Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

Benedek, Botond; László, Ede

doi:10.2478/subboec-2019-0009

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…K-means clustering is used to classify driver aggressiveness to inform a risk index of driving behaviour on different road types (primarily urban vs. highway). Benedek and László (2019) compare several interpretable AI techniques in their identification of insurance fraud indicators, which each facilitate the segmentation of such fraud indicators. DTs are highlighted as suitable AI methods for such indicator identification and classification.…”

Section: Intrinsically Interpretable Modelsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Insurance

Owens

Sheehan

Mullins

et al. 2022

Risks

View full text Add to dashboard Cite

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.

show abstract

Section: Intrinsically Interpretable Modelsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Insurance

Owens

Sheehan

Mullins

et al. 2022

Risks

View full text Add to dashboard Cite

show abstract

“…Artis and other scholars have updated and developed the Logit model to construct the AAG model [8] , which has the advantage of handling missing claims sample data. Benedek utilizes the data mining tool SQL Server Analysis Services (SSAS) to identify key automotive insurance fraud indicators and compares the performance of decision tree and neural network data analysis methods [10] . A common indicator for measuring the correlation between two vectors is the Pearson correlation coefficient [11] , which is more suitable for processing high-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Discrimination of Insurance Fraud based on Data Mining

Yang,

2023

Proceedings of the 2nd International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2023, July 7–

View full text Add to dashboard Cite

Insurance is an important component of the financial system, playing an important role in social stability and ensuring people's livelihoods. With the vigorous development of the insurance industry this year, automobile insurance fraud has been a frequent occurrence. This article achieves effective discrimination of insurance fraud by constructing a model of insurance fraud. Firstly, preprocess the data from the insurance claims dataset and encode the text variables using average encoding; Then, this article conducts data correlation analysis on all attributes of the sample using Pearson correlation coefficient; Based on the results of the previous analysis, the K-means clustering method is used to achieve dimensionality reduction and enhancement of sample attributes; Finally, by training an SVM classifier with Gaussian kernel function, effective discrimination of insurance fraud is achieved. Through experimental verification, the model method is effective and can achieve accurate discrimination with an accuracy of 96%.

show abstract