How does machine learning detect fraud?

How does machine learning detect fraud in financial transactions?

Machine learning detects fraud by analyzing patterns in transaction data to identify anomalies that may indicate fraudulent activity.

What types of algorithms are commonly used for fraud detection?

Common algorithms include decision trees, neural networks, and support vector machines, each suited for different types of data.

Can machine learning adapt to new types of fraud?

Yes, machine learning models can be trained continuously with new data, allowing them to adapt to emerging fraud patterns.

What are some challenges in using machine learning for fraud detection?

Challenges include data quality, the need for large datasets, and the risk of false positives, which can affect legitimate transactions.