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An Effective Machine Learning Model for Customer Attrition Predictionin Motor Insurance Using Gwo-Kelm Algorithm

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An Effective Machine Learning Model for Customer Attrition Predictionin Motor Insurance Using Gwo-Kelm Algorithm IT in Industry, Vol. 9, No.1, 2021 Published Online 18-Feb-2021 AN EFFECTIVE MACHINE LEARNING MODEL FOR CUSTOMER ATTRITION PREDICTIONIN MOTOR INSURANCE USING GWO-KELM ALGORITHM Deepthi Das1, Raju Ramakrishna Gondkar2 1 Research Scholar, Department of Computer Science, CMR University, (India) Associate Professor, Department of Computer Science, CHRIST (Deemed to be University),(India) 2 Professor, Department of Computer Science, CMR University,(India) ABSTRACT: categorizes the collected data based on the policy renewals. Then, the behavior of the The prediction of customers' churn is a customers are also investigated, so as to ease challenging task in different industrial the construction process training classifiers. sectors, in which the motor insurance With the help of Naive bayes algorithm, the industry is one of the well-known industries. behaviors of the customers on the upgraded Due to the incessant upgradation done in the policies are examined. Depending on the insurance policies, the retention process of dependency rate of each variable, a hybrid customers plays a significant role for the GWO-KELM algorithm is introduced to concern. The main objective of this study is classify the churners and non-churners by to predict the behaviors of the customers and exploring the optimal feature analysis. to classify the churners and non-churners at Experimental results have proved the an earlier stage. The Motor Insurance sector efficiency of the hybrid algorithm in terms dataset consists of 20,000 records with 37 of 95% prediction accuracy; 97% precision; attributes collected from the machine 91% recall & 94% F-score. learning industry. The missing values of the records are analyzed and explored via Keywords - Customer chum, Customer Expectation Maximization algorithm that 91 ISSN (Print): 2204-0595 Copyright © Authors ISSN (Online): 2203-1731 IT in Industry, Vol. 9, No.1, 2021 Published Online 18-Feb-2021 retention, Extreme Learning Machine the inefficient tools development that fails to (ELM), Grey Wolf Optimizer (GWO), predict the risk of customer churn. Motor Insurance Generally, each data item in the 1. Introduction communication environment consists of churn that can be identified with the help of Motor Industry is one of the well-known and background information. The churn of a leading industries that make use of data customer is defined as the process of ceasing mining techniques [1]. It is mainly on the the customer connection with the company fact that a vast amount of data with incessant [8]. In the context of an online business increased rate of customers relies on the environment, customers turn out to be insurance companies. It poses a challenging churned, when the customer communication scenario for the researchers as well as with the service time has elapsed. This could industrialists. Since the customers have the bring revenue lost and the deprived ability to switch over among the available marketing costs due to the improper providers, the prediction of the churn rate in customer communication. Therefore, the motor insurance industry is not customer churn should be minimized which performing well in the aspects of customer’s is the ultimate aim for all online business requirements. Therefore, it is addressed in environments [9]. The main challenges in terms of churn prediction using machine the customer churn prediction systems are: i) learning techniques [2-4]. The insurance do not associate with the business objective industry is extensive and changes rapidly and ii) relying on feedback loops. These are according to the global market trends. the two reasons that have increased the Developing a trustable and reliable count of churners in the business Customer Relationship Management (CRM) environment. Customers who are willing to [5-7] in the motor industry is an interesting churn should be excluded from the study area which stimulates to deal with the campaign, so as to preserve the resources. real-time challenges. The prediction of churn in the motor insurance industry is a The rest of the paper is organized as significant research area for the fault follows: Section II presents the Related recognition and classification tasks. The rise work; Section III presents the Research of churners in several businesses are due to Methodology; Section IV presents the 92 ISSN (Print): 2204-0595 Copyright © Authors ISSN (Online): 2203-1731 IT in Industry, Vol. 9, No.1, 2021 Published Online 18-Feb-2021 Experimental Results and Analysis and the key issues and the future directions of Section V presents the Conclusion. customer retention management. The role of customer