The complexity of matching invoice data with corresponding bank statement transactions is a task consuming a significant portion of time of financial advisors. A large volume of invoices, each with its own format, can lead to errors and delays in reconciliation processes.
A client approached me seeking a robust system to reconcile a large volume of invoices with various bank statements in pdf. He needed an automated solution that could handle multiple invoice formats, diverse payment descriptions, and occasionally missing or ambiguous information. The client's existing manual workflow was both time-consuming and prone to human error.
To address this challenge, I developed an API-based approach powered by a large language model. The system extracts text from invoices and automatically interprets key details such as amounts, due dates, and reference numbers. It then matches these details to the appropriate transactions in bank statements. By leveraging the LLM's advanced natural language processing capabilities, this approach significantly reduces manual intervention and improves the accuracy of invoice reconciliation.