Achieving 90% Automation in Cash Reconciliation for a Large Media Company

The Company

The client is a large integrated media company (“Media Company”) that dominates the U.S. market in TV and radio broadcasting. Additionally, the company manages a diverse portfolio of streaming and digital platforms, alongside services in publishing and direct marketing. A significant part of their revenue comes from local and national advertisements, with thousands of advertisers as customers.

The Challenge

The Media Company faced significant operational challenges due to the sheer volume of transactions processed daily:

·        12,000 Payments Per Month: Payments were received via checks, ACH, credit cards, and wire transfers.

·        25,000 Open Invoices Daily: With so many open invoices at any given time, reconciling payments was a complex task.

·        2,000 Unique Bill-to Addresses: Managing and matching payments to the correct invoice was complicated by the large number of unique bill-to addresses.

 The Solution

To overcome these challenges, AutoCash.ai developed and implemented a tailored software solution designed to automate the reconciliation process:

·        Remittance Data Integration: The software was integrated with the Media Company’s lockbox provider to automatically retrieve remittance information. However, the remittance data often lacked complete payment details, such as order numbers and invoice numbers, complicating the reconciliation process.

·        AI and ML for Intelligent Matching: AutoCash.ai applied artificial intelligence and machine learning algorithms to intelligently match payments with the correct open invoices, even when payment details were incomplete. This advanced matching capability significantly improved the accuracy of the reconciliation process.

·        Automated Posting to A/R Sub-Ledger: Once payments were successfully matched to invoices, the software converted the data into a format that could be electronically posted to the A/R sub-ledger. This automation streamlined the process, reducing manual intervention and errors.

·        High Automation Rate: The implementation of this solution enabled the Media Company to achieve nearly 90% automation in its cash reconciliation process, dramatically reducing the workload on the finance team and improving overall efficiency.

Results

·        High Matching Accuracy: The AI-driven solution resulted in a much higher rate of successful payment-to-invoice matching, even when remittance information was incomplete.

·        90% Automation: The Media Company was able to automate close to 90% of its cash reconciliation process, reducing the need for manual labor and minimizing errors.

·        Efficiency Gains: The automation allowed the finance team to process payments more quickly and accurately, freeing up resources for more strategic financial tasks.

·        Timely Financial Reporting: With faster and more accurate reconciliation, the Media Company was able to close its books in a timely manner, providing management with up-to-date financial insights.

·        Scalability: The automated solution provided the capacity to handle the growing volume of transactions without requiring proportional increases in staffing.

 Conclusion

Through the implementation of AutoCash.ai’s AI-driven automated reconciliation solution, the Media Company was able to significantly enhance its cash management processes. This case study illustrates the power of automation and AI in handling complex, high-volume financial operations, leading to improved efficiency, accuracy, and financial reporting.