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Friday, May 31
Machine Learning
Machine Learning E-Posters, II
Fri, May 31, 3:00 PM - 4:00 PM
Grand Ballroom Foyer

Predicting Claims Litigation using Text Mining (306335)

Aaron Barel , Univerisyt of California, Santa Barbara  
Mingxi Chen, Univerisyt of California, Santa Barbara  
Janet Duncan, Univerisyt of California, Santa Barbara  
*Xiyue Liao, Universiry of California, Santa Barbara 
Syen Yang Lu, Univerisyt of California, Santa Barbara 

Keywords: text mining, predictive model, binary classifier

Litigated claims are the most costly claims for insurance companies. Predicting which claimants are likely to litigate enables proper handling of claims prior to attorney involvement. Using small business claims data from an insurance company, we develop predictive models that will indicate whether or not a claimant will litigate. Our models utilize machine learning algorithms and natural language processing to perform prediction, using quantitative data and text data. By evaluating each model’s performance, an insurance company can choose which model to use in order to decide which claims and claimants need proper assignment.