Anomaly detection is the process of identifying unusual patterns or observations that do not conform to the expected behavior of a system. It is commonly used in a variety of fields, including cybersecurity, manufacturing, and finance. Some typical use cases for anomaly detection include identifying fraudulent credit card transactions, detecting equipment failures in a manufacturing process, and detecting network intrusions in a computer system. In these cases, anomaly detection algorithms are trained on historical data to learn the normal behavior of the system. They can then be applied to new data to identify instances where the system is behaving in an unexpected or abnormal way, which may indicate a problem or threat.
Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on the fight against international credit … Read more
