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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.
Under traditional methods, these processes would be carried out by a human being. Proponents of artificial intelligence in fraud detection have stated that these traditional methods are inefficient and can be more quickly accomplished with the aid of an intelligent computing system. [12]
Forensic data analysis (FDA) is a branch of digital forensics.It examines structured data with regard to incidents of financial crime.The aim is to discover and analyse patterns of fraudulent activities.
Miskell indicated the Treasury is exploring how to adopt the fraud-detection methods that leading banks and credit card companies are deploying, declining to go into detail to avoid “tipping off ...
Forensic accountants utilize an understanding of economic theories, business information, financial reporting systems, accounting and auditing standards and procedures, data management & electronic discovery, data analysis techniques for fraud detection, evidence gathering and investigative techniques, and litigation processes and procedures to ...
The methods must manage real-time data, diverse device types, and scale effectively. Garbe et al. [19] have introduced a multi-stage anomaly detection framework that improves upon traditional methods by incorporating spatial clustering, density-based clustering, and locality-sensitive hashing. This tailored approach is designed to better handle ...