Sales strategies with a probabilistic business model of an insurance company: The role of updating and targeting

Krisztián Koppány ,István Á. Harmati ,Dávid Fülep ,Norbert Kovács

doi:10.24294/jipd8803

Home - JIPD - Volume 8 - Issue 16

Submit to JIPD

Journal Browser

Volume | Year

Issue

• Current lssue

View All

News and Announcements

View All

Sales strategies with a probabilistic business model of an insurance company: The role of updating and targeting

Krisztián Koppány

István Á. Harmati

Dávid Fülep

Norbert Kovács

Journal of Infrastructure Policy and Development 2024, 8(16), 8803; https://doi.org/10.24294/jipd8803

Submitted：26 Aug 2024

Accepted：26 Aug 2024

Published：18 Dec 2024

Download PDF(634.29KB)

XML

Abstract

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

Keywords

References

Abousaber, I., & Abdalla, H. (2023). Review of Using Technologies of Artificial Intelligence in Companies. International Journal of Communication Networks and Information Security (IJCNIS). https://doi.org/10.17762/ijcnis.v15i1.5743.

An, W., Wang, S., Chen, Y., Wu, Q., Wu, C., Wang, K., Wang, S., Chen, H., & Chen, Z. (2022). Interaction design of financial insurance products under the Era of AIoT. Applied Mathematics and Nonlinear Sciences, 7, 745 - 756. https://doi.org/10.2478/amns.2021.2.00162.

Area, D. (2023). Impact of AI in the General Insurance underwriting factors. Central European Management Journal. https://doi.org/10.57030/23364890.cemj.31.2.72.

Bai, Y., Xing, Y., Wu, J. (2022). Integrating knowledge graph, complex network and Bayesian network for data-driven risk assessment. Chemical Engineering Transactions, 90, 31-36.

Biswas, B., Sanyal, M., & Mukherjee, T. (2023). AI-Based Sales Forecasting Model for Digital Marketing. Int. J. E Bus. Res., 19, 1-14. https://doi.org/10.4018/ijebr.317888.

Blyth, C. R. (1972). On Simpson’s Paradox and the Sure-Thing Principle. Journal of the American Statistical Association. 67 (338): 364–366. doi:10.2307/2284382. JSTOR 2284382.

Chang, P., Liu, C., & Lai, R. (2008). A fuzzy case-based reasoning model for sales forecasting in print circuit board industries. Expert Syst. Appl., 34, 2049-2058. https://doi.org/10.1016/j.eswa.2007.02.011.

Chen, K., & Chiu, H. (2020). Applying AI Techniques to Predict the Success of Bank Telemarketing. Proceedings of the 2020 4th International Conference on Deep Learning Technologies. https://doi.org/10.1145/3417188.3417198.

Cho, K. (2022). A Study on new Insurance Distribution Channel’s Right to Receive the Duty of Disclosure and Legal Issues: Focusing on AI (Artificial Intelligence) Insurance Solicitors and Insurance Companies Specializing in Insurance Product Sales. Korean Insurance Law Association. https://doi.org/10.36248/kdps.2022.16.2.003.

Chu, J., Than, J., Thon, P., & Jo, H. (2023). Investigation on Insurance Purchase Classification for Insurance Recommendation Using Deep Learning and Class Propagation. 2023 IEEE 8th International Conference on Software Engineering and Computer Systems (ICSECS), 1-6. https://doi.org/10.1109/ICSECS58457.2023.10256401.

Côté, M., Genest, C., & Stephens, D. (2020). A Bayesian Approach to Modeling Multivariate Multilevel Insurance Claims in the Presence of Unsettled Claims. Bayesian Analysis. https://doi.org/10.1214/20-ba1243.

Durante, D., Paganin, S., Scarpa, B., & Dunson, D. (2015). Bayesian modelling of networks in complex business intelligence problems. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66. https://doi.org/10.1111/rssc.12168.

Eckert, C., Osterrieder, K. How digitalization affects insurance companies: overview and use cases of digital technologies. ZVersWiss 109, 333–360 (2020). https://doi.org/10.1007/s12297-020-00475-9

Eglite, L., & Birzniece, I. (2022). Retail Sales Forecasting Using Deep Learning: Systematic Literature Review. Complex Syst. Informatics Model. Q., 30, 53-62. https://doi.org/10.7250/csimq.2022-30.03.

Filabi, A., & Duffy, S. (2021). AI-Enabled Underwriting Brings New Challenges for Life Insurance: Policy and Regulatory Considerations. Journal of Insurance Regulation. https://doi.org/10.52227/25114.2021.

Fung, G., Polanía, L., Choi, S., Wu, V., & Ma, L. (2021). Editorial: Artificial Intelligence in Insurance and Finance, Front. Appl. Math. Stat., Sec. Mathematical Finance Volume 7 - 2021 https://doi.org/10.3389/fams.2021.795207.

Giovanis, D., Papaioannou, I., Štraub, D., & Papadopoulos, V. (2017). Bayesian updating with subset simulation using artificial neural networks. Computer Methods in Applied Mechanics and Engineering, 319, 124-145. https://doi.org/10.1016/J.CMA.2017.02.025.

