud patterns, and produce high false alarm rates, making real-time detection in large-scale financial environments challenging. This research proposes an AI-driven fraud detection framework using deep learning models—BERT, Multi-Layer Perceptron (MLP), and Autoencoders—to analyze device usage patterns, IP anomalies, and KYC irregularities. BERT captures textual and sequential identity features, MLP classifies accounts as fraudulent or legitimate, and Autoencoders detect anomalies by reconstructing normal behavioral profiles and flagging deviations. Unlike clustering or optimization-based approaches, this framework learns directly from raw behavioral and contextual features, enabling adapt ability to novel fraud strategies. The system is scalable, real-time, and reduces false positives, offering financial institutions a robust solution against identity forgery, device/IP manipulation, and evolving fraud behaviours.

Copyright

© 2026 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  - P. Karthikeyan
AU  - S. Geetha
AU  - P. Janani
AU  - V. Abiya
AU  - S. Hemma Villacini
PY  - 2026
DA  - 2026/03/31
TI  - Context Aware AI for Multi-Modal Fraud Detection Using IP Pattern and Human Interaction Behavior
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 612
EP  - 627
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_46
DO  - 10.2991/978-94-6239-616-6_46
ID  - Karthikeyan2026
ER  -