Machine learning algorithms can analyze large amounts of data and detect patterns that may not be evident with traditional rule-based systems. By analyzing transactional data and customer behavior, the analyses provided by these algorithms can identify potentially fraudulent activities in real time. The idea behind the use of machine learning is that fraudulent transactions show certain patterns that differentiate them from authentic ones. Machine learning algorithms recognize these patterns and can differentiate between fraudsters and legitimate customers.
These algorithms can detect fraudulent activity much faster and more accurately than traditional rule-based systems, since they can use larger data sets. There is no universal and reliable machine learning algorithm for fraud detection. Instead, in real-world data science use cases, several techniques or their combinations are typically tested, the predictive accuracy of the model is calculated, and the optimal approach is selected.
