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<p>Government regulations and rules on the insurance industry have made tremendous advances, with many consumers and many new companies entering the sector. Consumer behaviour is changing rapidly concerning life insurance products. Hence, this paper proposes a Deep Learning-based Integrated Customer Behavior Predictive Model (DLICBPM) for monitoring customer behaviour in the insurance industry. This paper examines consumers’ reactions to pay transparency and how consumers deal with the wrong results. The data is analyzed using percentages and cross-table analysis. The study examines the type of policy the respondent preferred based on insurance company policies that have attracted them and the degree of satisfaction in the system that helps them decide their psychological state of mind. Results will enhance the theory of decision-making in a given segment. In practice, they are used in cooperative institutions to understand the customer’s state of mind and optimize the product. They help reduce the norms in prevention contracts and permanent expansion of customers.</p>

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Deep learning for monitoring customer behavior in insurance industry

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • Karthikeyan Parthasarathy,
  • Rajeswaran Ayyadurai,
  • Naresh Kumar Reddy Panga,
  • Jyothi Bobba,
  • Ramya Lakshmi Bolla,
  • Roseline Oluwaseun Ogundokun

摘要

Government regulations and rules on the insurance industry have made tremendous advances, with many consumers and many new companies entering the sector. Consumer behaviour is changing rapidly concerning life insurance products. Hence, this paper proposes a Deep Learning-based Integrated Customer Behavior Predictive Model (DLICBPM) for monitoring customer behaviour in the insurance industry. This paper examines consumers’ reactions to pay transparency and how consumers deal with the wrong results. The data is analyzed using percentages and cross-table analysis. The study examines the type of policy the respondent preferred based on insurance company policies that have attracted them and the degree of satisfaction in the system that helps them decide their psychological state of mind. Results will enhance the theory of decision-making in a given segment. In practice, they are used in cooperative institutions to understand the customer’s state of mind and optimize the product. They help reduce the norms in prevention contracts and permanent expansion of customers.