AML (Anti-Money Laundering) solutions typically tend to be rule engine driven and involve significant manual follow-up activities. Using a Machine Learning approach, AML solutions can be enhanced to reduce false positives, as well as to better prioritize the items flagged for manual follow-up. This one-hour session will be structured as below: First, we will briefly discuss the AML domain and the typical AML detection workflow. Secondly, we will have an in-depth look into how Machine Learning algorithms can help with enhancing the AML solutions through better detection and better prioritization of detected fraud activity items. Thirdly, we will look at how this can be implemented with Azure Machine Learning to achieve qualitative as well as quantitative enhancement objectives. Finally, we will briefly look at the applicability of the Machine Learning approach to other areas within Financial Services domain like Insurance Claims Fraud Detection, etc.