Good, I. J., Mittal, Y. (1987). The Amalgamation and Geometry of Two-by-Two Contingency Tables. The Annals of Statistics. 15 (2): 694–711. doi:10.1214/aos/1176350369. ISSN 0090-5364. JSTOR 2241334.

Hanafy, M., & Ming, R. (2021). Machine Learning Approaches for Auto Insurance Big Data. Risks. https://doi.org/10.3390/RISKS9020042.

Helmini, S., Jihan, N., Jayasinghe, M., & Perera, S. (2019). Sales forecasting using multivariate long short term memory network models. PeerJ Prepr., 7, e27712. https://doi.org/10.7287/peerj.preprints.27712v1.

Heskes, T., Spanjers, J., Bakker, B., & Wiegerinck, W. (2003). Optimising newspaper sales using neural-Bayesian technology. Neural Computing & Applications, 12, 212-219. https://doi.org/10.1007/s00521-003-0384-x.

Holland, C. (2022). Artificial Intelligence (AI) and Digital Transformation in the Insurance Market: A Case Study Analysis of BGL Group. Proceedings of the 55th Hawaii International Conference on System Sciences, 1-10. https://doi.org/10.24251/hicss.2022.553.

Hong, L., & Martin, R. (2016a). A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims. North American Actuarial Journal, 21, 228 - 241. https://doi.org/10.1080/10920277.2016.1247720.

Hong, L., & Martin, R. (2016b). A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims. ERN: Bayesian Analysis (Topic). https://doi.org/10.2139/ssrn.2690843.

Hong, L., & Martin, R. (2016c). A review of Bayesian asymptotics in general insurance applications. European Actuarial Journal, 7, 231-255. https://doi.org/10.2139/ssrn.2741074.

Hong, L., & Martin, R. (2018). Real-time Bayesian non-parametric prediction of solvency risk. Annals of Actuarial Science, 13, 67 - 79. https://doi.org/10.1017/S1748499518000039.

Huang, Y., & Meng, S. (2020). A Bayesian nonparametric model and its application in insurance loss prediction. Insurance Mathematics & Economics, 93, 84-94. https://doi.org/10.1016/j.insmatheco.2020.04.010.

Hungarian Central Statistical Office (HCSO) (2024). Dissemination database. Population and vital events. https://statinfo.ksh.hu/ accessed 01.03.2024.

Insua, S., Martín, J., Insua, D., & Ruggeri, F. (1999). Bayesian Forecasting for Accident Proneness Evaluation. Scandinavian Actuarial Journal, 1999, 134-156. https://doi.org/10.1080/03461239950132624.

Kaneko, Y., & Yada, K. (2016). A Deep Learning Approach for the Prediction of Retail Store Sales. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 531-537. https://doi.org/10.1109/ICDMW.2016.0082.

Kaushal, R., Giri, K., Raina, K., Choudhary, D., Naikade, K., & Kartikeya, M. (2023). AI-Based Approach for Retail Sale Forecasting. 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 111-116. https://doi.org/10.1109/ICSSAS57918.2023.10331651.

Lin, X., & Ruan, W. (2023). Research on the Marketing Transformation of Insurance Industry Under Generative Artificial Intelligence Technology. Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, September 1–3, 2023, Chongqing, China. https://doi.org/10.4108/eai.1-9-2023.2338781.

Liu, D., Wu, J., Liang, L., Li, X., Luo, C., & Xu, G. (2019). An incremental Bayesian network structure learning algorithm based on local updating strategy. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 1, 673-677. https://doi.org/10.1109/IAEAC47372.2019.8997724.

Loske, D., & Klumpp, M. (2021). Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics. International Journal of Production Economics, 241, 108236. https://doi.org/10.1016/J.IJPE.2021.108236.

Loureiro, A., Miguéis, V., & Silva, L. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decis. Support Syst., 114, 81-93. https://doi.org/10.1016/j.dss.2018.08.010.

Lupačov, J., & Stankovic, Z. (2022). Artificial intelligence in insurance companies. JTTTP - journal of traffic and transport theory and practice. https://doi.org/10.7251/jtttp2202077l.

Ma, S., & Fildes, R. (2021). Retail sales forecasting with meta-learning. Eur. J. Oper. Res., 288, 111-128. https://doi.org/10.1016/j.ejor.2020.05.038.

Maier, M., Carlotto, H., Saperstein, S., Sanchez, F., Balogun, S., & Merritt, S. (2020). Improving the Accuracy and Transparency of Underwriting with AI to Transform the Life Insurance Industry. AI Mag., 41, 78-93. https://doi.org/10.1609/aimag.v41i3.5320.

Makov, U., Smith, A., & Liu, Y. (1996). Bayesian methods in actuarial science. The Statistician, 45, 503-515. https://doi.org/10.2307/2988548.

Massaro, A., Panarese, A., Gargaro, M., Vitale, C., & Galiano, A. (2021). Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities. International Journal of Artificial Intelligence & Applications, 12, 01-13. https://doi.org/10.5121/IJAIA.2021.12301.

