Auto Insurance Tenure Prediction and Analysis

The purpose of this project is to understand the main factors that drive customer tenure within auto insurance industry for six or more years. The analysis is based on three years of the J.D. Power Auto Insurance survey data. For the analysis, multiple binary machine learning algorithms were implemented and measured to classify whether customers would stay with the same insurer for more than six years. Random forest was found to be the most robust model as compared to logistic regression, decision trees, and xgboost

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Pay-at-the-Pump Auto Insurance

PAY-AT-THE-PUMP is a proposal to replace the current insurance system of lump sum payments for automobile insurance by a mechanism whereby motorists pay for their insurance as they buy fuel for their vehicles. PAY-AT-THE-PUMP has several advantages. It reduces insurance cost and cross subsidies and enhances equity. It also benefits the environment, safety, balance of payments, and security. In this paper we study limited but very important issues in the theory and implementation of PAY-AT-THE-PUMP insurance. We address issues of efficiency, subsidy, equity, externalities, safety, insurance cost and cost of insuring the uninsured motorist under a PAY-AT-THE-PUMP regime. We use the insurance industry’s criticisms of mandatory auto insurance as a lens through which we view PAY-AT-THE-PUMP insurance and ask how PAY-AT-THE-PUMP fares by comparison. Finally we address one aspect of insurance that has been neglected in the current debate -- the human dimension of the problem of uninsured motorist and the contribution PAY-AT-THE-PUMP can make to solve this problem.

On the road again: traffic fatalities and auto insurance minimums

Prior research on policy-induced moral hazard effects in the auto insurance market has focused on the impact of compulsory insurance, no-fault liability, and tort liability laws on traffic fatalities. In contrast, this paper examines the moral hazard effect of a previously overlooked policy variable: minimum auto insurance coverage. We hypothesize that state-mandated auto insurance minimums may “over-insure” some drivers, lowering their incentives to drive carefully. Using a longitudinal panel of American states from 1982 to 2006, we find that policy-induced increases in auto insurance minimums are associated with higher traffic fatality rates, ceteris paribus

Research on UBI Auto Insurance Pricing Model Based on Parameter Adaptive SAPSO Optimal Fuzzy Controller

Aiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates

Research on UBI auto insurance pricing model based on parameter adaptive SAPSO optimal fuzzy controller

Aiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates

Auto Insurance Business Analytics Approach for Customer Segmentation Using Multiple Mixed-Type Data Clustering Algorithms

Customer segmentation is critical for auto insurance companies to gain competitive advantage by mining useful customer related information. While some efforts have been made for customer segmentation to support auto insurance decision making, their customer segmentation results tend to be affected by the characteristics of the algorithm used and lack multiple validation from multiple algorithms. To this end, we propose an auto insurance business analytics approach that segments customers by using three mixed-type data clustering algorithms including k-prototypes, improved k-prototypes and similarity-based agglomerative clustering. The customer segmentation results of these algorithms can complement and reinforce each other and demonstrate as much information as possible to support decision-making. To confirm its practical value, the proposed approach extracts seven rules for an auto insurance company that may support the company to make customer related decisions and develop insurance products