A significant volume of corporate fraud data is held within unstructured text documents, including procurement invoice descriptions, employee emails, whistleblower logs, and travel expense justifications. Fraud risk teams analyze this text data by deploying Natural Language Processing (NLP) engines.
Unstructured Text Document ──► Sentiment & Keyword Scan ──► Threat Score Allocation

NLP systems convert raw text files into structured data through specialized analysis techniques:
  1. Keyword and Phrase Identification: Scanning communication streams for specific terms that frequently correlate with bribery, collusion, or internal pressure (e.g., “off the books,” “special discount,” “override approval”).
  2. Sentiment Analysis Tracking: Monitoring text data within whistleblower submissions and internal message channels to track changes in corporate culture, helping to flag areas with elevated stress or toxic dynamics.
  3. Document Inconsistency Detection: Analyzing invoice descriptions across suppliers to identify plagiarized text or matching layout structures, which often signal phantom vendor generation schemes.

 
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