Expense Reconciliation for a large organization can be a daunting task, especially when it involves reviewing and reconciling thousands of paystubs and labor expenses. This was the challenge that a large hospital group faced until they turned to an innovative solution that combined robotic process automation (RPA) and artificial intelligence (AI).
The original process of expense reconciliation involved manually reviewing PDF documents and comparing the contents to labor records. The process was slow, tedious, and prone to errors, which made meeting deadlines challenging. After partnering with WCG’s team of AI and business process experts, the hospital was able to streamline the process and reduce the reconciliation timeline by months.
Using the Microsoft Power Platform, the team trained an AI model to read the PDF files at a rate of 98%+ accuracy. They then built a Power Automate workflow to feed the files to the AI model and structure the output data into a single view. Once the AI model had processed all the files and the output data was assessed for errors, it was sent to the review team, who were able to reconcile the paystub data against labor expenses using a combination of standardized formulas and queries.
A process that used to involve a large team effort over multiple months a was transformed into a process that was completed in a few short weeks. Utilizing these tools reduced the number of individuals needed to manage the reconciliation and the client was able to quickly process $500 million in expenses.
By leveraging the power of RPA and AI, the hospital group was able to improve the speed and accuracy of their payroll review process. The AI model not only processed paystub data quickly and accurately but also reduced the potential for human error, which ultimately saved the organization time and resources. The successful implementation of the RPA and AI solution shows the potential for these technologies to streamline complex business processes, enabling organizations to focus their time and resources on higher-value tasks.
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