Case Studies

Uncovering Fraud Using Strategic Querying & Artificial Intelligence

J.S. Held Strengthens Family Law Practice with Asset Acquisition of Luttrell Wegis

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The Situation

Fraud is an inherently deceptive act. One or more individuals must take advantage of a gap in the guardrails for further personal gain. However, when the victims of this deception are hospitals, therefore ultimately medical professionals and their patients, the crime is particularly insidious.

Amidst the acquisition of a medical staffing company, a healthcare firm received a call from a puzzled client who had been billed twice for an already paid invoice. The client supplied proof of payment for the services provided to the recently acquired company and questioned the reason for the additional invoice. The healthcare firm, equally puzzled, assured the client they would investigate it.

What appeared to be a small invoicing error quickly revealed itself to be an established fraud scheme in the medical staffing company. Two accountants at the company mismatched funds and misled profitability through fraudulent misapplication of payments received from clients. J.S. Held was swiftly retained by the acquiring healthcare firm to determine the full extent of the fraud, which included specifying how long it had been going on, how much money had been misapplied to incorrect accounts, and who was involved with or has knowledge of the ongoing crime. The client also sought to reconstruct the acquired company’s accounts receivables dating back several years to support a potential insurance claim for the inflated sale price during the acquisition.

How We Advised

The scope and scale of the case necessitated a multi-pronged investigation approach, which engaged several teams within J.S. Held’s Digital Investigations & Discovery (DI&D) group. The eDiscovery team assisted in identifying, collecting, and discovering documents of interest for review. At the same time, the Investigative Data Analytics (IDA) team traced back the accounts receivable data for over 5K healthcare facilities to pinpoint the extent of the fraud’s damage and history.

The eDiscovery team procured 1.5M documents containing unstructured data, such as emails and messages, from the devices and communications of the parties involved in the investigation. The team uploaded these documents to Reveal, a document review platform, where they were tasked with creating a bank of supporting evidence to distribute accountability to the acting fraudsters. Even with the collaboration of a representative from the healthcare firm and a member of the retained legal counsel, the traditional approach of running a series of keyword searches only reduced the pile of documents from 1.5M to 980K.

To increase efficiency, the eDiscovery team engaged in Reveal’s Ask feature, a generative AI search tool that answers users’ questions by querying the documents they upload to the platform. In addition to returning a brief written answer, Reveal Ask also returns a list of the documents that informed its answer as reinforcement. The team systematically entered questions related to the case and flagged the documents cited by Reveal Ask. The eDiscovery team evaluated the 1.6K flagged documents using domain and sentiment analyses, as well as full read-throughs. This strategic application of AI allowed the review team to process 1.5M documents in weeks and provided the client with key evidence in administering justice and damages. Key documents of interest would not have been identified as many were not responsive to any culling criteria (i.e., keywords). However, these documents were located and made available for review with Reveal’s Ask feature.

The IDA team was tasked with piecing together the accounts receivable history for the company, which dates back four years. We did not have access to or knowledge of the existence of a centralized, accurate data source for the required information. The team remained agile and pivoted its investigative approach to ingest and merge information from various systems and stand-alone client records.

The IDA team procured the general ledger data for over 5K facilities as well as images of 42K payment records, which the team converted to workable data using SQL and Python. This allowed the team to match these payments to clients in the general ledger data. The team calculated a net potential difference of $11.3M in accounts receivable from this data.

J.S. Held’s work on this investigation allowed the acquiring healthcare firm and its legal counsel to prepare a comprehensive insurance claim and move to litigation against the staffing company, as the findings potentially invalidate the terms of the acquisition. The diverse analytical capabilities of the eDiscovery and IDA teams resulted in reliable qualitative and quantitative evidence to supplement the plaintiff. As the case progresses towards trial, J.S. Held’s work products will serve as crucial evidence in the administration of justice.

Key Contacts

Ken Feinstein
Managing Director
Digital Investigations & Discovery
+1 917 277 7868
[email protected]

 

Mike Gaudet
Managing Director
Digital Investigations & Discovery
+1 346 509 5373
[email protected]

 

Kiran Patel, RCA, EnCE, CEDS
Senior Director
Digital Investigations & Discovery
+1 973 755 3264
[email protected]

 

Matthew Cordell
Director
Digital Investigations & Discovery
+1 214 216 4960
[email protected]

Related Practice Areas

> eDiscovery Consulting Services
Our global eDiscovery experts leverage advanced technologies to quickly uncover relevant information, providing insight and acceleration in complex investigations. During an internal or government investigation, in response to litigation or dispute, or after a cyber event occurs, our eDiscovery team assists clients in determining what happened, when it happened, and how it happened.

 

> Forensic Data Analytics
We combine our knowledge and experience analyzing structured and unstructured data with our investigative expertise to help our clients uncover and manage legal, reputational, regulatory, and financial risk. Our team analyzes large volumes of financial, transactional, and operational data to identify fraud, implement enhanced compliance programs, and reduce the time and cost of investigations and litigation.

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