Method and system for exception management

Abstract

State of the art approaches of exception management are reactive, manual, and intuition-driven. Command center teams often react to exceptions. They also use BI tools which only provide statistical observation, but fail to mine domain-aware insights and actionable recommendations. The disclosure herein generally relates to analyzing process exceptions, and, more particularly, to a method and system for generating a data model to analyze and predict process exceptions. The system generates a data model by using information on rules, associated properties and exceptions, as training data. The data model is further used to process information on different rules to identify exceptions, and then to generate recommendations in response to identified exceptions.