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About the customer – Customer is one of the top 10 largest bank 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
About the customer – Customer is one of the top 4 Credit card company in the world
Customer Challenge
- Create a recommendation model for cross-sell/up-sell recommendations for 80+ Million users
- Performance requirements of 300-350 TPS
- Create a Multi-Touch Attribution Model (MTA) for channel optimization
- Integrate Data from 10+ different sources for building both models
NucleusTeq Solution Approach
- Developed a product recommendation engine that personalizes recommendations for 80 million customers
- Created a high availability big-data platform where the recommendation engine processes the data
- Created high availability API infrastructure to address the high TPS requirement
- Created a graphical model for MTA
Outcomes Delivered
- Savings of millions of dollars in promotional expenses
- Increased Net New Conversions from 2% to 7%
- Increase Upgrade Conversions from 8% to 14%
- Model for channel budget allocation led to increase in ROI