On the prediction of claim duration for income protection insurance policyholders

Liu, Qing; Pitt, David; Wu, Xueyuan

doi:10.1017/s1748499513000134

Cited by 7 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the popular prediction tasks related to automobile insurance, the reduction in dataset dimensionality is also useful. Liu et al (2014) reduce their large claim frequency prediction to a multi-class prediction problem to aid the eventual implementation of Adaptive Boosting (AdaBoost) to automobile insurance data. The act of reducing the number of frequency classes contributes to AdaBoost presenting as superior to SVM, NN, DTs and GLM in terms of prediction ability and interpretability.…”

Section: Dimensionality Reductionmentioning

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: Dimensionality Reductionmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI) in Insurance

Owens

Sheehan

Mullins

et al. 2022

Risks

View full text Add to dashboard Cite

show abstract

“…LDA was originally developed in 1936 by Fisher, and it often produces models that obtain higher classification accuracies in comparison with more modern and complex classification methods [43]. It aims to maximize the ratio of the between-class variance to the within-class variance, and it provides the highest possible discrimination between different classes.…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

A study on ECG Signal Analysis and ECG databases

Karnewar¹,

Shandilya²,

Tambakhe³

2019

IJRAT

View full text Add to dashboard Cite

The ECG (Electrocardiogram) signal is continuous in nature and abruptly changing. For taking intelligent health care decisions related with heart diseases such as paroxysmal of heart, arrhythmia diagnosing, ECG signal needs to be analyze accurately. The ECG signal basically corresponds to the electrical activity of the heart. In the literature, the ECG signal has been analyzed and utilized for various purposes, such as measuring the heart rate, examining the rhythm of heartbeats, diagnosing heart abnormalities, emotion recognition and biometric identification. ECG analysis can contain several steps, such as pre-processing, feature extraction, feature selection and classification. Performing each step is crucial for the sake of the related analysis. In this work, the literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above. Each step in ECG analysis is briefly described, and the related studies are provided. Similarly, we have focused on the ECG database used for analysis.

show abstract

“…Linear discriminant analysis goal is maximize the ratio of the between-class variance to the within-class variance, and it provides the highest possible discrimination between different classes. LDA is utilized in some of the recent ECG classification studies [10].k nearest neighbor has a wide usage in most of the pattern recognition problems and is also employed in some recent ECG classification studies [11].…”

Section: Introductionmentioning

confidence: 99%

Processing of Biomedical Data with Machine Learning

Bogdanov¹,

Nasyrov²,

Dumchikova³

et al. 2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

View full text Add to dashboard Cite

The paper is about processing of biomedical data. It were used 13 methods of machine learning (Naive Bayes classifier for multivariate Bernoulli models, A decision tree classifier, An extremely randomized tree classifier, Classifier implementing the k nearest neighbors vote, Linear Discriminant Analysis, Linear Support Vector Classification, Logistic Regression, Nearest centroid classifier, A random forest classifier, Classifier using Ridge regression, Ridge classifier with built in cross validation, Gaussian Mixture Models, Support Vector Machines) and one method of deep learning (Multiplayer Perception). A discrete wavelet transform was used to extract of biometric features. Haar wavelets, Daubechi wavelets, Symlets, Coiflets, Biorthogonal, Reverse biorthogonal, Discrete Meyer (FIR Approximation) were used. The influence of Electrocardiorams (ECG) recording time on the accuracy of biometric identification and diagnosis of cardiovascular diseases was studied. It was found that the best methods of classification are: Multiplayer Perception, An extremely randomized tree classifier, Classifier implementing the k nearest neighbors vote and Logistic Regression aka logit MaxEnt classifier. Wavelet family doesn't affect significantly on accuracy of recognition. With increasing registration time, accuracy increases .

show abstract

On the prediction of claim duration for income protection insurance policyholders

Cited by 7 publications

References 15 publications

Explainable Artificial Intelligence (XAI) in Insurance

Explainable Artificial Intelligence (XAI) in Insurance

A study on ECG Signal Analysis and ECG databases

Processing of Biomedical Data with Machine Learning

Contact Info

Product

Resources

About