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A novel method based on entity relationship for online transaction fraud detection

Published: 17 May 2019 Publication History

Abstract

Fraud detection is the focus of research in Internet financial domain. The methods of both expert rules and machine learning detect fraud by seeking commonness among groups and individual differences between normal and abnormal transactions. However, they don't consider the relationship among attributes of transaction entities that may affect the precision of fraud detection. Different from the grid data, each node in relational network has different numbers of neighbors nodes, and the arrangement among them is disordered. Thus, this paper adds the relationship of transaction entities to machine learning model, which can effectively connect the graph domain and attributes space. First, the relational network between the transaction entity and the attribute entity is extracted from the transaction record. It is essentially a heterogeneous non-connected sparse bipartite graph with characteristics on nodes. However, the heterogeneous information network can not be uniformly characterized, and we should find and utilize the hidden relationship among the transaction entities we focused. Thus, Node Shrinkage Homogenization Algorithm is proposed for homogenization. Based on this homogeneous network, we put forward the method of graph-based Neighborhood Information Aggregation Gradient Boosting Decision Tree (NIAGBDT), so that the transaction features are integrated from its neighbor through the relational network. The experiments show that compared with current state of the art approaches, our algorithm has a significant improvement more than ten percent.

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Cited By

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  • (2024)Variable Selection of Kolmogorov-Smirnov Maximization with a Penalized Surrogate LossComputational Statistics & Data Analysis10.1016/j.csda.2024.107944(107944)Online publication date: Mar-2024
  • (2021)A comprehensive Study on Credit Card Fraud Prevention and Detection2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)10.1109/ICDS53782.2021.9626749(1-8)Online publication date: 20-Oct-2021
  • (2020)A Fraud Detection Method for Low-Frequency TransactionIEEE Access10.1109/ACCESS.2020.29706148(25210-25220)Online publication date: 2020

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cover image ACM Other conferences
ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China
May 2019
963 pages
ISBN:9781450371582
DOI:10.1145/3321408
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 May 2019

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Author Tags

  1. fraud detection
  2. graph representation learning
  3. neighborhood information aggregation gradient boosting decision tree
  4. node shrinkage homogenization
  5. relational network

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View all
  • (2024)Variable Selection of Kolmogorov-Smirnov Maximization with a Penalized Surrogate LossComputational Statistics & Data Analysis10.1016/j.csda.2024.107944(107944)Online publication date: Mar-2024
  • (2021)A comprehensive Study on Credit Card Fraud Prevention and Detection2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS)10.1109/ICDS53782.2021.9626749(1-8)Online publication date: 20-Oct-2021
  • (2020)A Fraud Detection Method for Low-Frequency TransactionIEEE Access10.1109/ACCESS.2020.29706148(25210-25220)Online publication date: 2020

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