Rule-based filters can detect well-known fraud patterns of payment transactions very effectively. However, for new and dynamic fraud patterns, rules are not that effective. As fraudsters decode the declines and find new ways to perpetrate fraud, the rules become stale. Here we find that AI/ML-based probabilistic fraud filters are more effective as it learns from the labelled data and creates models to detect fraud probabilistically.