The Latest Advancement For AI in Spend Analytics: Finding Legal Invoice Errors “Between the Rules” with InvoiceAI By Onit September 14, 2021 AI/ML,ELM 3 Minute Read Legal invoice review is rarely a top-ten task for corporate legal departments, meaning it’s the ideal process for AI in spend analytics to improve. That’s why Onit has announced the general availability of its AI-enabled invoice review offering – InvoiceAI. Onit’s InvoiceAI analyzes historical and real-time legal invoices to find errors “between the billing rules.” It uses AI and machine learning to support outside counsel guidelines, looking into common invoice areas of note such as non-working travel, block billing, vague descriptions and work done by improper staff class. How InvoiceAI Works Before the rise of e-billing and legal spend management, paper ruled the invoice process. Law firms sent substantial bills – think hundreds of pages – to their corporate clients. In-house counsel, in return, rarely had the time, tools or resources to scrutinize line items. The transition to e-billing opened the door for more technologies to improve bill review – namely, billing rules. Billing rules rely on specific descriptions and context provided (or not provided) by in-house legal professionals to evaluate line items. However, descriptions for line items can vary based on the biller and service. Invoices without the exact language or terms outlined in billing guidelines may evade review and be approved for payment. There is simply no way to code billing rules to cover every possible permutation of language that might populate future invoices. This is where AI in spend analytics enters the picture. InvoiceAI has been trained on millions of legal invoice charges. It fully integrates with Onit’s enterprise legal management system and works with billing rules to look for areas where overpayment is common. When these issues are flagged, InvoiceAI can automatically adjust an invoice to comply with guidelines or bring the item to reviewers’ attention. Essentially, InvoiceAI allows machine learning to do what it does best, looking for discrepancies and continually learning and improving its invoice review. It also allows Onit’s existing e-billing rules to continue doing what they do best – focusing on compliance with outside counsel guidelines and flagging issues for additional expert review. Finally, it allows legal operations teams to do what they do best: reviewing trends for compliance, managing vendor relationships and implementing best practices across outside counsel guidelines. As a result, corporate legal departments benefit from: A reduction in invoice review time due to better recommendations and less manual work The ability to review past invoices and have AI identify errors and unnecessary payments Insight into legal spend trends and vendor performance Access to Onit’s partner Sterling Analytics, the leader in third-party invoice review. How to Learn More About AI in Spend Analytics To learn more about InvoiceAI, hear Matt DenOuden, Senior Vice President of Sales, and Mary Fuzat, Vice President of Product Management, discuss how AI in spend analytics boosts efficiency and saves money in this podcast. InvoiceAI is now available to all Onit customers. Reach out to us today at [email protected] to learn more about InvoiceAI and Onit’s enterprise legal management system. You can also schedule a demo here. Today's Popular Post Business Process Management The Next Steps for DEI in Legal: A Conversation with JusticeBid Read More Business Process Management The Power of Intelligent CLM: Leveraging AI to Optimize Contract Management Read More Business Process Management AI (+CLM): It’s Not Just a Buzzword Read More Subscribe Today Sign up for updates on new posts from our blog. Related Readings Business Process Management The Next Steps for DEI in Legal: A Conversation with JusticeBid Read More Business Process Management The Power of Intelligent CLM: Leveraging AI to Optimize Contract Management Read More Business Process Management AI (+CLM): It’s Not Just a Buzzword Read More Thank you for subscribing!