retention was to increase the firm 2. Related work value by deducing the rate of subscription This section presents the reviews of existing based firms. By estimating the social- machine learning techniques designed for connectivity factors, the churns were churn predictions. The author in [10] has predicted. Campaign management was the discussed the market segmentation using most composed strategy that introduced the hierarchical self-organizing maps. It was win-back scenario on all customer retention. explored in a real-time dataset of market All the data were leveraged based on the segmentation in Taiwan. The classification retention values. expected error and the associated input Intelligent data analysis and their vectors were organized in a hierarchical way. approaches were studied to resolve the This helps to build a customer map that business issues [13]. Due to the effectively brings out the stage of customers enhancement in the globalization process, an under the churning stage. However, the in-depth analysis of churn in the sense of clustering process develops a class customer continuity management. It has imbalance issues over the multi-subscription stated the use and the merits of the customer data. Detection of potential prediction models with the business churners at the earlier stage is the solution. Some recommendations were complicating task due to the invasion of new given according to the scope of industry. subscription services [11]. It was explored The study has concluded that the scope of on the European pay TV company where a deep learning and ensemble learning might high rate of churners were observed. With even predict faster than the machine learning the help of markov chain concepts, the techniques. In [14], the author has reasons behind the customer attrition were introduced a random forest algorithm along found. These were then fed into RF with the use of regression analysis, in order classifiers, so as to effectively classify the to predict the profitability and retention of churners. Depending on the contact the customers. It was explored on the 100, variables, the technical problems were 000 customers taken from the warehouse of resolved. The author in [12] has discussed 93 ISSN (Print): 2204-0595 Copyright © Authors ISSN (Online): 2203-1731 IT in Industry, Vol. 9, No.1, 2021 Published Online 18-Feb-2021 European financial services company. The simulated annealing process, the rate of false RF algorithm was applied on two aspects, positivity and the choice of features one is to classify the binary data and second complexity were significantly reduced. is to leverage the linear dependent variables. Though it has provided an optimal strategy Compared to the logistic regression models, to predict the churn, yet the time complexity the behavior analysis of the customers was and termination criteria are high which done by exploring the buying variables and brings a heavy computational effort. In [17], the profitability variables. Though it has a random rough subspace based neural suggested to increase the profitability, ensemble learning process was developed to however, the multi-objective based behavior improve the performance of the neural patterns can also be focussed. The author in network classifiers. It was combined with [15] has suggested the scope of random the set of feature reductions process, so as to forest algorithms in order to enhance the increase the effectiveness and the efficiency uplift modeling on customer retention cases. of the ensemble strategies. The data Here, a customer retention model was proportion of the classifier misleads the data designed to the customers who were actively relationship. One-class SVM with engaged. By the use of a random forest undersampling data [18] was studied to algorithm, the customer attrition rate was improve the churn prediction along with leveraged. Strong empirical evidence was insurance fraud. It is analyzed on the suggested to retain the customers for long- Automobile Insurance fraud dataset and term. Though it has easily predicted the Credit card customer churn dataset by retentional customers, the effects of attrition significantly improving the AUC that leads to customer retention are not performance. Compared to other ML recommended. techniques, the OC-SVM has predicted the churners with the least computational steps. Biological inspired approaches were However, the class imbalance and false introduced to predict the churn, so as to positive rate are higher, when the sensitivity yield the optimal performance. PSO and its learning values increases. variants [16] were introduced to effectively preprocess the data. With the help of In the context of B2B e-commerce optimized feature selection process and the companies, the SVM [19] was employed to 94 ISSN (Print): 2204-0595 Copyright © Authors ISSN (Online): 2203-1731 IT in Industry, Vol. 9, No.1, 2021 Published
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