Cancel-for-Any-Reason Insurance Recommendation Using Customer Transaction-Based Clustering

Sadreddini, Zhaleh; Dönmez, İlknur; Yanıkömeroğlu, Halim

doi:10.1109/access.2021.3064929

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…The study (Uğur and Akbıyık 2020) shows that travelers have a high probability of canceling or delaying their trips when they hear the news, especially when the world is experiencing pandemics. Compared with other crises, COVID-19 proves that the pandemic has a much larger destructive impact on the travel and tourism industry (Sadreddini et al 2021). Due to the widespread impact of the COVID-19 pandemic, the World Travel & Tourism Council (WTTC) estimates the daily loss of one million jobs in the travel industry.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the PurTreeClust clustering algorithm ) was proposed for largescale transaction data, and the authors of Hsu et al (2012) propose a segmentation algorithm to identify similarities between clients. In the work of Sadreddini et al (2021), they propose an adaptive calculation method that minimizes the CPS fee for customers with low or no-cancellation rates, and maximizes the CPS fee for customers with high rates by using real-world customer transaction-based behavior data. Their algorithm can learn the cancellation rates from the customer's historical data, but also results in different fees for different customers.…”

Section: Introductionmentioning

confidence: 99%

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The personalized recommendation for OTA flight cancellation and change services during the pandemic

Gong¹,

Wang²,

Nie³

et al. 2022

J Revenue Pricing Manag

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The COVID-19 pandemic has had a dramatic impact on people's travels. Due to the recurrent pandemic and regionally different policies in China, travelers must pay a lot for flight cancellations and changes. To accommodate this, online travel agencies (OTA) can provide a more flexible ancillary as a supplement to the airline company's services. Here, we introduced the upgraded all-in-one (AIO) service package, which offers compensation for flight delays, changes, or refund. We also designed a dynamic recommendation engine (DRE), which can make real-time personalized recommendations. Backed by AB testing, the machine learning-based DRE not only raises the package attach rate without interrupting the flight ordering process, but also helps the customers cut cost when making flight cancellations or changes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The personalized recommendation for OTA flight cancellation and change services during the pandemic

Gong¹,

Wang²,

Nie³

et al. 2022

J Revenue Pricing Manag

View full text Add to dashboard Cite

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Explainable Artificial Intelligence (XAI) in Insurance

Owens

Sheehan

Mullins

et al. 2022

Risks

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Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.

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Cancel-for-Any-Reason Insurance Recommendation Using Customer Transaction-Based Clustering

Cited by 2 publications

References 38 publications

The personalized recommendation for OTA flight cancellation and change services during the pandemic

The personalized recommendation for OTA flight cancellation and change services during the pandemic

Explainable Artificial Intelligence (XAI) in Insurance

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