Banks need to check their customers for money laundering via their transactions and information from other sources (e.g., front desk). Ex-post monitoring of customers is largely done using rule-based approaches, which have a large false positive rate, and are inflexible to detect more advanced and emerging money laundering schemes. Artificial Intelligence (AI) techniques can be used to augment current compliance processes to increase effectiveness and efficiency of anti-money laundering (AML) activities. We will showcase the potential of AI in AML by presenting a use case, where we utilized unsupervised Machine Learning algorithms to perform KYC consistency checks. We will further highlight, which steps are necessary to cover the full Machine Learning life cycle, from the initial ideation phase, over the Proof of Concept stage, to deploying a model to production, and discuss the technology stack (e.g., Python, PySpark) used for this project