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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. Expert systems to encode expertise for detecting ...
Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. [1]
Credit card fraud. A fake automated teller slot used for "skimming". Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. [ 1] The purpose may be to obtain goods or services or to make payment to another account, which is controlled by a criminal.
Luhn algorithm. The Luhn algorithm or Luhn formula, also known as the " modulus 10" or "mod 10" algorithm, named after its creator, IBM scientist Hans Peter Luhn, is a simple check digit formula used to validate a variety of identification numbers. It is described in US patent 2950048A, granted on 23 August 1960. [ 1]
In the realm of data science, particularly in finance, cost-sensitive machine learning is applied to fraud detection. By assigning different costs to false positives and false negatives, models can be fine-tuned to minimize the overall financial impact of misclassifications.
Link analysis. In network theory, link analysis is a data-analysis technique used to evaluate relationships (Tap link [clarification needed]) between nodes. Relationships may be identified among various types of nodes (100k [clarification needed] ), including organizations, people and transactions. Link analysis has been used for investigation ...
v. t. e. In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. [ 1] It is used in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. [ 2]
Scientific misconduct is the violation of the standard codes of scholarly conduct and ethical behavior in the publication of professional scientific research. It is violation of scientific integrity: violation of the scientific method and of research ethics in science, including in the design, conduct, and reporting of research.