Journal of Networks · 2014 · 19 citations · 11 references
BusinessInsuranceDecision Support SystemsKnowledge RepresentationRulemakingExpert ModelingExpert SystemsFraud DetectionInsurance FraudKnowledge-based Decision SupportRule-based RepresentationInsurance Claim Fraud DetectionKnowledge Base EngineeringKnowledge EngineeringKnowledge-based Expert SystemsMachine Learning For InsuranceMedical Expert SystemsRule-based Detection SystemsRule-based Knowledge Representation
At present, the false claim cases related to insurance fraud emerge endlessly. In respect of frequent fraud cases of social insurance undertaken by government organizations, the inspection procedure usually relies on experts’ experience for verification and experienced personnel in charge of checking. However, due to heavy work load insufficient manpower and lack of experience, the ratio of miscarriages of justice is very high, which leads to improper settlement of claims and the waste of social resources. In this paper, we used rule technology to improve the above inefficiency. We employ a knowledge engineering methodology to analyze problems and construct knowledge modesl, including the domain schema and rules. We implement the knowledge model along with the existing database applications. The benefits generated by the research are: (1) establishing a knowledge system with expertise reasoning to solve the review problems of massive cases, (2) significantly reducing the large labor cost and consumed time of the existing reviewing system, and (3) improving the application level of traditional database in the expert reasoning system.
11
Data mining: concepts and techniques
Jiawei Han, Micheline Kamber · Choice Reviews Online · 2012 · 28.9K citations
Prolog programming for artificial intelligence
Computer-Aided Design · 1986 · 1.1K citations
Richard A. Derrig · Journal of Risk & Insurance · 2002 · 162 citations
Business, Accountancy, Insurance +12