The process of identifying and preventing fraudulent activities in industries such as finance, healthcare, insurance, and e-commerce is known as Fraud Detection. The primary goal of fraud detection is to detect and prevent fraud before it occurs or as early as possible to minimize the damage caused.
Fraud detection techniques involve the use of algorithms and analytics to detect patterns and anomalies in data. The techniques used can range from simple rule-based approaches to complex Machine Learning models that can detect and predict fraudulent behavior.
Anomaly detection is one of the most commonly used approaches in fraud detection. It involves the identification of deviations from normal behavior or pattern in data. Another approach to fraud detection is predictive analytics. This involves using historical data to build models that can predict the probability of fraudulent behavior.
Fraud detection is essential for protecting businesses and individuals from financial and reputational damages and other losses caused by fraudulent activities.