Customs Fraud Detection in the Presence of Concept Drift

• URL: http://arxiv.org/abs/2109.14155v1
• Date: Wed, 29 Sep 2021 02:52:19 GMT
• Title: Customs Fraud Detection in the Presence of Concept Drift
• Authors: Tung-Duong Mai and Kien Hoang and Aitolkyn Baigutanova and Gaukhartas Alina and Sundong Kim
• Abstract summary: ADAPT is an adaptive selection method that controls the balance between exploitation and exploration strategies. We find the system with ADAPT can gradually adapt to the dataset and find the appropriate amount of exploration ratio with high performance.
• Score: 2.257416403770908
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to detect both known frauds and unknown frauds within a limited budget. The current paper proposes ADAPT, an adaptive selection method that controls the balance between exploitation and exploration strategies used for customs fraud detection. ADAPT makes use of the model performance trends and the amount of concept drift to determine the best exploration ratio at every time. Experiments on data from four countries over several years show that each country requires a different amount of exploration for maintaining its fraud detection system. We find the system with ADAPT can gradually adapt to the dataset and find the appropriate amount of exploration ratio with high performance.

Related papers

• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• GraphFC: Customs Fraud Detection with Label Scarcity [21.7060251265426]
  With limited manpower, the custom offices can only undertake manual inspection of a limited number of declarations. Current approaches for customs fraud detection are not well suited and designed for this real-world setting. In this work, we propose a model-agnostic, domain-specific, semi-supervised graph neural network based customs fraud detection algorithm.
  arXiv  Detail & Related papers  (2023-05-19T01:47:12Z)
• Weakly Supervised Anomaly Detection: A Survey [75.26180038443462]
  Anomaly detection (AD) is a crucial task in machine learning with various applications. We present the first comprehensive survey of weakly supervised anomaly detection (WSAD) methods. For each setting, we provide formal definitions, key algorithms, and potential future directions.
  arXiv  Detail & Related papers  (2023-02-09T10:27:21Z)
• On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning [0.5820960526832067]
  The UK anti-fraud charity Fraud Advisory Panel estimates business costs of fraud at 144 billion. Building an efficient fraud detection system is challenging due to many difficult problems, e.g.imbalanced data, computing costs, etc.
  arXiv  Detail & Related papers  (2022-05-10T02:13:58Z)
• Application of Deep Reinforcement Learning to Payment Fraud [0.0]
  A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. We argue that such a formulation can lead to suboptimal solutions. We formulate fraud detection as a sequential decision-making problem by including the utility within the model in the form of the reward function.
  arXiv  Detail & Related papers  (2021-12-08T11:30:53Z)
• Generalized Out-of-Distribution Detection: A Survey [83.0449593806175]
  Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. Several other problems, including anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD) are closely related to OOD detection. We first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems.
  arXiv  Detail & Related papers  (2021-10-21T17:59:41Z)
• Incentivizing Exploration in Linear Bandits under Information Gap [50.220743323750035]
  We study the problem of incentivizing exploration for myopic users in linear bandits. In order to maximize the long-term reward, the system offers compensation to incentivize the users to pull the exploratory arms.
  arXiv  Detail & Related papers  (2021-04-08T16:01:56Z)
• Active Learning for Human-in-the-Loop Customs Inspection [12.66621970520437]
  We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected. We show that a hybrid strategy of selecting likely fraudulent and uncertain items will eventually outperform the exploitation-only strategy.
  arXiv  Detail & Related papers  (2020-10-27T13:31:31Z)
• Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking [9.13755431537592]
  We propose an enhanced threshold selection policy for fraud alert systems. The proposed approach formulates the threshold selection as a sequential decision making problem and uses Deep Q-Network based reinforcement learning. Experimental results show that this adaptive approach outperforms the current static solutions by reducing the fraud losses as well as improving the operational efficiency of the alert system.
  arXiv  Detail & Related papers  (2020-10-21T15:10:57Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)
• DFraud3- Multi-Component Fraud Detection freeof Cold-start [50.779498955162644]
  The Cold-start is a significant problem referring to the failure of a detection system to recognize the authenticity of a new user. In this paper, we model a review system as a Heterogeneous InformationNetwork (HIN) which enables a unique representation to every component. HIN with graph induction helps to address the camouflage issue (fraudsterswith genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews.
  arXiv  Detail & Related papers  (2020-06-10T08:20:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.