Interaction algorithms between automobile service centers and the insurance companies during car repair
https://doi.org/10.26518/2071-7296-2026-23-2-210-223
EDN: FTJOFG
Abstract
Introduction. In the field of auto insurance, there is a significant problem such as the long downtime of cars undergoing repairs because of the delays while approving the volume and cost of repairs between the automobile service centers and the insurance company. Optimizing the algorithm of their interaction will reduce time wastes and increase the efficiency of the process. The purpose of this work is to develop and substantiate an improved algorithm for the interaction of the service station and the insurance company during the repair of cars damaged as a result of an accident, in order to minimize vehicle downtime.
Methods and materials. The paper analyzes the existing algorithm of interaction between the automobile service centers and the insurance company in the framework of auto insurance regulation. The time wastes for unproductive operations (review and approval of repair costs) have been established. A comparative analysis of the current and proposed algorithm for concluding an agreement about volume and cost of repair has been carried out. The description of the object and subject of the study is given –the repair of a vehicle damaged as a result of an accident on car insurance contract, and the algorithms of interaction between the automobile service centers and the Insurer.
Results. The current algorithm of interaction between the automobile service centers and the Insurer has been described in details, and the reasons related to long approval periods have been identified. An alternative algorithm has been developed and presented, aimed at reducing the downtime of vehicles in repair. The fundamental possibility of practical implementation of the proposed algorithm has been proved. A quantitative assessment of the time spent on key approval stages within both algorithms has been conducted.
Discussion and conclusion. The proposed algorithm allows to reduce significantly the downtime of cars in repair by optimizing the coordination processes between the automobile service centers and the insurance company. Practical recommendations on the implementation of the new algorithm in the work of the insurance companies and service centers have been formulated. A promising direction for further research is to test the algorithm in practice and evaluate the economic effectiveness of its application.
About the Authors
S. A. Buraga
Tula State University
Russian Federation
Russian Federation
Buraga Sergey A. – postgraduate student, Tula State University
92, Lenin Prospect, Tula, 300012
I. E. Agureev
Tula State University
Russian Federation
Russian Federation
Agureev Igor E. – Dr. of Sci. (Engineering),Associate Professor, Director of the Scientific and Educational Center
92, Lenin Prospect, Tula, 300012
References
1. Dammer D. D. [Mathematical model of an insurance company in the form of a mass service system with an unlimited number of devices taking into account one-time insurance payments] Information technology and mathematical modeling (ITMM-2016). 2016. (In Russ.)]
2. Nikolaycheva A. M. [Trends in digitalization and automation of service station processes] Management accounting. 2021. No. 7-2.(In Russ.)]
3. Mamedov E.N. [Game-theoretic optimization of insurance methods in the field of motor transport] SCIENCE AND WORLD. 2013. (In Russ.)]
4. Nesterenko I.S. [Design of a customer zone that allows increasing demand for the services of car service stations] International Research Journal. 2022. No. 1 (115). DOI: 10.23670/IRJ.2022.115.1.112. (In Russ.)]
5. Nesterenko G.A., Nesterenko I.S., Zaloznov I.P. [Using BIM technologies to improve the efficiency of development and operation of enterprises for car maintenance and sales] International Research Journal. 2023. No. 11 (137). (In Russ.)] DOI: 10.23670/IRJ.2023.137.14
6. Zakharov N.S., Kozin E.S. [Technological design of vehicle service stations using genetic algorithms] International Journal of Advanced Studies : Transport and Information Technologies, Vol. 14, No. 2, 2024; DOI: 10.12731/2227-930X-2024-14-2-296. (In Russ.)]
7. Phi-Hung Nguyen. [Automotive Service Quality Investigation Using a Grey-DEMATEL Model] Computers, Materials & Continua. 2022, vol.73, No.3. DOI: 10.32604/cmc.2022.030745
8. Revina I. V. Trifonova E. N. [Car Service Optimization Based on Simulation] Journal of Physics: Conference Series. 1791 (2021) 012084. DOI: 10.1088/1742-6596/1791/1/012084
9. Bugrimov V., Sarbaev V. [Optimization of the system of management of stores of the car service with the help of imitation simulation] MATEC Web of Conferences 334, 01022 (2021). DOI: 10.1051/matecco-nf/202133401022. (In Russ.)]
10. Krynke M., Mazur M. [Innovative Work Order Planning with Process Optimization Using Computer Simulation in the Automotive Industry, in the Case of Repair Workshops] Periodica Polytechnica Transportation Engineering. 2024. DOI: 10.3311/pptr.23546
11. Budnikova I.K., Mardanova A.M. [Modeling of financial activities of an insurance company] Information technologies in construction, social and economic systems. 2020. No. 1. (In Russ.)]
