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

arXiv: https://arxiv.org/abs/2409.19022 · HTML: https://mlsanity.com/paper/2409.19022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 318 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Yelp Dataset Challenge: Review Rating Prediction](https://mlsanity.com/paper/1605.05362/) (2016)
- [word2vec Parameter Learning Explained](https://mlsanity.com/paper/1411.2738/) (2014)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models](https://mlsanity.com/paper/2310.00737/) (2023)
- [Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing](https://mlsanity.com/paper/2310.13715/) (2023)
- [The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention](https://mlsanity.com/paper/2307.11864/) (2023)
- [Charting the Landscape of Online Cryptocurrency Manipulation](https://mlsanity.com/paper/2001.10289/) (2020)
- [From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude](https://mlsanity.com/paper/2310.19181/) (2023)
- [How Spammers and Scammers Leverage AI-Generated Images on Facebook for Audience Growth](https://mlsanity.com/paper/2403.12838/) (2024)
- [Detecting Scams Using Large Language Models](https://mlsanity.com/paper/2402.03147/) (2024)
- [AbuseGPT: Abuse of Generative AI ChatBots to Create Smishing Campaigns](https://mlsanity.com/paper/2402.09728/) (2024)
- [Identifying and Analyzing Cryptocurrency Manipulations in Social Media](https://mlsanity.com/paper/1902.03110/) (2019)
- [Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources](https://mlsanity.com/paper/2303.13521/) (2023)
- [Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements](https://mlsanity.com/paper/2111.13530/) (2021)
- [Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam](https://mlsanity.com/paper/2204.01233/) (2022)
- [Commercial Anti-Smishing Tools and Their Comparative Effectiveness Against Modern Threats](https://mlsanity.com/paper/2309.07447/) (2023)
- [A First Look at Human Values-Violation in App Reviews](https://mlsanity.com/paper/2012.10095/) (2020)
- [On the Violation of Honesty in Mobile Apps: Automated Detection and Categories](https://mlsanity.com/paper/2203.07547/) (2022)
- [Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews](https://mlsanity.com/paper/2004.05865/) (2020)
- [Early Detection of Spam Domains with Passive DNS and SPF](https://mlsanity.com/paper/2205.01932/) (2022)
- [Canary in Twitter Mine: Collecting Phishing Reports from Experts and Non-experts](https://mlsanity.com/paper/2303.15847/) (2023)
- [Automatic Scam-Baiting Using ChatGPT](https://mlsanity.com/paper/2309.01586/) (2023)
- [Automatically Dismantling Online Dating Fraud](https://mlsanity.com/paper/1905.12593/) (2019)
- [Understanding phishers' strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approach](https://mlsanity.com/paper/2007.00489/) (2020)
- [Fine-Grained Element Identification in Complaint Text of Internet Fraud](https://mlsanity.com/paper/2108.08676/) (2021)
- [Active Countermeasures for Email Fraud](https://mlsanity.com/paper/2210.15043/) (2022)

## Built on by (most cited first, 1 of 1)

- [Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows](https://mlsanity.com/paper/2607.13078/) (2026)

## Abstract (arXiv)

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.