Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Mining GPS data to augment road models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The invisible future
When Things Start to Think
Probabilistic Estimation-Based Data Mining for Discovering Insurance Risks
IEEE Intelligent Systems
A comparative assessment of classification methods
Decision Support Systems
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Making the world (of communications) a different place
ACM SIGCOMM Computer Communication Review
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
A decision support system of dynamic vehicle refueling
Decision Support Systems
Adaptability in smart business networks: An exploratory case in the insurance industry
Decision Support Systems
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
Using data mining techniques to predict hospitalization of hemodialysis patients
Decision Support Systems
Data mining for credit card fraud: A comparative study
Decision Support Systems
A web spatial decision support system for vehicle routing using Google Maps
Decision Support Systems
GPS trajectory feature extraction for driver risk profiling
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Performance of classification models from a user perspective
Decision Support Systems
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Vehicle sensor data enable novel, usage-based insurance premium models known as 'Pay-As-You-Drive' (PAYD) insurance, but pose substantial challenges for actuarial decision-making because of their inherent complexity and volume. Based on a large real-world sample of location data from 1572 vehicles, the present study proposes a classification analysis approach that addresses (i) the selection of predictor variables, (ii) the presence of class skew and time-variant prior distributions, and (iii) the suitability of classifier scores as an aggregated actuarial rate factor. Using raw location data, we derive a set of 15 predictor variables that we use to train and compare logistic regression, neural network, and decision tree classifiers. We find that while neural networks exhibit superior classification performance, logistic regression is better suited from an actuarial viewpoint in several ways. In sum, our results clearly demonstrate the potential of high-resolution exposure data for reducing the complexity of PAYD insurance pricing in practice.