12. Burkaltseva D.D., Idrisova E.R. [ADVANTAGES AND DISADVANTAGES OF CAR INSURANCE IN
13. RUSSIA] Scientific Bulletin Finance, Banks, Investments. 2015. № 4 (33). (in Russ.)
14. Kachalov V.A. Chto zhe eto takoe «autsorsing»? [What is «outsourcing»?]. Zhurnal «Metodi menedzhmenta kachestva», 2008, № 4-5 [The journal «Methods of quality Management»], 2008, No. 4-5 (in Russ.)
15. Ermilina D.A. Business process modeling as a factor in improving the effeciency of an organization. Economic Systems. 2025; 18(1):107-114. (in Russ.) DOI: 10.29030/2309-2076-2025-18-1-107-114
16. Samigullina A.F., Smirnova A.I. Analiz rossiiskogo rinka avtomatizirovannikh informatsionnikh sistem v oblasti strakhovoi deyatelnosti [analysis of the russian market of automated information systems in the field of insurance activities]. Materiali VII Mezhdunarodnoi studencheskoi nauchnoi konferentsii «Studencheskii nauchnii forum» [Proceedings of the VII International Student Scientific Conference “Student Scientific Forum”] Available at: https://scienceforum.ru/2015/article/2015009941”>https://scienceforum.ru/2015/article/2015009941 (Accessed 10 august 2025) (in Russ.)
17. Hosein P.A [Data-Driven Pricing Strategy for Automobile Insurance Policies] 2022 5th Asia Conference on Machine Learning and Computing (ACMLC). DOI: 10.1109/ACMLC58173.2022.00009
18. Xie Sh. [Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation] Mathematics, 10.3390/math12193150, 12, 19, (3150). (2024) DOI: 10.3390/math12193150
19. Henckaerts R., Antonio K. [The added value of dynamically updating motor insurance prices with telematics collected driving behavior data] Insurance: Mathematics and Economics, 105, (79-95), (2022) DOI:10.1016/j.insmatheco.2022.03.011
20. Masello L., Sheehan B., Castignani G., Guillen M., Murphy F. [Predictive Modeling for Driver Insurance Premium Calculation Using Advanced Driver Assistance Systems and Contextual Information] IEEE Transactions on Intelligent Transportation Systems, 26, 2, (2202-2211), (2025) DOI: 10.1109/TITS.2024.3518572
21. Kushelev I.Yu. [Implementation of innovative information technologies in the insurance market in Russia: telematics in car insurance] Entrepreneur’s Guide. 2023. Vol. 16. No. 2. (In Russ.)] DOI: 10.24182/2073-9885-2023-16-2-110-119
22. Lang F., Riegel L. [Acceptance of online customer channels for damage claims in Germany] Information Technology and Management. 10.1007/s10799-023-00404-z26:1(101-116) Online publication date: 1-Mar-2025. DOI: 10.1007/s10799-023-00404-z
23. McDonnell K., Murphy F., Sheehan B., Masello L., Castignani G. [Deep learning in insurance] Expert Systems with Applications: An International Journal. 10.1016/j.eswa.2023.119543217:C. Online publication date: 1-May-2023. DOI: 10.1016/j.eswa.2023.119543
24. Li H., Luo X., Zhang Z., Jiang W., Huang S. [Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data] Decision Support Systems. 10.1016/j. dss.2023.113985172:C. Online publication date: 1-Sep-2023 DOI: 10.1016/j.dss.2023.113985.
25. Brühwiler L., Fu Ch., Huang H., Longhi L., Weibel R. [Predicting individuals’ car accident risk by trajectory, driving events, and geographical context] Computers, Environment and Urban Systems. Volume 93, April 2022 –DOI:10.1016/j.compenvurb-sys.2022.101760
26. Cunha L., Bravo J. M. [Automobile Usage-Based-Insurance: Improving Risk Management using Telematics Data] 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), 10.23919/CISTI54924.2022.9820146, (1-6), (2022) DOI: 10.23919/CISTI54924.2022.9820146.
27. Ortega M., Quintanilla J., Ong E.R., Ramos M.R., Trinidad C.J. [Asfalis: A Web-based System for Customer Retention Strategies Optimization of a Car Insurance Company Using Cohort and Churn Analysis] 2023 International Conference on Inventive Computation Technologies (ICICT). DOI: 10.1109/ICICT57646.2023.10134149
28. Manko B.A. [Erie Insurance: Monitoring technology in the car insurance market and the issue of data privacy] Journal of Information Technology Teaching Cases, 10.1177/20438869221117571, 13, 2, (193-198), (2022) DOI:10.1177/20438869221117571
29. Agureev I.Е., Buraga S.A. Methods of the theory of transport macrosystems for mass service dynamics and auto insurance. The Russian Automobile and Highway Industry Journal. 2025;22(5):760-771. (In Russ.) DOI: 10.26518/2071-7296-2025-22-5-760-771. EDN: PBYTVD
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For citations:
Buraga S.A., Agureev I.E. Interaction algorithms between automobile service centers and the insurance companies during car repair. The Russian Automobile and Highway Industry Journal. 2026;23(2):210-223. (In Russ.) https://doi.org/10.26518/2071-7296-2026-23-2-210-223. EDN: FTJOFG
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