A Comprehensive Survey of Data Mining-based Fraud Detection Research
arXiv:1009.6119 · doi:10.1016/j.chb.2012.01.002
Abstract
This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.
14 pages
Cited by in corpus (39)
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- Learning and Evaluating Representations for Deep One-class Classification
- Graph-based Anomaly Detection and Description: A Survey
- Variational quantum one-class classifier
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- Data, text and web mining for business intelligence: a survey
- Unveiling the frontiers of deep learning: innovations shaping diverse domains
- TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
- Contextual Outlier Interpretation
- Adversarial Learning of Deepfakes in Accounting
- Anomaly detection in online social networks
- Quantum support vector data description for anomaly detection
- Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach
- CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions
- Sequence embeddings help to identify fraudulent cases in healthcare insurance
- Message Importance Measure and Its Application to Minority Subset Detection in Big Data
- ESAD: End-to-end Deep Semi-supervised Anomaly Detection
- OneFlow: One-class flow for anomaly detection based on a minimal volume region
- An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
- Implementation of Correlation and Regression Models for Health Insurance Fraud in Covid-19 Environment using Actuarial and Data Science Techniques
- Evaluating Classifiers in Detecting 419 Scams in Bilingual Cybercriminal Communities
- Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets
- Quick survey of graph-based fraud detection methods
- Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook
- Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques
- Occupational Fraud Detection Through Visualization
- An Efficient System for Subgraph Discovery
- Uncovering Longitudinal Healthcare Utilization from Patient-Level Medical Claims Data
- TAPESTRY: A Blockchain based Service for Trusted Interaction Online
- Towards WaterLab: A Test Facility for New Cyber-Physical Technologies in Water Distribution Networks
- Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data
- Some Experimental Issues in Financial Fraud Detection: An Investigation
- State Variation Mining: On Information Divergence with Message Importance in Big Data
- A Switch to the Concern of User: Importance Coefficient in Utility Distribution and Message Importance Measure
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- Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data