Messaoudi, F., Loukili, M., & Ghazi, M. (2023). Demand Prediction Using Sequential Deep Learning Model. 2023 International Conference on Information Technology (ICIT), 577-582. https://doi.org/10.1109/ICIT58056.2023.10225930.

Mosallam, A., Medjaher, K., Zerhouni, N. (2013). Bayesian approach for remaining useful life prediction. Chemical Engineering Transactions, 33, 139-144.

Mullins, M., Holland, C., & Cunneen, M. (2021). Creating ethics guidelines for artificial intelligence and big data analytics customers: The case of the consumer European insurance market. Patterns, Volume 2, ISSUE 10. https://doi.org/10.1016/j.patter.2021.100362.

National Regional Development and Spatial Planning Information System (TeIR) (2024). Population (31 December). https://www.oeny.hu/oeny/teir/#/tablo/5 accessed 01.03.2024.

Pavlyshenko, B. (2022). Forecasting of Non-Stationary Sales Time Series Using Deep Learning. ArXiv, abs/2205.11636. https://doi.org/10.48550/arXiv.2205.11636.

Qadadeh W., Abdallah S. (2018). Customers segmentation in the insurance company (TIC) dataset. Procedia computer science, 144, 277-290. https://doi.org/10.1016/j.procs.2018.10.529

Qazi, M., Fung, G., Meissner, K., & Fontes, E. (2017). An Insurance Recommendation System Using Bayesian Networks. Proceedings of the Eleventh ACM Conference on Recommender Systems. https://doi.org/10.1145/3109859.3109907.

Ravi H., Vedapradha R. (2023). Artificial intelligence service agents: a silver lining in rural India. Kybernetes. https://doi.org/10.1108/k-09-2022-1239.

Simpson, E. H. (1951). The Interpretation of Interaction in Contingency Tables. Journal of the Royal Statistical Society, Series B. 13 (2): 238–241. doi:10.1111/j.2517-6161.1951.tb00088.x.

Singh, S. (2020). A Commentary on the Application of Artificial Intelligence in the Insurance Industry. Trends in Artificial Intelligence. Volume 4 Issue 1 https://doi.org/10.36959/643/305.

Śmietanka, M., Koshiyama, A., & Treleaven, P. (2020). Algorithms in future insurance markets. International Journal of Data Science and Big Data Analytics. https://doi.org/10.2139/ssrn.3641518.

Smuha, N. (2021). From a ‘race to AI’ to a ‘race to AI regulation’: regulatory competition for artificial intelligence. Law, Innovation and Technology, 13, 57 - 84. https://doi.org/10.1080/17579961.2021.1898300.

Stahl, B., Schroeder, D., & Rodrigues, R. (2023). Ethics of Artificial Intelligence. Case Studies and Options for Addressing Ethical Challenges. SpringerBriefs in Research and Innovation Governance. https://doi.org/10.1007/978-3-031-17040-9.

Vairo, T., Bragatto, P., Milazzo, M. F., Pettinato, M., & Fabiano, B. (2022). DYN-RISK–Design and Development of a Dynamic Risk Assessment Tool. Chemical Engineering Transactions, 90, 325-330.

Velmurugan, K., Pazhanivel, K. Divyasree, R., Gowtham, E., & Guruharan, S. (2023). Data Driven Analysis of Insurance Claims Using Machine Learning Algorithm. International Journal of Advanced Research in Science, Communication and Technology. Vol. 3, Issue 1, May 2023 https://doi.org/10.48175/ijarsct-9689.

Vij A., Preethi N. (2021). Approaches Towards a Recommendation Engine for Life Insurance Products. 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC), Tumkur, Karnataka, India, 2021, pp. 1-5, doi: 10.1109/ICMNWC52512.2021.9688436.

Wang, H. (2020). An Insurance Sales Prediction Model Based on Deep Learning. Rev. d’Intelligence Artif., 34, 315-321. https://doi.org/10.18280/ria.340309.

Wang, Z., & Shafieezadeh, A. (2020). Highly efficient Bayesian updating using metamodels: An adaptive Kriging-based approach. Structural Safety, 84, 101915. https://doi.org/10.1016/j.strusafe.2019.101915.

Wolf, C. (2020). AI Ethics and Customer Care: Some Considerations from the Case of “Intelligent Sales”. Proceedings of 18th European Conference on Computer-Supported Cooperative Work. https://doi.org/10.18420/ecscw2020_n02.

Yin, X., & Tao, X. (2021). Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning. Sci. Program., 2021, 2179692:1-2179692:9. https://doi.org/10.1155/2021/2179692.

Zarifis, A., Holland, C., & Milne, A. (2019). Evaluating the impact of AI on insurance: The four emerging AI- and data-driven business models. Emerald Open Research. Volume 1 Issue 1 https://doi.org/10.35241/emeraldopenres.13249.1.

Zhang, Y. (2017). Bayesian Analysis of Big Data in Insurance Predictive Modeling Using Distributed Computing. ERN: Bayesian Analysis (Topic). https://doi.org/10.1017/ASB.2017.15

Previous
article in this issue

Next
article in this issue