Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and improve their performance on tasks without being explicitly programmed. By analysing large datasets, machine learning models can recognise patterns, make predictions, and adapt to new information over time. These models are trained on historical data and use algorithms to refine their accuracy as more data is processed. 

In the context of Anti-Money Laundering (AML) and financial crime compliance, ML can significantly enhance the detection of suspicious activities. Traditional rule-based systems may miss complex or evolving schemes, but ML can identify subtle patterns and anomalies that might indicate money laundering, fraud, or other financial crimes. This technology enables more accurate, efficient, and adaptive compliance processes, helping financial institutions stay ahead of emerging threats while reducing false positives.