CGI AI-driven Anomaly Detection is a machine learning solution designed for customer onboarding processes in the asset management sector. The solution analyzes metadata across the entire onboarding lifecycle, including historical and recent data related to how, when, and where new accounts are opened. The system uses multiple AI analysis models at different points along the onboarding lifecycle to predict potential account opening failures. Unlike rules-based approaches, the solution can detect any type of error in onboarding, including errors that have never been contemplated or anticipated. It analyzes patterns in onboarding data to identify anomalies that may indicate errors or risks. The solution addresses limitations of traditional rules-based anomaly detection by adapting to changes in human behaviors, forms, and processes. It can identify patterns such as increased error rates during specific times of day or by particular personnel. The system helps organizations detect onboarding errors, manage quality control triggers, and reduce financial risks associated with incorrect account configurations or transactions. The solution is deployed in cloud environments and includes consulting services for AI implementation and cloud migration support.