AI-Powered Invoice Reconciliation App from Scry Analytics

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Collatio® - Invoice Reconciliation software from Scry Analytics extracts and reconciles relevant data with more than 95% accuracy from invoices, purchase orders, and related agreements thereby avoiding duplicate and wrong payments, ensuring early payment discounts, avoiding late payment penalties and strained supplier relationships.

“Scry's AI-based invoice reconciliation software extracts and reconciles relevant data with more than 95% accuracy from invoices, purchase orders, and related agreements, reduces the processing cost and manual labor by 75%, and mitigates several other inadequacies of manual reconciliation.”

Around 13 billion invoices were sent in the United States in 2019 from businesses to each other and to government agencies. The current process to reconcile these invoices is manual and costs 2%-4% for an average invoice worth $700 to $800. Keeping this in view, Scry Analytics has developed a proprietary software app called Collatio® - Invoice Reconciliation.

According to Dr. Alok Aggarwal, CEO and Chief Data Scientist of Scry Analytics, “This AI-based invoice reconciliation app extracts and reconciles relevant data with more than 95% accuracy from invoices, purchase orders, master services agreements and statements of work, reduces the processing cost and manual labor by 75%, and mitigates several other inadequacies of manual reconciliation.”

Collatio® - Invoice Reconciliation uses more than 30 proprietary algorithms and pre-built ontologies for the extraction and reconciliation of various entities from invoices and related agreements. It also uses external data enrichment to reconcile the information present in these invoices. By doing so, it detects incorrect and potentially fraudulent invoices with the wrong price, wrong quantity, missing tax amount, missing tax identification number, no purchase order or contract, incorrect net amount, and more. It also creates a chronological sequence of all invoices that were provided by a specific supplier. Next, it determines duplicate invoices as well as those that may not be exact duplicates but contain duplicates at the invoice-item level. Finally, it helps in mitigating the following shortfalls of the manual reconciliation process:

  • Invoices come in varied formats including paper, fax, scanned, PDF machine-readable, and spreadsheets. Manual extraction of relevant information from these formats is error-prone and laborious.
  • Manual reconciliation also leads to money slippage due to inadequate analysis and often firms continue to pay for products and services they no longer use.
  • To avoid duplicate or wrong payments, reconciliation with other invoices, POs, MSAs, or SOWs usually causes delays that lead to losing early payment discounts, incurring late payment penalties, repeated supplier inquiries, and strained supplier relationships. To mitigate the issue of duplicate payments, sometimes firms hire third party examiners who charges about one-third of the recovered amount as a contingent fee per invoice that is exorbitant for large invoices whereas recovery firms are reluctant to recover money for smaller ones.

Since more than 80% of all invoices are received by small and medium-sized businesses (SMBs), who have limited funding for capital expenses, Collatio® - Invoice Reconciliation app is sold in SaaS (software as a service) mode but can also be installed on-premise and behind a firm’s information technology firewall.

For more details on Scry’s Collatio® - Invoice Reconciliation, click here.

Company Details – Scry Analytics
Scry Analytics (http://www.scryanalytics.ai) was founded in 2014 and builds innovative AI-based enterprise applications that enable clients to rethink and automate their data-driven and manually intensive business operations. Scry’s family of apps include Collatio (for ingesting, extracting, and reconciling unstructured and structured data), Concordia (for ingesting and harmonizing IoT data), Anomalia (for detecting anomalies and potential fraud), and Risc (for predicting operating and marketing risks).

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Alok Aggarwal

Akanksha Singh
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