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About the customer – Customer is one of the top 3 largest credit card & payments companies in the world.

Customer Challenge

  • Teradata was the only platform that was catering to the enterprise reporting, This added to significant annual costs for the customer
  • Data was in silos and each BU had their own process to exchange data with other Bus, This added significant delays in developing products that required enterprise level data.
  • Lack of self-service of the enterprise data leading to higher time to market, costs & redundancies.

NucleusTeq Solution Approach

  • Centralized, Standardized & Certified Data for Consistency
  • Democratize Data through self-service API’s while eliminating redundancy
  • Ensuring compliance to Enterprise data security guidelines
  • Maximize application/data Reusability & Globalization

Outcomes Delivered

  • Comprehensive Metadata Management, Security Data Quality & Governance
  • Single source of truth
  • Data integrity & quality with financial B&C
  • Eliminated redundancy by 47%
  • Reduced Attribute SORs by 72%
  • 8 Petabyte data migrated
  • 18 Months total delivery time
  • 35+ M in operations savings per year

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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