Optimizing Your Legal Invoice Review Process with A.I.
The challenges of invoice review
Invoice processing poses a logistical challenge for large legal departments. Spending millions on external legal advice can equate to tens of thousands of invoices and hundreds of thousands of individual line item descriptions containing detail of each task and activity completed by outside counsel. Finding the time to review these documents or to put a process in place that deals with this scale of input is challenging for any organization.
Cost control processes, such as billing guidelines, can be difficult to establish effectively without consistent invoice review. The full scope of what you’re billed for and what items you should establish guidelines for may not be fully understood without usable data. This can make agreeing rates for external legal work between an external provider and a company’s legal department difficult.
Law firm relations are of key strategic importance. Of course, corporate teams partner with law firms to collaborate on legal work. Reviewing their invoices is a necessary supporting process. Ultimately it’s not something you want to get in the way of legal service delivery. Time is too commonly spent chasing or querying invoices, or requesting information from firms about the accrued spend they plan to invoice for. Additionally, when it comes time for a quarterly review, or similar, a lack of aggregated data from the invoices which your partner firm has submitted can mean the discussion isn’t rooted against clear metrics. This makes managing the commercial aspect of the relationship more difficult.
Having an effective e-billing process in place which ensures invoices get reviewed and paid while also providing visbility gives legal departments a solution to these challenges. Reducing the amount of human touch required removes some of the burden from legal operations professionals and corporate lawyers.
New natural language analysis technology can read the individual line item descriptions on legal invoices and understand the activities and tasks outlined by the timekeeper. The data contained can be used in multiple ways by A.I. to optimize the invoice review process for your corporate team and provide a deeper understanding of how legal work is delivered.
Improving the process
Automated invoice review
The most immediate benefit of having an automated invoice review platform is the removal of extensive manual input required to review and approve invoices. The workload of in-house lawyers or legal operations professionals responsible for reviewing and approving invoices can be drastically mitigated.
As machine learning language analysis technology understands line item descriptions of the activities it can classify the work done according to the invoice and flag any violations which occur against your billing guidelines. Not only is the technology removing the need for invoice review, but it is also consistently applying your billing guidelines, a requirement of any successful cost control plan. When using A.I., invoices can be approved or rejected based on an appropriate set of conditions for your organization. For example, some legal teams may automatically approve invoices below a certain spend threshold, such as $5,000, whereas others may automatically reject and notify the law firm who submitted the invoice if any guideline breaches occur.
An automated email notification to law firms cuts out a significant amount of manual communication and speeds up the entire e-billing process.
The application of A.I. technology doesn’t stop at approving or rejecting invoices. Invoices from outside counsel contain a reservoir of data in the line item descriptions. New technology doesn’t just review against billing guidelines and established processes, it can take that data and surface it for reporting purposes.
These reporting tools allow users to see a breakdown of their legal spend and which firms are the most cost-effective in adhering to their guidelines. Spend against matter budgets or by practice area are examples of management reports commonly used. Tasks and activities completed by outside counsel on individual matters are logged and visualised from the line item descriptions in your invoices. Dashboards and reports can be configured to give visibility across an organization. They can be scheduled for delivery to relevant stakeholders, giving them a level of transparency that didn’t previously exist. All of this is achievable without the need for manually using spreadsheets to pull information from various sources.
Strategic decision making
The data that A.I. can uncover is useful for more than just management reporting. Users can take advantage of this large volume of information for future strategic decision-making. In-depth reports which go deeper than monthly spend management allow senior decision makers in the legal team to review how different types of legal work are resourced and adjust their resourcing mix. It provides significant quantitative and comparative information when determining the appropriate pricing arrangements with external service providers. They can use this data to inform panel review processes or relationship review meetings. Perhaps your team is relying too much on in-house counsel in a litigation matter and should look to expand the scope of work for one of your trusted firms. Or maybe they’re over reliant on outsourcing employment matters and it would be a more effective use of resources to move that work in-house.
In summary, optimizing your invoice review process doesn’t just mean you can free up additional time for your team to get more high-value work done. With A.I. technology it becomes a significant source of insight into how your legal team is working, helping to strategically guide how you resource legal work going forward.
Interested in finding out more? Check out our webinar. Brightflag provides A.I. Enterprise Legal Management software. Alex Kelly, COO and co-founder of Brightflag hosted a webinar discussing how leading corporate legal teams are automating invoice review and billing guidelines.