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Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
It is widely used in the financial sector, especially by accounting firms, to help detect fraud. In 2022, PricewaterhouseCoopers reported that fraud has impacted 46% of all businesses in the world. [1] The shift from working in person to working from home has brought increased access to data.
Fuzzing Project, includes tutorials, a list of security-critical open-source projects, and other resources. University of Wisconsin Fuzz Testing (the original fuzz project) Source of papers and fuzz software. Designing Inputs That Make Software Fail, conference video including fuzzy testing; Building 'Protocol Aware' Fuzzing Frameworks
Fraud detection deals with the identification of bank fraud, such as money laundering, credit card fraud and telecommunication fraud, which have vast domains of research and applications of machine learning. Because ensemble learning improves the robustness of the normal behavior modelling, it has been proposed as an efficient technique to ...
Moreover, numerous graph-related applications are found to be closely related to the heterophily problem, e.g. graph fraud/anomaly detection, graph adversarial attacks and robustness, privacy, federated learning and point cloud segmentation, graph clustering, recommender systems, generative models, link prediction, graph classification and ...
In the 1990s AI was applied to fraud detection. In 1993 FinCEN Artificial Intelligence System (FAIS) launched. It was able to review over 200,000 transactions per week and over two years it helped identify 400 potential cases of money laundering equal to $1 billion. [80] These expert systems were later replaced by machine learning systems. [81]
Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).
Fraud can violate civil law or criminal law, or it may cause no loss of money, property, or legal right but still be an element of another civil or criminal wrong. [1] The purpose of fraud may be monetary gain or other benefits, for example by obtaining a passport, travel document, or driver's license, or mortgage fraud , where the perpetrator ...