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A semi-supervised Anti-Fraud model based on integrated XGBoost and BiGRU with self-attention network: an application to internet loan fraud detection
Recently, fraud debt has been one of the major issues for Internet financial institutions. Due to fraudulent activities, huge losses are occurring in...
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Research on Comprehensive Blockchain Regulation and Anti-fraud System
The blockchain technology has attracted attention due to its characteristics of anonymity, openness, decentralization, traceability, and...
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Overview of Digital Finance Anti-fraud
The development of digital financial technology and its penetration into the traditional financial industry have become an irreversible trend. At...
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A New Improved Method for Online Credit Anti-Fraud
AbstractWith the rapid development of Internet finance, the demand for online credit anti-fraud is more and more urgent. The current research on...
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CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation
Establishing a reliable credit card fraud detection model has become a primary focus for academia and the financial industry. The existing anti-fraud...
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Purchase Pattern Based Anti-Fraud Framework in Online E-Commerce Platform Using Graph Neural Network
Click Farming is fraudulent behaviors sponsored by malicious merchants to increase exposure by hiring fraudulent teams to place fraudulent orders,...
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Domain Ontology Construction for Intelligent Anti-Telephone-Fraud Applications
Domain ontology should reflect real data and support intelligent applications. However, existing methods for ontology construction focus on the...
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Distinguishing Good from Bad: Distributed-Collaborative-Representation-Based Data Fraud Detection in Federated Learning
Breaking down data silos and promoting data circulation and cooperation is an important topic in the digital age. As data security and privacy...
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Horizontal Association Modeling: Deep Relation Modeling
The rapid development of internet finance has caused increasing concern in online payment fraud due to its great threat. Online payment fraud...
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CT-GCN+: a high-performance cryptocurrency transaction graph convolutional model for phishing node classification
Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This...
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An abnormal surgical record recognition model with keywords combination patterns based on TextRank for medical insurance fraud detection
Increasing insurance fraud has resulted in the loss of large amounts of money, making it difficult to expand insurance coverage and scale. This...
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AP-GCL: Adversarial Perturbation on Graph Contrastive Learning
A serious ecological hazard of illegal transactions (money laundering, financial fraud, etc.) on the Bitcoin trading network. Anti-money laundering...
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AntiPhiMBS-TRN: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Transaction Level
With the continuous improvement and growth at a rapid pace in the utility of mobile banking payment technologies, fraudulent mobile banking...
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Case Study: TalkingData AdTracking Fraud Detection Challenge
This chapter centers on the typical anti click fraud competition question of a contest held on the Kaggle competition platform in 2018, i.e.,...
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Explicable Integration Techniques: Relative Temporal Position Taxonomy
In data-driven anti-fraud engineering for online payment services, the integration of proper function modules is an effective way to further improve...
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CT-GCN: a phishing identification model for blockchain cryptocurrency transactions
With the widespread application of blockchain technology, the cyberspace security issue of phishing has also appeared in the emerging blockchain...
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Knowledge Oriented Strategies: Dedicated Rule Engine
Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud...
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Fine-Tuning Pre-Trained Model for Consumer Fraud Detection from Consumer Reviews
Consumer fraud is a significant problem that requires accurate and prompt detection. However, existing approaches such as periodic government...
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Data quality model for assessing public COVID-19 big datasets
For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For...
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HAMLET: A Transformer Based Approach for Money Laundering Detection
Money laundering has damaging economic, security, and social consequences, fueling criminal activities like terrorism, human and drug trafficking....