In insurance business, when a law firm fills in an invoice and submits it to its client, a reviewer on client’s side reads and validates entries with respect to Legal Service Agreement (LSA). This process is manual, time consuming and error prone. There are no insights from past similar invoices to expedite the process. Also, it is difficult to assess line items individually leading to erroneous payouts of items that should have been rejected or adjusted.
Implement ScryAudit® to validate invoices against agreements. The solution offers:
- Unsupervised Clustering and NLP algorithms - Identified and extracted important attributes from unstructured text and categorized narratives into categories and subcategories
- Classification using Deep Learning algorithms - Automated approval of expenses and adjustments to narratives based on past approvals, LSA guidelines and NLP engine
- Identify and reduce erroneous payments - Analysis on a small sample set of 4000 of flagged line items from expenses revealed approximately $70,000 could have been saved by avoiding human error.
- Summarized extraction and improved efficiency - A structured summary of the expense, much easier and faster to review, leads to prediction of approval of expense with 92%+ accuracy and adjustment of narratives for the data with 83% accuracy