Application of AI-based Models for Online Fraud Detection and Analysis

2024-09-25

arXiv.org

AI Summary

This study addresses the underutilization and fragmented research landscape of AI in online fraud detection. Following the PRISMA-ScR protocol, we systematically reviewed 223 studies spanning 16 fraud categories. We present the first taxonomy of NLP methods—such as BERT, LSTM, and TF-IDF—structured by fraud type, and identify three persistent bottlenecks: weak model generalizability, outdated and biased training data, and selective reporting of evaluation metrics. Critically, we argue that the prevailing “single-model, single-fraud” paradigm is unsustainable, advocating instead for dynamic, generalizable modeling aligned with the evolving nature of fraud. Our contribution includes the first comprehensive technology landscape map of fraud detection, integrating data sources, algorithms, and evaluation metrics. This map establishes a theoretical foundation and practical roadmap for methodological standardization and sustainable AI-driven fraud research.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Fraud is a prevalent offence that extends beyond financial loss, causing psychological and physical harm to victims. The advancements in online communication technologies alowed for online fraud to thrive in this vast network, with fraudsters increasingly using these channels for deception. With the progression of technologies like AI, there is a growing concern that fraud will scale up, using sophisticated methods, like deep-fakes in phishing campaigns, all generated by language generation models like ChatGPT. However, the application of AI in detecting and analyzing online fraud remains understudied. We conduct a Systematic Literature Review on AI and NLP techniques for online fraud detection. The review adhered the PRISMA-ScR protocol, with eligibility criteria including relevance to online fraud, use of text data, and AI methodologies. We screened 2,457 academic records, 350 met our eligibility criteria, and included 223. We report the state-of-the-art NLP techniques for analysing various online fraud categories; the training data sources; the NLP algorithms and models built; and the performance metrics employed for model evaluation. We find that current research on online fraud is divided into various scam activitiesand identify 16 different frauds that researchers focus on. This SLR enhances the academic understanding of AI-based detection methods for online fraud and offers insights for policymakers, law enforcement, and businesses on safeguarding against such activities. We conclude that focusing on specific scams lacks generalization, as multiple models are required for different fraud types. The evolving nature of scams limits the effectiveness of models trained on outdated data. We also identify issues in data limitations, training bias reporting, and selective presentation of metrics in model performance reporting, which can lead to potential biases in model evaluation.

Problem

• AI-based detection of online fraud is understudied
• Systematic review identifies 16 fraud types needing diverse models
• Current models face data bias and outdated training limitations

Innovation

• AI-based models for fraud detection
• Systematic Literature Review methodology
• NLP techniques for scam analysis