An engine to simulate insurance fraud network data

• URL: http://arxiv.org/abs/2308.11659v2
• Date: Sun, 06 Oct 2024 12:56:01 GMT
• Title: An engine to simulate insurance fraud network data
• Authors: Bavo D. C. Campo, Katrien Antonio,
• Abstract summary: We develop a simulation machine that is engineered to create synthetic data with a network structure. We can specify the total number of policyholders and parties, the desired level of imbalance and the (effect size of the) features in the fraud generating model. The simulation engine enables researchers and practitioners to examine several methodological challenges as well as to test their (development strategy of) insurance fraud detection models.
• Score: 1.3812010983144802
• License:
• Abstract: Traditionally, the detection of fraudulent insurance claims relies on business rules and expert judgement which makes it a time-consuming and expensive process (\'Oskarsd\'ottir et al., 2022). Consequently, researchers have been examining ways to develop efficient and accurate analytic strategies to flag suspicious claims. Feeding learning methods with features engineered from the social network of parties involved in a claim is a particularly promising strategy (see for example Van Vlasselaer et al. (2016); Tumminello et al. (2023)). When developing a fraud detection model, however, we are confronted with several challenges. The uncommon nature of fraud, for example, creates a high class imbalance which complicates the development of well performing analytic classification models. In addition, only a small number of claims are investigated and get a label, which results in a large corpus of unlabeled data. Yet another challenge is the lack of publicly available data. This hinders not only the development of new methods, but also the validation of existing techniques. We therefore design a simulation machine that is engineered to create synthetic data with a network structure and available covariates similar to the real life insurance fraud data set analyzed in \'Oskarsd\'ottir et al. (2022). Further, the user has control over several data-generating mechanisms. We can specify the total number of policyholders and parties, the desired level of imbalance and the (effect size of the) features in the fraud generating model. As such, the simulation engine enables researchers and practitioners to examine several methodological challenges as well as to test their (development strategy of) insurance fraud detection models in a range of different settings. Moreover, large synthetic data sets can be generated to evaluate the predictive performance of (advanced) machine learning techniques.

Related papers

• Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity [80.16488817177182]
  GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. We introduce three model stealing attacks to adapt to different actual scenarios.
  arXiv  Detail & Related papers  (2023-12-18T05:42:31Z)
• Explainable Fraud Detection with Deep Symbolic Classification [4.1205832766381985]
  We present Deep Classification, an extension of the Deep Symbolic Regression framework to classification problems. Because the functions are mathematical expressions that are in closed-form and concise, the model is inherently explainable both at the level of a single classification decision and the model's decision process. An evaluation on the PaySim data set demonstrates competitive predictive performance with state-of-the-art models, while surpassing them in terms of explainability.
  arXiv  Detail & Related papers  (2023-12-01T13:50:55Z)
• Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition [49.38757847011105]
  computational fallacy recognition faces challenges due to diverse genres, domains, and types of fallacies found in datasets. We aim to enhance existing models for fallacy recognition by incorporating additional context and by leveraging large language models to generate synthetic data. Our evaluation results demonstrate consistent improvements across fallacy types, datasets, and generators.
  arXiv  Detail & Related papers  (2023-11-16T04:17:47Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation [17.314619091307343]
  We describe a privacy-preserving framework that allows financial institutions to jointly train highly accurate anomaly detection models. We show that our solution enables the network to train a highly accurate anomaly detection model while preserving privacy of customer data.
  arXiv  Detail & Related papers  (2023-10-06T19:16:41Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• 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)
• Fraud Dataset Benchmark and Applications [25.184342958800293]
  Fraud dataset Benchmark (FDB) is a compilation of publicly available datasets catered to fraud detection. FDB comprises variety of fraud related tasks, ranging from identifying fraudulent card-not-present transactions, detecting bot attacks, classifying malicious URLs, estimating risk of loan default to content moderation. Python based library for FDB provides a consistent API for data loading with standardized training and testing splits.
  arXiv  Detail & Related papers  (2022-08-30T17:35:39Z)
• Credit card fraud detection - Classifier selection strategy [0.0]
  Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. fraud data sets are diverse and exhibit inconsistent characteristics. We propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets.
  arXiv  Detail & Related papers  (2022-08-25T07:13:42Z)
• Mechanisms that Incentivize Data Sharing in Federated Learning [90.74337749137432]
  We show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. We then introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent.
  arXiv  Detail & Related papers  (2022-07-10T22:36:52Z)
• Social network analytics for supervised fraud detection in insurance [1.911867365776962]
  Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim.
  arXiv  Detail & Related papers  (2020-09-15T21:40:15Z)

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.