Customer Churn Prediction for a Motor Insurance Company

Spiteri, Maria; Azzopardi, George

doi:10.1109/icdim.2018.8847066

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…The experimental result shows that random forest algorithms have a high accuracy of 91.6% compared to other supervised machine learning and ensemble learning techniques. Azzopardi et al, (2018 ) have developed a novel approach using a digital set-top box and have overwhelming histories from cable networks' financial systems for customer churn prediction. According to the findings of the studies, customer payment practices, customer consumption, and customer viewing concertation all significantly impact customer churn prediction.…”

Section: Related Workmentioning

confidence: 99%

Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia

Asfaw

2023

The Scientific Temper

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The number of service providers is increasing rapidly in every business. These days, there is plenty of options for customers in the banking sector when choosing where to put their money. As a result, customer churn and engagement have become one of the top issues for most of the banks. In this paper, a method to predict customer churn in a Bank using machine learning techniques, which is a branch of artificial intelligence, is proposed. The research promotes the exploration of the likelihood of churn by analyzing customer behavior. random forest (RF), logistic regression (LR), gradient boosting classifier (GBC), extreme gradient boosting classifier (EGBC), and light gradient boosting machine classifier (LGBCMC) are used in this study. Also, some feature selection methods have also been done to find the more relevant features and verify system performance. The experimentation was conducted on the churn modeling dataset from Kaggle. The results are compared to find an appropriate model with higher precision and predictability. As a result, using the Random Forest model after oversampling is better than other models in terms of accuracy. The experimental result shows that the Light Gradient Boosting Machine classifier outperformed with an accuracy of 98%, a precision of 97%, and a recall of 100%, with an AUC of 99% than other proposed supervised machine learning algorithms with balanced datasets across all evaluation metrics.

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Section: Related Workmentioning

confidence: 99%

Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia

Asfaw

2023

The Scientific Temper

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“…A dynamic competitive environment is evident in such a strictly regulated market. The interference of government is not observed with the additional insurance policies and this combination develops a competitive and dynamic environment [25]. There is a reduction in customer churn from 6.9% to 5.3% in 2018 in health insurance firms, but this still covers 1.2 million customers due to the stagnant price level of health insurance.…”

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confidence: 99%

The use of knowledge extraction in predicting customer churn in B2B

Jamjoom

2021

J Big Data

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Data mining techniques were used to investigate the use of knowledge extraction in predicting customer churn in insurance companies. Data were included from a health insurance company for providing insight into churn behaviour based on a design and application of a prediction model. Additionally, three promising data mining techniques were identified for the prediction of modeling, including logistic regression, neural network, and K-means. The decision tree method was used in the modeling phase of CRISP-DM for identifying the attributes of churned customers. The predictive analysis task is undertaken through classification and regression techniques. K-means clustering variation is selected for exploring if the clustering algorithms categorize the customers in churning and non-churning groups with homogeneous profiles. The findings of the study show that data mining procedures can be very successful in extracting hidden information and get to know customer's information. The 50:50 training set distribution resulted in effective outcomes when the logistic regression technique was used throughout this study. A 70:30 distribution worked effectively for the neural network technique. In this regard, it is concluded that each technique works effectively with a different training set distribution. The predicted findings can have direct implications for the marketing department of the selected insurance company, whereas the models are anticipated to be readily applicable in other environments via this data mining approach. This study has shown that the prediction models can be utilized throughout a health insurance company's marketing strategy and in a general academic context with a combination of a research-based emphasis with a business problem-solving approach.

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“…Long term customers would be more beneficial and, if satisfied, may provide new referrals. (Shirazi 2018, Ekinci et al 2012, Vafeiadis et al 2015, Wadikar 2020. Hence, customer churn retention analyses are designed to predict which customers are about to churn and facilitate an accurate segmentation of the market which allows organizations to target the customers who are most likely to churn with a retention campaign (Zaqueu 2019).…”

Section: Introductionmentioning

confidence: 99%

“…For financial institutions including insurance, it is even more complex to identify customer churn due to the sparsity of data as compared to other domains. This requires longer investigation periods for churn prediction (Kaya et al 2018, Wadikar 2020. Hence, instead of predicting the binary response variable and classifying customers of a specific annual period into churners and non-churners, this research aims to observe the behavior of policyholders to define time before the said crucial event of interest.…”

Section: Introductionmentioning

confidence: 99%

Game Over: An Application of Customer Churn Prediction using Survival Analysis Modelling in Automobile Insurance

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Customer churn poses a big challenge in financial institutions including the field of insurance. Hence, retention analyses are designed to predict time to churn and identify which among the customers are most likely to leave the company so that retention campaigns and strategies may be done to address the issue. By using 6-year data of individual insurance policyholders from an automobile insurance company in Asia, the study utilized Kaplan-Meier, Log-rank tests and Cox-Proportional Hazard model to understand the risk of churning and to identify significant factors affecting the said event of interest. Demographic variables, including age, marital status, region, as well as policy and claim details, including mortgagee and claim indicator, were found to be significant in the hazard model. Furthermore, results of K-Prototypes, a clustering algorithm used for handling mixed data types, show that customers can be grouped into three (3) clusters and that strategies must be geared towards retaining the young, single and working adults' segment.

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Customer Churn Prediction for a Motor Insurance Company

Cited by 11 publications

References 28 publications

Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia

Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia

The use of knowledge extraction in predicting customer churn in B2B

Game Over: An Application of Customer Churn Prediction using Survival Analysis Modelling in Automobile Insurance

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