Molecule.io Reduces Anomaly Detection Time by 80% with BUILDSTR & AWS ML

Financial services platforms require robust anomaly detection capabilities to ensure trading accuracy and maintain customer trust. Traditional methods of identifying disparities between target configurations and actual trade data often rely on manual processes or rule-based systems that can be slow, error-prone, and unable to adapt to evolving patterns. As financial markets become increasingly complex and fast-paced, organizations need more sophisticated, automated approaches to detect anomalies quickly and accurately.

Molecule.io partners with BUILDSTR, a PACE scale partner, to implement an AI/ML-driven anomaly detection solution using AWS-native tooling. The project begins with a proof of concept to evaluate the feasibility of using a custom machine learning model to automatically detect disparities between target-state product configurations and actual trade data. Within just three weeks, BUILDSTR successfully prepares data, trains an appropriate ML model, deploys the solution, and delivers comprehensive documentation along with a production implementation plan.

The technical architecture leverages a Random Cut Forest model within Amazon SageMaker, which proves ideal for this unsupervised learning use case. The solution integrates multiple AWS services including Lambda for serverless compute, API Gateway for endpoint management, Amazon Data Firehose for real-time data ingestion, Amazon S3 for storage, and SNS for notifications. This entirely AWS-native architecture provides a scalable foundation that can evolve with Molecule.io's business needs while maintaining high performance and reliability.