Feature Engineering for Fraud Detection Models

Feature Engineering for Fraud Detection Models

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As cybercriminals are constantly updating their strategies to avoid being detected, traditional fraud detection tools, such as expert rules, are less effective as they do not incorporate recent fraud patterns as fast as the fraudsters are changing their behavior. To incorporate the fraudulent behavior fast, it is important to use advanced machine learning algorithms, such as deep neural networks, support vector machines or random forests. Notwithstanding, when constructing a fraud detection model there are several factors that impact the performance of the algorithm. In particular, the fact that there are a few number of frauds detected, the different financial costs associated with fraud losses and the fraud detection process, the short time response of the system, and how to preprocess and create features that accurately represent a customer behavior. [Read More]