A Case Study in Knowledge Acquisition for Insurance Risk Assessment using a KDD Methodology

Abstract

We describe some initial experiences in dealing with the task of acquiring knowledge where a very large collection of case histories is available. A Knowledge Discovery in Databases (KDD) approach is taken. KDD is the process of extracting novel information and knowledge from large databases, consisting of many interacting stages performing specific data manipulation and transformation operations with an information flow from one stage onto the next (and usually with feedback into previous stages). We characterise our experiences of this process for the task of acquiring knowledge for the domain of motor vehicle insurance premium setting for NRMA Insurance Limited. Keywords: Knowledge acquisition, knowledge discovery in databases, data mining, insurance premiums, risk analysis, fraud. 1 Introduction Knowledge Discovery in Databases (KDD) is commonly defined as the "non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data" (...