Large US Financial Services Enterprise Enables Real-time FX Treasury Risk Mitigation With AI-driven Anomaly Detection

A large US financial services firm needed reliable anomaly detection in FX treasury transactions spanning multiple currencies, products, time buckets, and systems. We built a three-tier framework combining rule-based checks, statistical/ML (autoencoder) scoring, and LLM reasoning for final justification. Delivered on Databricks, the solution features automated training, model registration, serving endpoints, and end-to-end logging — reducing deal errors and speeding risk remediation.

We implemented a progressive, three-layer anomaly framework combining client-defined rules, statistical/ML (autoencoder) scoring, and LLM-based contextual evaluation to ensure high-accuracy detection with minimal false positives. Built on Databricks, the system automates data onboarding, model training, MLflow versioning, and serving endpoints, with end-to-end logging for full auditability. It reliably flags high-risk FX transactions across currencies, products, time buckets, and systems, accelerating risk remediation.

The client handled massive volumes of FX treasury transactions daily, spanning multiple currencies, financial products, and time buckets. Manual reviews were time-consuming and error-prone, leading to delayed detection of incorrect deals. The client’s existing system lacked contextual insights, forcing teams to interpret large amounts of raw transaction data. It required a robust, scalable solution that could detect anomalies in real time, reduce false positives, and provide explanations that business users, not just data scientists, could understand. Additionally, onboarding new customers to the anomaly detection platform needed to be automated and efficient.

We deployed a comprehensive solution combining business rules, advanced ML models, and LLM-based contextual validation. The solution identifies anomalies at various levels, from transaction patterns and deviation from historical norms to contextual mismatches. The Databricks platform automates training, model lifecycle management, and endpoint creation for new clients. MLflow ensured structured tracking of experiments and model versions. Azure OpenAI added interpretability by explaining why a specific transaction was anomalous, enabling trust and faster risk mitigation. With scalable infrastructure, enterprises could deploy this capability across multiple client groups without additional overhead.

The solution significantly improved the accuracy and speed of detecting anomalies across FX transactions. Clients using the treasury and risk manager platform now benefit from proactive monitoring, reduced financial exposure, and fewer operational disruptions. The automation of onboarding, training, and endpoint creation accelerated adoption while minimizing costs and manual overhead. With LLM-enabled explanations, business users can make faster and more informed decisions. Overall, the platform now delivers higher integrity, trust, and efficiency in treasury operations.

This case study demonstrates how AI, ML, and LLM technologies can transform treasury operations for large financial enterprises. By combining rule-based checks, advanced modeling techniques, and contextual language intelligence, we delivered a powerful, scalable, and interpretable anomaly detection capability. The fully automated Databricks-based solution significantly reduces manual workloads and improves accuracy, enabling the client to swiftly and confidently mitigate financial risks. This approach sets a strong foundation for future enhancements across other financial datasets and risk domains.