Legal invoice review and validation is a significant pain point for most companies and legal departments. In a recent survey, legal operations professionals cited their top three challenges as:
- Business process improvements (59.7%)
- Cost containment and savings (49.3%)
- Staying abreast of law department technology (35.8%)
All three of these items apply to legal invoice review, where outdated processes and technologies, combined with increasing workloads, more often than not lead to lost time, compliance risks, reputational risks, possible fraud, unnecessary payments and more.
Then there are the invoice charges that manage to fly under the radar. These are ones that slip by preset e-billing rules and human review, leading to the approval of invoices with potential erroneous charges like nonworking travel, block billing, vague descriptions and work done by improper staff class.
How Corporate Legal Can “Right-Size” Legal Invoice Review
Billing tasks don’t have to take up more of your time than identifying and mitigating risks to your company. AI, combined with enterprise legal management and e-billing rules, helps companies more accurately and efficiently process invoices.
Now, we’re introducing a high-level guide titled “How to Find ‘Between the Rules’ Invoice Errors” that discusses how AI, combined with ELM and billing rules, is revolutionizing legal invoice review.
Here are just a few of the highlights you’ll find in the Quick Start Guide.
- The Limitations of Billing Rules: With paper invoices, in-house counsel never had time to review every line item. eBilling relieved a lot of strain by applying billing rules, which scour the invoices for keywords and parameters that might conflict with billing guidelines. However, because these rules cannot represent all potential billing language, they open the door for “between the rules” errors.
- How AI-Powered Invoice Review Software Fills the Gap: Unlike traditional e-billing tools, AI-enabled invoice review solutions are constantly learning, searching for discrepancies and improving invoice review via machine learning. For example, an unauthorized travel charge may make it through review because its description doesn’t match the exact parameters of the billing rules’ language. AI and machine learning can pick up on language that may not be explicitly called out in billing rules.
- What to Look For in an AI-Enabled Invoice Review Solution: All AI-enabled invoice review solutions are not created equal. When choosing a solution, you should look for specific features, including historical analysis, ELM integration, continuous learning, advanced analytics and reporting, advanced bill review services and reduced bill review time. The Quick Start Guide explains each of these features and what to look for.
Just how much of a difference can AI-assisted invoice review make? One historical review of a Fortune 500’s outside counsel spend found an extra 11-20% in potential savings. For example, when AI reviewed an average of $45 million in invoices for one year, it detected $900K of non-attorney billing, $1M in block billing and $100K of travel-related fees during the height of COVID.
AI-assisted invoice review also helps catch common errors fast, helps companies meet their ROI objectives more quickly, helps boost productivity and better insulates companies from fraud. It does so by combining smart rules, keeping experts in the loop and employing powerful analytics.
To learn more about finding those between the rules invoice errors, download the Quick Start Guide here.