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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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GrooveSquid.com Paper Summaries

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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