Summary of Application Of Ai-based Models For Online Fraud Detection and Analysis, by Antonis Papasavva et al.
Application of AI-based Models for Online Fraud Detection and Analysis
by Antonis Papasavva, Shane Johnson, Ed Lowther, Samantha Lundrigan, Enrico Mariconti, Anna Markovska, Nilufer Tuptuk
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators will appreciate this research paper abstract, which delves into the application of AI and NLP techniques for online fraud detection. The authors conduct a systematic literature review on 223 papers that focus on various online fraud categories, including phishing campaigns, deep-fakes, and language generation models like ChatGPT. The study finds that current research is divided into specific scam activities and identifies 16 different frauds that researchers focus on. The authors also highlight the limitations of existing models, including data limitations, training bias reporting, and selective presentation of metrics in model performance reporting. This paper offers valuable insights for policymakers, law enforcement, and businesses seeking to safeguard against online fraud. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fraud is a big problem that hurts people’s feelings and bodies too. It used to be mainly about money, but now it’s also happening online. With new technologies like AI, there’s a worry that fraud will get even worse. But some smart people are working on ways to detect and stop online fraud using computer science techniques. This paper looks at what’s being done so far to solve this problem. It found that most research is focused on specific types of scams, but that different models might be needed for each type. The authors also said that the way researchers report their results can sometimes hide problems with how they did their studies. Overall, this paper helps us understand more about online fraud and what we need to do to stop it. |
Keywords
* Artificial intelligence * Machine learning * Nlp
