Using machine learning to fight fraud is becoming increasingly important as digital technology advances and fraudulent transactions rise. Machine learning models are effective at detecting and preventing fraud but require high-quality data, complex feature engineering, and continuous maintenance. The benefits of using real-time ML for risk assessment and fraud detection include the ability to evaluate risk quickly and detect new fraudulent behavior as it emerges. However, there are also challenges such as managing vast amounts of data from various sources, ensuring data security and regulatory compliance, and avoiding vendor lock-in. Companies may consider buying a pre-packaged solution or building an in-house solution, but both options come with limitations and risks. Implementing a feature platform can simplify complex feature engineering and help teams efficiently manage real-time data, making it a viable option for companies looking to improve their fraud detection capabilities.