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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 ...

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 ...

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 ...

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 ...

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 ...

• 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 ...

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 ...

During recent decades, using credit cards represents a pivotal part of the financial lifeline. Credit cards and online payment gateways are vital elements in the world of world-wide-web. Given the fact that credit cards play an essential role in ...

Premium maize varieties are the focus of attention of farmers, breeders, food manufacturers, and people in other industries. Maize seed fraud causes huge financial losses to these industries and many varieties are difficult to distinguish due to ...

• Hyperspectral imaging combined with one-class learning for seed fraud detection.
• A one-class classifier (OCC) base on spatial and spectral information is proposed.
• The OCC trains a hypersphere to receive the real variety and reject ...

Traditional methods of fraud detection rely on rule-based systems or supervised machine learning models that require labelled data and domain knowledge. However, these methods have limitations such as high false positive rates, low scalability, ...

Discovering fraud patterns from numerous user activities is crucial for fraud detection. However, three factors make this task quite challenging: Firstly, previous research usually utilize just one of the two forms of user activity, namely ...

Anomaly detection is a process that detects unlike observations from entire data points. Also, detection of anomalies in unlabelled data particularly is very tedious task using unsupervised learning models. It is a very big problem in banking, ...

Since the reform and opening up, my country's economic growth has been strong, and it has achieved brilliant achievements in the past few decades. With the digital transformation of the economy, credit card fraud has become a more prominent problem. Due ...

Detecting fraudulent activities is a major worry for businesses and financial organizations because they can result in significant financial losses and reputational harm. Traditional fraud detection a method frequently depend on present rules and ...

Phishing attacks are a critical and escalating cybersecurity threat in the modern digital landscape. As cybercriminals continually adapt their techniques, automated phishing detection systems have become essential for safeguarding Internet users. ...

Fraud detection and prevention has received a lot of attention from the research community due to its high impact on financial institutions’ revenues and reputation. The increased use of the web and the provision of online services open up the ...

It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in an ongoing business are inspected to support the providers’ decision on whether to lend the money. ...

• Financial fraud is identified based on multiple views in supply chain finance.
• A multitask learning framework with heterogeneous GNN is proposed to identify frauds.
• Comprehensive explanations are provided on multiple heterogeneous ...

Healthcare fraud detection algorithms are mostly based on applying machine learning methods to payer-claims data transactions to detect fraudulent activities. However, claim transactions are often analyzed in isolation, disregarding the ...

Buy-now-pay-later (BNPL) has been increasingly adopted by young shoppers as well as older generations because applying for a BNPL service is hassle-free compared to credit card applications. However, preventing fraudulent transactions is indispensable. ...