With live data streams (transactions, user behavior, activity logs, verification data, etc.) Rapid FI can analyze anomalies as they occur, using AI/ML models to immediately identify irregular or fraudulent activities. Deviations from normal patterns trigger real-time alerts or decisions.

The platform supports the deployment of various AI/ML models including Time-Series Models, Anomaly Detection Models, Classification Models, and Neural Network Models, which are trained to identify fraudulent patterns.

Batch analytics on historical data allow for the identification of evolving fraud patterns and trends, which then inform the continual improvement and retraining of the AI/ML models for enhanced accuracy. This adaptability allows Rapid FI to learn and detect evolving fraud tactics.

Rapid FI can integrate broader functioning Foundation Models (LLMs, Vision models, Multimodal models, Audio models, etc.), leveraging their ability to distill unstructured data (e.g. text, image based-documents, PDFs, audio, etc.) into datapoints that can be analyzed and finally used to detect fraud indicators.

Rapid FI can process large volumes of data with scalable data storage, data ingestion pipelines, distributed data processing, and robust ML Operations (MLOps) processes. This allows for in-depth investigations and reporting that power data-driven decisions at scale.