AI Platform Extracts Financial Data From Litigation Documents in Minutes
eFraud Services announced eFraud Investigator, an AI platform designed to convert thousands of pages of financial documents into usable evidence for litigation. The tool extracts data from PDFs, digitizes it, and verifies transactions against statement balances - a process that typically requires manual work by legal associates.
In high-net-worth divorce and fraud cases, opposing counsel often produces massive document dumps with redacted or rotated pages to slow discovery. Traditional e-discovery software makes these files searchable but leaves the numbers unusable for analysis.
What the Platform Does
eFraud Investigator handles redacted and rotated documents, extracting transaction data with what the company claims is nearly 100% accuracy. The platform automatically verifies extracted figures against the statement's ending balance, ensuring the data matches the source document.
The tool includes a dashboard that visualizes fund flows instantly, allowing attorneys to assess case viability before committing to forensic accounting work.
The Business Case
For law firms, the platform reduces billable hours spent on data entry. Barbara Steinberg, CEO of eFraud Services, said: "In litigation, data integrity is everything. eFraud Investigator delivers virtually 100% extraction accuracy and automatically verifies every transaction against the statement's ending balance - ensuring the evidence you present is mathematically identical to the source documents provided in discovery."
Steinberg founded the company in January 2020 with Robert Hayum as COO. Steinberg spent over 20 years analyzing bank statements and financial documents for federal, state, and local government agencies, law firms, and forensic accounting firms. Hayum held senior technology roles at Revlon and Coopers & Lybrand (now PwC).
The company is based in Naples, Florida. For finance professionals working in litigation support or forensic accounting, this addresses a specific operational bottleneck: converting static documents into structured data that can withstand courtroom scrutiny.
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