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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