Designing a Comprehensive and Integrated Competitive Intelligence Model Using Machine Learning for Predicting Competitors’ Behavior in the Insurance Industry
Keywords:
Competitive intelligence, machine learning application, competitor behavior prediction, insurance industryAbstract
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.
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[1] A. I. Daraojimba, "Business analytics and decision science: A review of techniques in strategic business decision making," World Journal of Advanced Research and Reviews, vol. 21, no. 2, pp. 1-9, 2024, doi: 10.30574/wjarr.2024.21.2.0247.
[2] A. Sharma, "Role of Machine Learning in Business Enterprises," International Journal of Computer Science and Mobile Computing, vol. 14, no. 2, 2025, doi: 10.47760/ijcsmc.2025.v14i02.007.
[3] L. K. Muriira. "Competitive strategies adopted by insurance companies in Kenya." https://www.semanticscholar.org/paper/40adfc54a344e067048c25fb64138710cb54f0c8 (accessed.
[4] Y. S. Reshi and R. Khan, "Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool," Information and Knowledge Management, vol. 4, pp. 65-75, 2014.
[5] A. Osman, O. Fowowe, R. Agboluaje, and P. Orekha, "Integrating machine learning in business analytics consulting for proactive decision- making and innovation," World Journal of Advanced Research and Reviews, vol. 25, pp. 1817-1836, 2025, doi: 10.30574/wjarr.2025.25.1.0251.
[6] P. Akter, "Sentiment Analysis of Consumer Feedback and Its Impact on Business Strategies by Machine Learning," The American Journal of Applied Sciences, vol. 7, no. 1, pp. 1-2, 2025, doi: 10.37547/tajas/Volume07Issue01-02.
[7] K. Jones and S. Sah, "The Implementation of Machine Learning In The Insurance Industry With Big Data Analytics," International Journal of Data Informatics and Intelligent Computing, vol. 2, pp. 21-38, 2023, doi: 10.59461/ijdiic.v2i2.47.
[8] B. Kisuya, A. Kihara, and J. Macheru, "Innovation Strategies and Competitive Advantage of Insurance Firms in Kenya," Journal of Business and Strategic Management, vol. 8, pp. 1-26, 2023, doi: 10.47941/jbsm.1201.
[9] D. Collaris and J. v. Wijk, "StrategyAtlas: Strategy Analysis for Machine Learning Interpretability," IEEE Transactions on Visualization and Computer Graphics, vol. 29, pp. 2996-3008, 2022, doi: 10.1109/TVCG.2022.3146806.
[10] H. Nan and M. Hu, "Corporate Marketing Strategy Analysis with Machine Learning Algorithms," Wireless Communications and Mobile Computing, 2022, doi: 10.1155/2022/9450020.
[11] H. Bakameiri, M. Lagziyan, A. Pouya, and H. Sharif, "Foresight of the banking industry using the scenario-writing approach and cross-impact matrix," Smart Business Management Studies, vol. 10, no. 37, pp. 233-266, 2021, doi: 10.22054/ims.2020.49555.1664.
[12] L. Kaufman and P. Rousseeuw, Finding Groups in Data: An Introduction To Cluster Analysis. 1990.
[13] L.-M. Catalina, F. P. Romero, J. Serrano-Guerrero, A. Peralta, and J. A. Olivas, "Potential Applications of Explainable Artificial Intelligence to Actuarial Problems," Mathematics, vol. 12, no. 5, pp. 1-13, February 2024, doi: 10.3390/math12050635.
[14] A. A. Patil and N. R. Wankhade, "Telecom Churn Prediction Using Machine Learning," International Journal of Advanced Research in Science, Communication and Technology, 2023, doi: 10.48175/IJARSCT-13886.
[15] P. Shi, W. Zhang, and K. Shi, "Leveraging Weather Dynamics in Insurance Claims Triage Using Deep Learning," Journal of the American Statistical Association, vol. 119, no. 546, pp. 825-838, 2024, doi: 10.1080/01621459.2024.2308314.
[16] Z. Shasha, "Analysis of Strategies to Enhance the Competitiveness of Financial Insurance Companies," Financial Engineering and Risk Management, 2023, doi: 10.23977/ferm.2023.060506.
[17] Y. S. Kamdem, "Personalized Data Insights: How Machine Learning is Revolutionizing Everyday Business Decisions," International Journal of Scientific Research and Management (IJSRM), vol. 12, no. 10, 2024, doi: 10.18535/ijsrm/v12i10.em18.
[18] K. Kaushik, A. Bhardwaj, A. D. Dwivedi, and R. Singh, "Machine Learning-Based Regression Framework to Predict Health Insurance Premiums," International Journal of Environmental Research and Public Health, vol. 19, no. 13, p. 7898, 2022, doi: 10.3390/ijerph19137898.
[19] Z. Quan, Z. Wang, G. Gan, and E. Valdez, "On hybrid tree-based methods for short-term insurance claims," Probability in the Engineering and Informational Sciences, vol. 37, pp. 1-24, 2023, doi: 10.1017/S0269964823000074.
[20] P. Wanke and C. P. Barros, "Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach," Economic Modelling, vol. 53, pp. 8-22, 2016, doi: 10.1016/j.econmod.2015.11.005.
[21] N. Natasha, D. Indrawan, and I. T. Saptono, "FORMULATION OF STRATEGY FOR COMPETITIVENESS IMPROVEMENT OF GENERAL INSURANCE COMPANY IN INDONESIA," Russian Journal of Agricultural and Socio-Economic Sciences, vol. 120, pp. 75-83, 2021, doi: 10.18551/rjoas.2021-12.08.
[22] H. Alizadeh, M. Khorramabadi, H. Saberian, and M. Keramati, "Qualitative Study to Propose Digital Marketing based on Customer experience: Considering Grounded theory (GT)," Business, Marketing, and Finance Open, vol. 1, no. 6, pp. 86-98, 2024, doi: 10.61838/bmfopen.1.6.8.
[23] S. Bai and Y. Zhao, "Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches," Syst., vol. 9, p. 55, 2021, doi: 10.3390/SYSTEMS9030055.
[24] B. Dong, "Performance Prediction of Listed Companies in Smart Healthcare Industry: Based on Machine Learning Algorithms," Journal of Healthcare Engineering, vol. 2022, 2022, doi: 10.1155/2022/8091383.
[25] H. Maleki Kakler, J. Bahri Sales, S. Jabbarzadeh Kangarlooee, and A. Ashtab, "The efficiency of statistical and machine learning models in predicting fraudulent financial reporting," Financial Economics (Financial Economics and Development), 2021.
[26] J. Zhang, "Exploring the Transformative Role of Machine Learning in Predictive Marketing: Enhancing Customer Targeting and Addressing Privacy Challenges," 2024.
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Copyright (c) 2025 Vahid Khashei Varnamkhasti, Mahdi Ebrahimi, Rahim Zare , Farzaneh Kashani (Author)
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