Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a new variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational au-toencoder. Each model's dynamics is ...
In addition, the influence of data preprocessing approaches, like normalization, feature scaling, and missing data imputation, on the accuracy and resilience of fraud detection models is studied. Moreover, the potential of ensemble methods, combining multiple unsupervised learning models to enhance ...
in real-time. Fraud analytics is the core of all insurance fraud detection solutions. Many vendors offer traditional rule-based fraud analytics models, whereas some prefer Artificial Intelligence and Machine Learning based techniques. Fraud analytics solutions proactively detect frauds and also help meet...
Significant research exists related to the general insurance fraud detection in the past that focuses on data mining and machine learning techniques [16]. Researchers have mostly focused on one of the stakeholders of insurance triangle , more frequently, on the frauds done by patients or by the h...
probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out-of-sample fraud ...
Although fraud detection has b... Jha,Sanjeev - Dissertations & Theses - Gradworks 被引量: 3发表: 2009年 Estimation of a selectivity model with misclassified selection Despite the great interest in models of self-selection and models with misclassification, there have been few studies combining ...
models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply themethodologyto a real database—the Italian Antifraud Integrated Archive—and compare the results to out-of-sample fraud scams under ...
Predictive AI models by FRISS and behavioral analytics by ForMotiv are expected to assist insurers to improve their ability to identify potential frauds while attaining greater accuracy and efficiency. Insurance Fraud Detection Market Report Scope Report Attribute Details Market size value in 2024 USD ...
To make this detection possible the algorithm should be fed with a constant flow of data. Usually, insurance companies use statistical models for efficient fraud detection. These models rely on the previous cases of fraudulent activity and apply sampling method to analyze them. In addition, ...
finalized a relationship agreement withTransUnion(Chicago), a global information and insights company. As a result, insurers using TransUnionTruLookupattributes and data to support their risk management initiatives can now also have that data used to informShift Claims Fraud Detectionmodels via a direct...