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E-billing, invoicing, payments, machine learning


It’s no secret that many law firms bill clients as much as they reasonably can for services. Corporate legal departments working with outside counsel often go through invoices line by line looking for potential overcharges or compliance missteps before payments are made, forcing law firms and legal departments into standoffs over invoicing.

Wolters Kluwer’s LegalVIEW BillAnalyzer is a new tool in the belt of corporate legal departments using Wolters’ billing software. The BillAnalzyer software uses machine learning to help bill reviewers find potential overcharges in invoices.

David Moran, senior director of product management for Wolters Kluwer’s Legal Analytics, spoke to Legaltech News about the new product and its machine learning capabilities.

Who it serves: Like many Wolters Kluwer products, BillAnalyzer is geared toward high-volume work. While the product is scalable, Moran said corporate legal departments and insurance claim defense organizations “that have a significant throughput of invoices in a month (greater than 400) and oversee a number of law firms (75-plus) are ones that should look at this offering.” High volumes and huge data sets are where machine learning approaches actually bring efficiency benefits, so gearing the product this way actually aligns best with the technology.

What it does: BillAnalyzer takes in invoices and uses machine learning to establish rules and hierarchies that allow the software to flag probable areas worth contesting, which then go to billing department staff for expert review. Moran noted the technology can identify possible overcharges. “Reviewers are able to identify in a timely manner issues that are ambiguous, like if the work being billed for was done by the proper level of experience (i.e. paralegal vs. associate),” Moran said.

Keeping tech “disruption” at bay: Machine learning tools often are designed with the intent to replace manual human labor, but the BillAnalyzer positions itself as squarely in the realm of assistive technology for staff. Moran said the workflow enabled by BillAnalyzer “allows reviewers to compare invoices against internal models,” but the work of that comparison is still imagined as a human job. Moran noted that the product could be used on either side of human review, but BillAnalyzer doesn’t seem to be looking to automate anyone out of a job.

What it’s trying to do: Basically, the tool infuses machine learning into invoicing analysis to provide “better benchmarking and identification of noncompliant invoices,” Moran said. Perhaps a fairly modest goal in a technology atmosphere promising a machine learning revolution, BillAnalyzer is looking more to dip a machine-learning toe into e-billing to see how the technology can best be used to curb legal spending costs.

Feeding the machine: While this tool has a lot to offer corporate legal departments, it might create headaches for billing specialists on the law firm side. Wolters Kluwer’s own research finds that firms are asked by clients to support an average of 13 different e-billing platforms. Given that law firm billing staffs are already oversaturated with different platforms they have to submit invoices to, they’re unlikely to be thrilled about adding another platform to their docket.

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