Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence (Palshikar 2002).
Examples of statistical data analysis techniques are:
•Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data.
•Calculation of various statistical parameters such as averages, quantiles, performance metrics, probability distributions, and so on. For example, the averages may include average length of call, average number of calls per month and average delays in bill payment.
•Models and probability distributions of various business activities either in terms of various parameters or probability distributions.
•Computing user profiles.
•Time-series analysis of time-dependent data.
•Clustering and classification to find patterns and associations among groups of data.
•Matching algorithms to detect anomalies in the behavior of transactions or users as compared to previously known models and profiles. Techniques are also needed to eliminate false alarms, estimate risks, and predict future of current transactions or users.
The main Artificial Intelligence 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, land sequence matching are also used for fraud detection (Palshikar 2002).
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