Case Study – Using Artificial Intelligence for Knowledge Management 2018-11-12T11:14:05+00:00

A Banking Success Story

An Artificial Language based Knowledge Management System for Identifying Subject Matter Experts anywhere across the Globe.


A multinational financial services client headquartered in Washington DC funds social development projects in Latin America and the Caribbeans by providing financial and technical support. The Bank’s core focus area involves overcoming challenges of social inclusion and equality, productivity and innovation and economic integration. The goal is to address issues such as gender equality and diversity, climate change and environmental sustainability; and institutional capacity and the rule of law.


The bank boasts of 100+ offices sprawled across the globe and supports a multilingual workforce. The bank’s officers have worked on various projects by which they have gained a lot of real-world experiences. They now possess a variety of skills, gotten a lot of operational knowledge and have developed themselves into Subject Matter Experts over certain key topics areas. These officers work from geographically spread locations using documents that are in English and/or Spanish and/or Portuguese or other languages.

The primary business goal and use case is to create a single integrated knowledge management platform for individuals to access, identify and reach out to individual SME’s seeking help on current projects and tasks at hand.


ThirdEye worked with the client to implement a cognitive computing application leveraging IBM Watson
services on the IBM Bluemix cloud infrastructure. The overall solution included a virtual agent that understands natural language query inputs and uses cognitive deductions to respond to the conversation; simulating a human-like conversation between users and the system.

ThirdEye leveraged AlchemyAPIs for advanced text analytics including, keyword extraction, entity extraction, sentiment analysis, emotion analysis, concept tagging, relation extraction, taxonomy classification, author/person extraction and relevance score extraction. The output was further processed to eventually create JSON structures with all relevant metadata and associated information of every SME.
SMEs were retrieved and ranked in real time as per the user’s queries

The process steps were as follows:

  • Process data from multiple sources including
    online publications, project documentations, blog
    postings, employee information from internal
    systems and online social media platforms.
  • Extract entities and metadata information about
    each entity.
  • Analyze all information processed so far to
    identify “Subject Matter Experts” on various topics
    across the enterprise.
  • Rank the SMEs, based on contributions,
    publications, etc. and create their deep profiles.
  • Enable users to query using simple English
    sentences through a “chat” interface to find &
    connect with relevant subject matter experts.
  • Perform drill down analysis on each ranked SME
    and look up all associated information & facts

Technologies Incorporated:

  • IBM Watson Developer Cloud
  • Alchemy API – for text analysis through natural language
  • Document Conversion – for converting various
    document formats to text documents.
  • Language Translator – to support multilingual
    conversations with users.
  • CloudantDB – for storing the output from AlchemyAPI
    and other unstructured data.
  • DashDB – for storing structured data and analytics.
    Watson Conversations – for automating interactions
    with end users.
  • Insights for Twitter – to load and analyze Twitter data.
    Suite of custom Java Applications for crawling, extracting
    and processing data from multiple sources
  • Purpose built search and ranking algorithms for SME
    search and ranking.
  • Custom NodeJS chat bot interface


The bank now believes that productivity across the globe will significantly increase for identifying Subject Matter Experts. Such enhancements will improve operational efficiencies by a margin of 30% to 40% on all funding and technical evaluations. Additionally, as the scope of the system further expands to include multiple uses cases including but not limited to knowledge management of subject expertise, improving cross-border collaboration and employee engagement across project implementations.


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