The main AI techniques used for fraud management 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.
- Expert systems to encode expertise for detecting fraud in the form of rules.
- Pattern recognition to detect approximate classes, clusters, or patterns of suspicious behavior either automatically (unsupervised) or to match given inputs.
- Machine learning techniques to automatically identify characteristics of fraud.
- Neural networks that can learn suspicious patterns from samples and used later to detect them.
Other techniques such as link analysis, Bayesian networks, decision theory, and sequence matching are also used for fraud detection.