REVI-MINER is a KDD-environment which supports the detection and analysis of deviations in warranty and goodwill cost statements. The system was developed within the framework of a cooperation between DaimlerChrysler Research & Technology and Global Service and Parts (GSP) and is based upon the CRISP-DM methodology as a widely accepted process model for the solution of Data Mining problems. Also, we have implemented different approaches based on Machine learning and statistics which can be utilized for data cleaning in the preprocessing phase. The Data Mining models applied have been developed by using a statistical deviation detection approach. The tool supports controllers in their task of auditing the authorized repair shops. In this paper we describe the development phases which have led to REVI-MINER.

Article Free Access Share on WAPS, a data mining support environment for the planning of warranty and goodwill costs in the automobile industry Authors: E. Hotz DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 Ulm DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 UlmView Profile , G. Nakhaeizadeh DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 Ulm DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 UlmView Profile , B. Petzsche DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 Stuttgart DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 StuttgartView Profile , H. Spiegelberger DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 Stuttgart DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 StuttgartView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 417–419https://doi.org/10.1145/312129.312307Published:01 August 1999Publication History 6citation556DownloadsMetricsTotal Citations6Total Downloads556Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper presents a project realized in the domain of risk management at a financial services subsidiary of DaimlerChrysler.The project resulted in the implementation of an innovative KDD approach designed to optimize the collection efforts in automobile financing.In contrast with the conventional "credit scoring"which is mainly geared towards assessing a customer's credit worthiness at the time when she/he applies for a loan -in this project we followed the credit life cycle downstream and focused on how to best deal with delinquent customers in an actual credit portfolio.The resulting Data Mining solution was implemented in the finance company's systems environment and in daily operations.