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• Addressing imbalanced data in ML poses challenges due to class disproportion.
• In some imbalanced datasets, false negatives impact more than false positives.
• This work introduces the MinFNR algorithm to minimize False Negative Rates ...

Imbalanced datasets present a challenge in machine learning, especially in binary classification scenarios where one class significantly outweighs the other. This imbalance often leads to models favoring the majority class, resulting in ...

Fraud detection is a challenging task that can be difficult to carry out. To address these challenges, a comprehensive framework has been developed which includes a new resampling method combined with a data-dependent classifier that can detect ...

• A resampling method to preserve the distribution of fraud instances.
• Combining OCSVM with SMOTE and random undersampling to detect outliers.
• Light Gradient-Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM).
• ...

Fraudulent activities on e-commerce platforms, such as spamming product reviews or fake payment behaviors, seriously mislead users’ purchasing decisions and harm platform integrity. To effectively identify fraudsters, recent research mainly ...

• A novel multiplex graph fusion network with reinforcement structure learning.
• We well capture the structural relationship and semantic feature.
• RestMGFN model can eliminate the perturbation of camouflage fraud.
• We achieve high ...

Identifying fraud in healthcare programs is crucial, as an estimated 3%–10% of the total healthcare expenditures are lost to fraudulent activities. This study presents a systematic literature review of machine learning techniques ...

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• This survey paper provides a systematic review of healthcare fraud detection.
• Healthcare fraud detection is challenging due to scarce fraudulent cases.
• Various machine learning approaches have been used for healthcare fraud ...

In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labeled nodes, but also ...

Financial fraud is a vital issue which causes considerable economic losses for financial institutions. Existing studies apply various methods to detect fraudsters from financial data. In particular, methods based on Graph Neural Networks(GNNs) ...

• Utilize temporal frequency and out-degree to detect fraudsters.
• Fight camouflages in graph by random dropping on propagated messages.
• Propose a novel graph neural network model for accurate fraud detection.

Instance-dependent cost-sensitive learning addresses classification problems where each observation has a different misclassification cost. In this paper, we propose cost-sensitive parametric models to minimize the expectation of losses. A loss ...

Cases of financial fraud are increasing despite recent technical breakthroughs. It is tough to find authentic financial transaction data due to privacy issues and a lack of inter-organization synergy. However, for technologies based on data, such ...

The proliferation of fraud in online shopping has accompanied the development of e-commerce, leading to substantial economic losses, and affecting consumer trust in online shopping. However, few studies have focused on fraud detection in e-...

This paper presents a method for identifying fraudulent fund transfers using real bank data, analyzing customer information, transactional activities, and customer relationships. The preprocessing step transforms high-dimensional, irregular ...

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• A fraud detection algorithm handling irregular transactions and customer relations.
• Fusions of variational self-attention-based autoencoders and graph neural networks.
• Neighborhood sampling with mini-batch training for efficient ...

A Ponzi scheme is an investment fraud in which existing investors are paid with funds collected from new investors, which causes significant financial losses. This fraudulent activity also exists in blockchain-enabled platforms, but it can be ...

It might be instinctively assumed that the occurrence of the first digit of a randomly selected number is uniformly distributed among 1 to 9. However, the Newcomb–Benford law (NBL), also known as the first-digit law or Benford’s law, reveals a ...

We propose a mixed integer programming (MIP) procedure to find an outer belief approximation of a lower conditional joint cumulative distribution function (lower conditional joint CDF) obtained by the statistical matching of several sources of ...

Artificial Intelligence (AI) and Machine Learning (ML) are being used more and more to handle complex tasks in many different areas. As a result, interconnected information systems are growing, which means that autonomous systems are needed to ...

Anti-money laundering (AML) efforts are critical not just for financial stability but also for global security, as money laundering supports various criminal activities like terrorism, human trafficking, and drug trade. Fraud detection, as part of ...

• AI-based approach exposes the activities of fraudsters in auto insurance.
• Graph features increase the quality of classifying fraudulent claims.
• The claims graph is built on a dataset without participant identifiers.
• The ...

The article proposes a process for claims assessment in car insurance, which makes it possible to calculate the fraud rate on the annual set of claims using a reduced set of attributes and graph vertex properties. This approach improves the ...

Short Message Service (SMS) spamming is a harmful phishing attack on mobile phones. That is, fraudsters are trying to misuse personal user information, using tricky text messages, sometimes included with a fake URL that asks for this personal ...

Graph Neural Networks are widely employed for node classification in attributed networks. When it comes to fraud detection, however, GNNs can perform poorly, because a node’s features are typically computed based on its local neighborhood, and ...

The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the ...

• Fraud detection models must be updated continually to handle new fraud strategies.
• They must balance plasticity (learn new patterns) and stability (remember old ones).
• We show how to quantify both and discuss the trade-off for ...

As data grow exponentially, the demand for advanced intelligent solutions has become increasingly urgent. Unfortunately, not all businesses have the expertise to utilize machine learning algorithms effectively. To bridge this gap, the present ...