Banking and Financial Services Detection of Intent to Fraud using NLP for one of world’s largest banks

About the customer – Customer is one of the top 10 largest banks in the world.

Customer Challenge

  • Bank lost billions of dollars in penalties and trading losses
  • Challenge posed was to detect the intent to commit fraud proactively by monitoring communications in order to avoid penalties and damage to reputation
  • Large team of ~30 people were dedicated to ensure compliance using keyword search but face too many false positives

NucleusTeq Solution Approach

  • Delivered a semi-supervised learning-based NLP solution to aid in detecting fraudulent activity
  • Coordinated multiple rounds of reviews to improve solution accuracy across 10 categories
  • Attained expected precision in SLA within 2 months
  • Implemented solution on Stanford NLP, Spark and Tachyon

Outcomes Delivered

  • Delivered expected accuracy to detect email messages with scores for fraud categories
  • Elimination of a large part of the data via identification of non-relevant messages using learning algorithms
  • Defined templates unique to each type of fraud based on presence of different entities (persons, places etc)
  • Created an approach to profile all the users in the data by means of graph analytics algorithms

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