Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Abdul-Rahman, Shuzlina; Arifin, Nurin Faiqah Kamal; Hanafiah, Mastura; Mutalib, Sofianita

doi:10.14569/ijacsa.2021.0120950

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Bhatia et al studied consumer life insurance purchasing behavior and stated that researchers could apply advanced supervised and unsupervised machine learning and Artificial Neural Network methods [8]. Meanwhile, previous studies recommend employing alternative classification methods such as Random Forest, Naive Bayes, or even Artificial Neural Networks for consumer segmentation and profiling [21]. This paper will use five classification models: Decision Tree, Logistic Regression, Naïve Bayes, Random Forest, and Artificial Neural Network.…”

Section: Methodsmentioning

confidence: 99%

“…They concluded, using data mining approaches, that segmenting customers may be accomplished. The results of consumer segmentation have been improved by implementing both clustering and classification algorithms [21]. Recent research by Pereira, K. et al [22] stated that the predictive ability of machine learning models may be hindered by privacy-preserving techniques and proved that discretization and encryption, two privacy-preserving methods, affect the accuracy of machine learning models using life insurance data.…”

Section: Life Insurance and Machine Learningmentioning

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: Methodsmentioning

confidence: 99%

Section: Life Insurance and Machine Learningmentioning

confidence: 99%

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

2023

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“…Clustering finds applications in various real-world scenarios, including market segmentation [1,2], healthcare [3,4], image processing [5][6][7], bioinformatics [8,9], social sciences [10], and text mining [11].…”

Section: Introductionmentioning

confidence: 99%

“…To distinguish the K-modes algorithm from the K-means algorithm, there exist at least three characteristics concerning similarity metrics and how they represent the cluster: (1) The K-means algorithm employs means to represent the clusters, whereas the K-modes algorithm uses modes. (2) The K-means algorithm utilizes Euclidean distance, whereas the K-modes algorithm employs a dissimilarity metric.…”

Section: Introductionmentioning

confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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“…Research showed that ML algorithms have proven extremely useful for addressing many predictions and classification problems with broad application scope, including customer classification and segmentation [5], market analysis [6], [7], and education [8]. Unfortunately, ML is relatively limited and very far from being used in real estate applications mainly in the Malaysian sense.…”

Section: Introductionmentioning

confidence: 99%

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

Abdul-Rahman¹,

Zulkifley²,

Ibrahim³

et al. 2021

IJACSA

Self Cite

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House price is affected significantly by several factors and determining a reasonable house price involves a calculative process. This paper proposes advanced machine learning (ML) approaches for house price prediction. Two recent advanced ML algorithms, namely LightGBM and XGBoost were compared with two traditional approaches: multiple regression analysis and ridge regression. This study utilizes a secondary dataset called 'Property Listing in Kuala Lumpur', gathered from Kaggle and Google Map, containing 21984 observations with 11 variables, including a target variable. The performance of the ML models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and adjusted r-squared value. The findings revealed that the house price prediction model based on XGBoost showed the highest performance by generating the lowest MAE and RMSE, and the closest adjusted r-squared value to one, consistently outperformed other ML models. A new dataset which consists of 1300 samples was deployed at the model deployment stage. It was found that the percentage of the variance between the actual and predicted price was relatively small, which indicated that this model is reliable and acceptable. This study can greatly assist in predicting future house prices and the establishment of real estate policies.

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Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

Cited by 4 publications

References 25 publications

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

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