Summary of A Customer Level Fraudulent Activity Detection Benchmark For Enhancing Machine Learning Model Research and Evaluation, by Phoebe Jing et al.
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
by Phoebe Jing, Yijing Gao, Xianlong Zeng
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| 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 paper introduces a new benchmark for customer-level fraud detection in machine learning, addressing the scarcity of comprehensive and privacy-compliant datasets. The benchmark provides structured datasets that encapsulate customer-centric features while adhering to strict privacy guidelines. This allows for the evaluation of various machine learning models, enabling researchers to understand their strengths and weaknesses in predicting fraudulent activities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps to bridge a gap in data availability by providing a valuable resource for researchers and practitioners. It offers a new approach to fraud detection that can operate effectively at the customer level. The benchmark is designed to facilitate the development of next-generation fraud detection techniques, which can be used to detect sophisticated fraud schemes. |
Keywords
» Artificial intelligence » Machine learning
