Analyzing Life Insurance Data with Different Classification Techniques for Customers’ Behavior Analysis

Rahman, Md. Saidur; Arefin, Kazi Zawad; Masud, Saqif; Sultana, Shahida; Rahman, Rashedur M.

doi:10.1007/978-3-319-56660-3_2

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Next, Thakur and Sing [20] employed a classification approach based on decision trees to create a prediction system based on customer data. They trained data on people who purchased auto insurance online and classified new clients for their interest in online insurance based on the characteristics of the customers.…”

Section: Data Miningmentioning

confidence: 99%

“…Besides that, a study with an imbalanced dataset showed that balancing improved performance significantly. Rahman et al [20] stated that their objective is to identify a classifier that can distinguish between a regular client of an insurance company and a non-regular customer. They additionally apply balancing algorithms in order to balance the data.…”

Section: Data Miningmentioning

confidence: 99%

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Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

2023

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Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provide a helpful framework for predicting potential life insurance policyholders using a data mining approach with different sampling methods and to lead to a transition to sustainable life insurance industry development. Various samplings, such as the Synthetic Minority Over-sampling Technique, Randomly Under-Sampling, and ensemble (bagging and boosting) techniques, are proposed to handle the imbalanced dataset. The result reveals that the decision tree is the best performer according to ROC and, according to balanced accuracy, F1 score, and GM comparison, Naïve Bayes seems to be the best performer. It is also found that ensemble models do not guarantee high performance in this imbalanced dataset. However, the ensembled and sampling method plays a significant role in overcoming the imbalanced problem.

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Section: Data Miningmentioning

confidence: 99%

Section: Data Miningmentioning

confidence: 99%

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

2023

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“…Data mining methods have been verified to be effective in the topics of insurance 5 . Classification methods are used to foresee the behavior of insurance holder to give an advice about choose of insurance 6 . Asymmetric information，casing the break of an insurance company, is found by artificial intelligence to reduce this kind of risk.…”

Section: Introductionmentioning

confidence: 99%

Analysis of medical insurance in China by neural network

Xiang

2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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Medical insurance plays role in the wellbeing of population. However, the difficulty with the amount addition of participants increases dynamically. Especially, the increase of organizations including in this domain makes optimization of medical insurance complex. Thus, it is necessary to find the rule based on the information provided by the related to data as supplementary to support decision making. In order to finish this task, Neural Network, proved its robustness in data analysis, is included in the proposed data management frame. Input and cost as the most important attributes of medical insurance are set as target features. Then, the relationship between these two attributes must be considered. A skipping window is defined to adjust the proportion of the target features in training and testing stages. NN network with double direction of time window for medical insurance is given to foreseeing target attributes. Based on the simulation result, the largest average accuracy is 0.8333.

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Classification on Participants Renewal Process in Insurance Company: Case Study PT XYZ

Utomo

Damanik

Budi

2021

2021 9th International Conference on Information and Communication Technology (ICoICT)

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Analyzing Life Insurance Data with Different Classification Techniques for Customers’ Behavior Analysis

Cited by 3 publications

References 4 publications

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

Analysis of medical insurance in China by neural network

Classification on Participants Renewal Process in Insurance Company: Case Study PT XYZ

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