Case Study: FinTech Platform Reduces Fraud with NLP and Data Annotation
Client: Digital banking startup focused on fraud prevention
Service: Text annotation + classification for transaction logs, support chats, and KYC documentation
Challenge: The system was missing suspicious behavior patterns in text-based data (chat messages, descriptions, etc.).
Solution: We annotated thousands of real support conversations and transaction descriptions using NLP tagging (intent, entity, sentiment) and trained the model to detect fraud cues.
Result:
✅ 54% faster fraud detection time
✅ 19% reduction in false positives
✅ Improved KYC automation accuracy to 92%