Future of Work and Digital Management Journal 3092-720X 3 Serial Number 7 2025 03 30 Designing a Comprehensive and Integrated Competitive Intelligence Model Using Machine Learning for Predicting Competitors’ Behavior in the Insurance Industry Designing a Comprehensive and Integrated Competitive Intelligence Model Using Machine Learning for Predicting Competitors’ Behavior in the Insurance Industry 1 17 EN Journal Article 2024 10 10 <table> <tbody> <tr> <td> <p>In the face of a dynamic, complex, and uncertainty-filled competitive environment—particularly with economic, technological, and regulatory uncertainties—in dynamic industries such as the insurance sector, organizations require tools that go beyond traditional analysis and decision-making methods to ensure survival and growth. The present study aims to design a comprehensive competitive intelligence model with a machine learning approach in Iran’s insurance industry, specifically focusing on Parsian Insurance Company. The model seeks to predict competitors’ strategic behavior and provide strategic recommendations, striving to shift the organization from a reactive to a proactive position. The proposed model is designed as a systemic framework comprising five main subsystems: (1) data collection and preprocessing, (2) modeling and predicting competitors’ pricing behavior using multilayer perceptron (MLP) neural networks, (3) a scenario knowledge base utilizing cross-impact analysis and K-Modes clustering, (4) identification of the current state and alignment with environmental scenarios through the K-NN algorithm, and (5) provision of strategic recommendations based on prescriptive artificial intelligence principles. The research findings indicated that the designed model, with a very high accuracy in predicting competitors’ pricing behavior (correlation coefficient exceeding 0.99), and the ability to integrate prediction outputs with future-oriented scenarios, provides an effective platform for strategic decision-making under uncertainty. Moreover, by creating a link between data-driven analysis, scenario planning, and recommender systems, this model offers a practical framework for enhancing organizational competitive intelligence. From a theoretical perspective, the study fills existing gaps in the competitive intelligence literature by presenting an integrated systemic framework and applying advanced machine learning algorithms, providing an operational model for implementation in data-driven organizations. From a practical perspective, the proposed model can be used as an intelligent decision support tool in the insurance industry and other competitive industries.</p> </td> </tr> </tbody> </table> https://www.journalfwdmj.com/index.php/fwdmj/article/download/84/70