How does AI improve transaction monitoring over rules-based systems?

Detailed Explanation

Rules-based monitoring fires when a transaction crosses a threshold somebody wrote down in advance. That catches the patterns you already know about and, by construction, misses the ones you do not. Machine learning models work from behaviour instead of thresholds: the shape of a customer's payment network, the velocity of movement through it, and clusters of activity that look anomalous relative to peers rather than relative to a fixed number. Layering that on top of rules widens coverage without discarding the deterministic checks a regulator expects to see. The practical gain is prioritisation. The same alert volume gets ordered by likelihood rather than arriving flat, so analyst time lands on the cases most likely to be real.