FRAUDability: Estimating Users' Susceptibility to Financial Fraud Using Adversarial Machine Learning

• URL: http://arxiv.org/abs/2312.01200v1
• Date: Sat, 2 Dec 2023 18:33:05 GMT
• Title: FRAUDability: Estimating Users' Susceptibility to Financial Fraud Using Adversarial Machine Learning
• Authors: Chen Doytshman, Satoru Momiyama, Inderjeet Singh, Yuval Elovici, Asaf Shabtai,
• Abstract summary: We propose FRAUDability, a method for the estimation of a financial fraud detection system's performance for every user. The proposed method produces scores, namely "fraudability scores," which are numerical estimations of a fraud detection system's ability to detect financial fraud for a specific user. We show that the scores can also help attackers increase their financial profit by 54%, by engaging solely with users with high fraudability scores.
• Score: 24.817067681746117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, financial fraud detection systems have become very efficient at detecting fraud, which is a major threat faced by e-commerce platforms. Such systems often include machine learning-based algorithms aimed at detecting and reporting fraudulent activity. In this paper, we examine the application of adversarial learning based ranking techniques in the fraud detection domain and propose FRAUDability, a method for the estimation of a financial fraud detection system's performance for every user. We are motivated by the assumption that "not all users are created equal" -- while some users are well protected by fraud detection algorithms, others tend to pose a challenge to such systems. The proposed method produces scores, namely "fraudability scores," which are numerical estimations of a fraud detection system's ability to detect financial fraud for a specific user, given his/her unique activity in the financial system. Our fraudability scores enable those tasked with defending users in a financial platform to focus their attention and resources on users with high fraudability scores to better protect them. We validate our method using a real e-commerce platform's dataset and demonstrate the application of fraudability scores from the attacker's perspective, on the platform, and more specifically, on the fraud detection systems used by the e-commerce enterprise. We show that the scores can also help attackers increase their financial profit by 54%, by engaging solely with users with high fraudability scores, avoiding those users whose spending habits enable more accurate fraud detection.

Related papers

• Online Corrupted User Detection and Regret Minimization [49.536254494829436]
  In real-world online web systems, multiple users usually arrive sequentially into the system. We present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors. We devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations.
  arXiv  Detail & Related papers  (2023-10-07T10:20:26Z)
• 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)
• A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection [0.0]
  This research focuses on machine learning scalability for banks' credit card fraud detection systems. We have compared the existing machine learning algorithms and methods that are available with the newly proposed technique. The goal is to prove that using fewer bits for training a machine learning algorithm will result in a more scalable system, that will reduce the time and will also be less costly to implement.
  arXiv  Detail & Related papers  (2022-06-24T01:22:17Z)
• 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)
• Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection [106.33204064461802]
  We review the effectiveness of a feature-level fusion of super-app customer information, mobile phone line data, and traditional credit risk variables for the early detection of identity theft credit card fraud. We evaluate our approach over approximately 90,000 users from a credit lender's digital platform database.
  arXiv  Detail & Related papers  (2021-11-05T19:10:35Z)
• Detecting and Quantifying Malicious Activity with Simulation-based Inference [61.9008166652035]
  We show experiments in malicious user identification using a model of regular and malicious users interacting with a recommendation algorithm. We provide a novel simulation-based measure for quantifying the effects of a user or group of users on its dynamics.
  arXiv  Detail & Related papers  (2021-10-06T03:39:24Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• Deep Learning Methods for Credit Card Fraud Detection [3.069837038535869]
  This paper presents a study of deep learning methods for the credit card fraud detection problem. It compares their performance with various machine learning algorithms on three different financial datasets. Experimental results show great performance of the proposed deep learning methods against traditional machine learning models.
  arXiv  Detail & Related papers  (2020-12-07T14:48:58Z)
• 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)
• 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)
• A Semi-supervised Graph Attentive Network for Financial Fraud Detection [30.645390612737266]
  We propose a semi-supervised attentive graph neural network, namedSemiSemiGNN, to utilize the multi-view labeled and unlabeled data for fraud detection. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks.
  arXiv  Detail & Related papers  (2020-02-28T10:35:25Z)

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.