The effect of relationship marketing on customer satisfaction in insurance agencies

The research aims to determine the effect of relational marketing on customer satisfaction in insurance agencies. The research is a quantitative study, and the survey method was used. The applied questionnaire was composed of scales whose reliability and validity was proven in the literature. In this context, the data for the research was obtained by questionnaire from consumers in Antalya between September-November 2019 and using home or car insurance services in insurance agencies. The data obtained from the questionnaires conducted with 461 participants were evaluated in the SPSS 25.0 package program, and regression analyses were carried out in line with the research purpose. First, the scale was subjected to factor analysis, and Cronbach Alpha values were calculated. According to the results, the scale is reliable and valid. Then, the means of the scale items were calculated. According to the results of the mean analysis, it can be said that the perceptions of the participants regarding the competence of the insurance agency they receive service from are higher than the other dimensions. According to the results of the regression analysis, trust, communication commitment, and competence, which are four dimensions of relational marketing in insurance agencies, have a positive effect on customer satisfaction.

Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification

Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.

Damaged Vehicle Parts Detection Platforms using Deep Learning Technique

Automatic vehicle damage detection platform can increase the market value of car insurance. The es- timation process is usually manual and requires hu- man experts and their time to evaluate the damage cost. Intelligent Vehicle Accident Analysis (IVAA) system provides an artificial intelligence as a service (AIaaS) for building a system that can automatically assess vehicle parts’ damage and severity level. The insurance company can adopt our service to build the application to speedup the claiming process. There are four main elements in the service system which support four stakeholders in an insurance company: insurance experts, data scientists, operators and field employees. Insurance experts utilize the data label- ing tool to label damaged parts of a vehicle in a given image as a training data building process. Data scientists iterate to the deep learning model build- ing process for continuous model updates. Opera- tors monitor the visualization system for daily statis- tics related to the number of accidents based on lo- cations. Field employees use LINE Official integra- tion to take a photo of damaged vehicle at the acci- dent site and retrieve the repair estimation. IVAA is built on the docker image which can scale-in or scale- out the system depend on utilization efficiently. We deploy the Faster Region-based convolutional neural network, along with residual Inception network to lo- calize the damage region and classify into 5 damage levels for a vehicle part. The accuracy of the localiza- tion is 93.28 % and the accuracy of the classification is 98.47%.

Applying of the Extreme Value Theory for determining extreme claims in the automobile insurance sector: Case of a China car insurance

According to the Chinese Health Statistics Yearbook, in 2005, the number of traffic accidents was 187781 with total direct property losses of 103691.7 (10000 Yuan). This research aims to fill the gap in the literature by investigating the extreme claim sizes not only for the entire portfolio. This empirical study investigates the behavior of the upper tail of the claim size by class of policyholders.

AUTO INSURANCE: RUSSIAN AND FOREIGN PRACTICE.

This article discusses car insurance in Russia and in the world. Auto insurance in Russia has its own characteristics at the level of legislation. The article also discusses the types of motor transport insurance that are most common for the Russian and global insurance market.

CURRENT TRENDS OF AUTOTRANSPORT INSURANCE IN UKRAINE

The research of the article is aimed at highlighting the essence and features of the functioning of the car insurance market in Ukraine. The study found that motor insurance is associated with profound economic and social changes in society due to mass motorization, the growth of the car fleet and traffic intensity, as well as huge material losses as a result of road accidents. It should be noted that freight transport is developing quite rapidly both within the country and abroad. At the same time, an increase in the number of intercity bus transportation, excursion and tourist bus services has been established, and as a result, international motor tourism is growing. It is proved that these factors contribute to the growth of accidents, losses in the transportation of goods, increase accidents with passengers and pedestrians on highways and, accordingly, material and social losses of society, population, commercial and government agencies. It is substantiated that insurance in general and civil liability insurance, as its integral part, is an infrastructure that helps to increase the efficiency of all areas of business. This determines the importance of the development of all types of insurance in Ukraine, taking into account the process of integration into the world community. It is established that the development of insurance market in our country should be based on the study and balanced use of experience of industrialized countries with long traditions in the insurance market, legal regulation of insurers and diversification of various types of insurance. However, it should be borne in mind that the world community has invented universal means of compensation, which is the most popular type of liability insurance worldwide – is the insurance of civil liability of owners of land vehicles. It provides for the payment of monetary compensation to the victim in the amount that would be collected from the owner of the vehicle on a civil lawsuit in favor of a third party for damage to life and health, as well as for damage or loss of property due to an accident or other road – transport accident due to the fault of the insured. Given the state and prospects of motorization in our country, as well as foreign experience in insurance market, we can say with confidence that liability insurance is one of the leading areas among other types of insurance. However, in its organization and implementation there are many different problems of legal, social, economic and organizational type. Recommendations on the prospects for the development of civil liability insurance of owners of land vehicles in Ukraine are given.

Factors influencing clinical symptoms and treatment of patients with traffic accident injuries: A retrospective chart review with a questionnaire survey

This study analyzed factors influencing clinical symptoms and treatment of patients with traffic accident injuries. It used a retrospective chart review and questionnaire survey obtained from 560 patients (266 men and 294 women). It also conducted follow-up observations of progress after car insurance settlements and investigated the usefulness of and patient satisfaction with integrative Korean medicine treatment for traffic accident injuries. Retrospective data of patients admitted for traffic accident injury were obtained. A questionnaire survey was conducted to collect data regarding the degree of traffic accident damage, severity of pain at settlement, any treatment after settlement and duration and cost of such treatment, and patient satisfaction with car insurance services and Korean medicine treatment for traffic accident injury. The results showed no significant association between pain and the degree of damage to the car at the time of traffic accident (P = 0.662), although the degree of damage to the car was more significantly associated with time to reach a car insurance settlement than severity of pain in the patient (P = 0.003). There was no significant association between the degree of damage to the car in a traffic accident and pain after a traffic accident. Greater severity of pain at the time of the car insurance settlement was associated with greater cost and longer time spent in treatment after the car insurance settlement.