Intelligent Case Management

Overview

Many organizations have a variety of business operations teams who are dependent on an e-mail based processes to track requests, manage their teams and work requests.

These teams have a number of challenges including:

• High volume of requests which are dependent on manual case assignment based on review of the requests.
• Since teams use mail as a channel of communication, it lacks a single case view, queue management and reporting. There is also a limited audit trail of a case.
• Lack of organization or separation of important and not that important data in emails.
• Heavy dependency on the resolver and minimal opportunity to automate the business process.

Key Features & Functionality

The Intelligent Case Management System by TCS enables a seamless migration for such teams to a Digital Transformation Platform and lays the framework for future growth. The application brings the benefits of case management and artificial intelligence through Google AutoML to enable an optimized target state. Natural language processing is used for classification of the incoming requests and assignment to the right skilled consultants in the team.

The application provides:

• Multi-Channel Case Creation - Receive cases through email requests or via an online form for the application.
• SPAM Detection - Identification of SPAM requests based on rule based analysis of requests.
• AI-based Case Classification - The valid cases are evaluated with Google AutoML using a pre-trained ML model to classify the incoming cases into case types.
• Skill Based Assignment - The system is also configured to assign cases to the resolver based on the case types. This enables skill based auto-assignment of the incoming requests, without any manual intervention.
• Case Records - Access to active and historical cases

The benefits of this application include:

• Process Optimization - The application enables an easy move from a manual case tracking to a case management system with minimal effort.
• Straight Through Processing - Enables auto case assignment based on Google AutoML thus reducing manual intervention in the case tracking.
• Scalability - Allows organizations to further automate cases with downstream integration to reduce manual case processing.
• Appian Reports - Pre-built reports provide case tracking information based on SLAs and also allow the review of the machine learning accuracy.