Summary of Advanced Financial Fraud Detection Using Gnn-cl Model, by Yu Cheng et al.
Advanced Financial Fraud Detection Using GNN-CL Model
by Yu Cheng, Junjie Guo, Shiqing Long, You Wu, Mengfang Sun, Rong Zhang
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST)
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 The proposed GNN-CL model combines graph neural networks (GNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks to improve financial fraud detection. This synergistic approach enables multifaceted analysis of complex transaction patterns, enhancing detection accuracy and resilience against fraudulent activities. The model uses multilayer perceptrons (MLPS) to estimate node similarity, filtering out neighborhood noise that can lead to false positives. Reinforcement learning strategies are employed to address feature weakening, dynamically adjusting weights assigned to central nodes to retain important clues of fraud in less informative data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The GNN-CL model is a new way to detect financial fraud by combining different types of artificial intelligence (AI) together. This helps analyze transaction patterns and find fraudulent activities more effectively. The model also filters out noise that can cause false alarms, making it better at finding real fraud. It’s like using multiple detectives to solve a mystery! |
Keywords
* Artificial intelligence * Gnn * Lstm * Reinforcement learning
