Quadient Announces AI-Based Cash Application Module to Accelerate Invoice-to-Cash Processes

Quadient a leader in helping businesses create meaningful customer connections through digital and physical channels announced the global availability of a new Advanced Cash Application module for Quadient’s Intelligent Communication Automation SaaS platform, to offer mid-market and enterprise customers a complete end-to-end Accounts Receivable (AR) automation solution.

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Cash application is a crucial component of the invoice-to-cash process, and the recent shift to digital payments has added complexity for businesses of all sizes. Quadient’s new Advanced Cash Application module brings additional services powered by artificial intelligence and machine learning that enable financial teams to improve efficiency and increase accuracy by minimizing time spent on manual cash application activities through automation. Enriched with the new module, Quadient Accounts Receivable by YayPay now offers capabilities ranging from credit and collections management, payments, dispute management, business intelligence and analytics, invoicing, multichannel electronic and print delivery, and cash application.

“At Quadient, we integrate artificial intelligence innovation to expand our offerings with more automated and enriched services,” said Chris Hartigan, Chief Solution Officer, Intelligent Communication Automation at Quadient. “Whether an organization is challenged with cash application delays due to missing remittance advice or the sheer volume of incoming payments that need to be matched and processed, automating the cash application process drives faster processes for employees and customers. The new module reduces the risk of manual errors and lowers days sales outstanding (DSO), helping us address the requirements from our growing customer base and handle more use cases in a broader array of industries.”

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The new Advanced Cash Application module eliminates manual tasks for financial teams, such as applying incoming payments to customers and invoices and re-keying payments received from the bank. In instances where a payment has been received but the customer has not been identified, the software’s machine learning engine will automatically consider data—such as the type of payment, the amount and any remittance data—to provide a suggested customer. At that point, the AR team can review the transaction for easy and trustworthy cash matching and approve it, removing it from payment reminder workflows to avoid overcommunication and thus improving the customer relation.

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