This paper describes results of using machine learning model to aid reduction of number of repairs in external workshops for motor insurance company. The model predicts the customer decision based on data stored in insurance company’s database as well as additional features. We built several models, based on decision tree, random forest, gradient boost, ada boost, naive bayesian, logistic regression, neural network, then we evaluated them on real data.

Built models were tested on separate evaluation dataset provided by the insurance company. Models achieved over 0.8 area under curve ROC and thus were accepted for a pilot study in the production environment.

Date Published: 6 November 2019
PDF: 6 pages
Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 111761U (6 November 2019); doi: 10.1117/12.2536523

Published in SPIE Proceedings Vol. 11176:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019
Ryszard S. Romaniuk; Maciej Linczuk, Editor(s)