Fraud and anti-money laundering (AML) are critical issues for businesses, particularly in sectors like financial services and e-commerce, and traditional methods like rule-based systems and predictive AI have limitations such as lack of context and costly feature engineering. The evolution of risk management technology has progressed from manual, intuition-based systems (Risk 1.0) to predictive modeling with machine learning (Risk 2.0), and now to the latest advancement driven by vector search (Risk 3.0), which utilizes real-time data feeds and continuous monitoring to detect emerging threats. MongoDB's Atlas Vector Search enhances fraud detection by addressing the limitations of previous methods, enabling organizations to leverage real-time analytics to uncover hidden insights before fraud occurs, while also supporting a holistic platform approach to manage diverse and evolving data sets. Additionally, MongoDB's Community Champions program acknowledges contributions from passionate advocates who advance MongoDB's growth and knowledge, while a leadership transition sees Chirantan “CJ” Desai set to take over as CEO, with a focus on guiding MongoDB through its next growth phase, emphasizing AI and data-intensive applications.