Case Study - NLP for Business Decisions by Avenga | IoT ONE Digital Transformation Advisors

	NLP for Business Decisions Avenga

Challenge	The client, operating in the financial sector, is working with large collections of client meeting notes in order to search for certain information, then organize and consolidate it into easy-to-read interactive sets so it could be used for different business purposes. So, they needed to search, find, and process texts quickly, and obtain actual insights from the data across the notes, in order to understand their (potential) customers, and identify market trends and risks for further informed business decisions by tagging documents with labels. Read More
About Customer	The client is a leading global financial consulting company that supports financial advisors, consultants, and institutional investors. Read More
Customer Name	Not disclosed Read More
Solution	To enable fast searches through the text that would reveal actual valuable information, out-of-the-box and custom approaches were compared: An out-of-the-box cloud solution that leverages the AWS Comprehend service. Custom solution based on Topic Modeling using LDA and Named Entity Recognition (NER) to tag documents with labels in order to enable a search through raw unstructured texts. The use of Topic Modeling helps to determine the list of topics discussed in an entire collection of meeting notes and that reveals the presence of one or more topics in each document. Using custom NER solutions, based on a deep learning model, they can detect both default and custom entities in real-time. Read More Log in to view content
Contents

Challenge

The client, operating in the financial sector, is working with large collections of client meeting notes in order to search for certain information, then organize and consolidate it into easy-to-read interactive sets so it could be used for different business purposes.

So, they needed to search, find, and process texts quickly, and obtain actual insights from the data across the notes, in order to understand their (potential) customers, and identify market trends and risks for further informed business decisions by tagging documents with labels.

About Customer

The client is a leading global financial consulting company that supports financial advisors, consultants, and institutional investors.

Customer Name

Not disclosed

Solution

To enable fast searches through the text that would reveal actual valuable information, out-of-the-box and custom approaches were compared:

An out-of-the-box cloud solution that leverages the AWS Comprehend service.
Custom solution based on Topic Modeling using LDA and Named Entity Recognition (NER) to tag documents with labels in order to enable a search through raw unstructured texts.

The use of Topic Modeling helps to determine the list of topics discussed in an entire collection of meeting notes and that reveals the presence of one or more topics in each document.

Using custom NER solutions, based on a deep learning model, they can detect both default and custom entities in real-time.

Log in to view content

Contents

Overview

NLP for Business Decisions