Machine Learning Based Ticket Audit Automation

Client Background:

Client is a global information technology company that specialises in providing industry-focused solutions integrated with leading-edge security to enterprises in the government, financial services and commercial markets.

Major offerings include security solutions, advanced data analytics, cloud and infrastructure services, application services and application and server software.

Business Context:

Client supports ticket management process for its suite of customer applications through a single web portal. These tickets raised by end customers are handled by different representatives for providing correct resolution as per agreed upon SLAs (Service Level Agreement). To maintain highest standards of customer service and ensure compliance with the process, closed and resolved tickets are manually audited every month across every compliance parameter. Each compliance parameter is evaluated and scored using set of rules that are verified against supporting documents and logs for each ticket. These documents can be PDF, document, excel, email logs plus other structured ticket parameters. Audit process is completely manual as of now and is best limited to only a sample of tickets i.e. 700-1000 per month.

Business Problem:

Therefore, it was desired to automate the audit process using Machine Learning/NLP driven solution.  Such a platform should free up resources otherwise utilised in manual review.

Solution Approach:

Ticket Audit Automation

Ticket Audit Automation

Solution Workflow:

Ticket Audit Automation

Example:

Sample audit question: Are the resolution details updated on the incident?

Solution steps:

Ticket Audit Automation

ROI:
  • 85% reduction in efforts
  • 10x improvement in turn around time
  • Reduction in errors
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