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PurposeDigital technology is revolutionizing insurance distribution allowing the insurer companies to reach customers via multichannel. The aim of this study is to segment potential customers of life insurance based on their information search, purchasing channels and personal characteristics in the digital environment.Design/methodology/approachThe study uses cross-sectional research survey. In total, 422 questionnaires were collected through a convenience sample of the Romanian population. The data was segmented based on consumer information touchpoints (online vs offline), purchase channel preference (offline by a professional vs online by a standardized platform) and personal characteristics (age, marital status and children).FindingsThe channel segmentation analysis revealed that information channel preferences are the most important clustering variables, followed by purchase channel preferences, marital status, having children and age. Four distinct segments were identified: young fully offliners (23.7%), mature fully offliners (31.5%), committed online searchers (23.2%) and cross-channel onliners (21.6%).Practical implicationsInsurance companies should adapt their communication and distribution strategy based on multichannel segmentation and should focus on digital touchpoints with costumers.Originality/valueFirstly, the paper reveals multichannel and hybrid segmentation for life insurance. Secondly, it extends the already studied retail channels with search engines and companies' websites. Thirdly, it extends the behavioural variables for channel segmentation with technology acceptance behaviour, attitude towards life insurance, knowledge about life insurance, attitude towards personal selling and quality appraisal of online information sources.

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Purpose The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection. Design/methodology/approach Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics. Findings This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research. Practical implications This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets. Originality/value This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

Business practice and various industry reports all show that automobile insurance fraud is very common, which is why effective fraud detection is so important. In our study, we investigate whether today’s widespread AI-based fraud detection methods are more effective from a financial (cost-effectiveness) point of view than methods based on traditional statistical-econometric tools. Based on our results, we came to the unexpected conclusion that the current AI-based automobile insurance fraud detection methods tested on a real database found in the literature are less cost-effective than traditional statistical-econometric methods.