CASE STUDY

NLP-Based Solution for Managing Data Similarities for an Information Services Provider


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Industry | Information Provider
Technology | MLOps
Location | UK

The client is a professional information services provider using technology and expertise to provide insights and help businesses make informed decisions for growth, efficiency, and performance.

The team consists of more than 5,000 experts, including data experts, subject matter experts, and consultants who offer actionable intelligence across many industries including financial services, insurance, retail, automotive, etc.

Industry | Information Provider
Technology | MLOps
Location | UK

The client is a professional information services provider using technology and expertise to provide insights and help businesses make informed decisions for growth, efficiency, and performance.

The team consists of more than 5,000 experts, including data experts, subject matter experts, and consultants who offer actionable intelligence across many industries including financial services, insurance, retail, automotive, etc.


By downloading this content, you are agreeing to receive communications from Royal Cyber, including our Insights newsletter.




Challenges

Difficulty handling frequent incoming customers
Ineffective data similarities and matching job
Lack of understanding of every incoming transaction file

How We Did It

Our data science experts crafted and implemented an NLP-based MLOps solution that would help the client with lexical and semantic similarity and enable their business in handling every customer’s transaction requests and files.
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Key Outcomes

90%

Semantic similarity score reached

85%

Accurate match in shorter transaction files (less than 50 terms)

70%

Accurate match in longer transaction files (more than 150 terms)

Hassan Sherwani

Hassan Sherwani

Head of Data Analytics & Data Science

It was a great learning experience working with an industry-leading client in the information services sector. We started by understanding the client’s pain points and how the frequent influx of customers and their transaction files affected their business. The case allowed our data science personnel to create an MLOps-driven solution that would help them match their data similarities and locate inconsistencies promptly.

90%

Semantic similarity score reached

Audience

  • Executives, CTOs, Director

  • IT Consultants

  • Business Analysts

  • Project Managers

  • IT Project Coordinators

  • Architects and Specialists

Case Study

Learn how our NLP-based solution helped an information services provider manage data similarities, increase Semantic similarity scores, etc.

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