A forward-thinking back-office accounting services company delivering Finance-as-a-Service (FaaS) through a unique blend of people and technology. Their platform provides reliable, scalable finance operations for growing businesses.
Publishing monthly financial statements required a time-intensive and costly review process by senior accountants. While critical to maintaining service quality, this manual process impacted gross margins, SLA performance, and customer satisfaction.
thinkbridge developed and implemented a machine learning-based anomaly detection system embedded directly into the client’s accounting workflows.
This intelligent engine automatically flags anomalies in draft financial statements, enabling senior reviewers to focus only on exceptions. The system includes a built-in feedback loop, learning from every review and continuously improving its accuracy and contextual understanding of each client’s financial patterns.
Importantly, no new tools or processes were required—accountants and reviewers continued working within their existing systems, ensuring a smooth rollout and rapid adoption.
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