Machine Learning in Legal and Economic Systems: Enhancing Institutional Efficiency and Judicial Predictability
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Yevgeniy SHULGIN Karaganda Academy of the Ministry of Internal Affairs of the Republic of Kazakhstan, Kazakhstan
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Yergali MABIYEV Institute of Philosophy and Law of the National Academy of Sciences, Kyrgyz Republic
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Ruslan KENZHEBEKOV Institute of Philosophy and Law of the National Academy of Sciences, Kyrgyz Republic
Machine learning (ML) offers powerful capabilities for large-scale data processing, predictive modelling, and decision support in complex legal and economic systems. This study investigates the application of ML techniques in judicial and economic governance, focusing on their role in optimizing institutional performance, improving case-flow management, and strengthening judicial predictability.
Using judicial case data from Kazakhstan (2014–2024), during which investigative and security services nearly quadrupled, supervised and unsupervised learning algorithms are employed to model caseload dynamics, forecast procedural duration, and assess efficiency-enhancing scenarios. The results indicate that ML-based interventions can reduce case processing time by 5%–15% and increase judicial throughput by 5%–18%, significantly improving predictive accuracy, resource allocation, and operational planning. These findings highlight the potential of ML to enhance institutional efficiency, reduce procedural uncertainty, and support sustainable judicial modernisation.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s History: Received 30th of December, 2025; Revised 31st of January, 2025; Accepted 2nd of March, 2025; Available online: 30th of March, 2026. Published as article in the Volume XXI, Spring, Issue 2(92), March, 2026.
Shulgin, Ye., Omarova, A., Mabiyev, Ye., Kenzhebekov, R., Kussainova, L., & Isaeva, K. (2026). Machine Learning in Legal and Economic Systems: Enhancing Institutional Efficiency and Judicial Predictability. Journal of Applied Economic Sciences, Volume XXI, Spring, 2(92), 579-594. https://doi.org/10.57017/jaes.v21.2(92).11
Acknowledgement: This research has been/was/is funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan under Grant No BR24992826 “Innovative approaches to ensuring accessibility of justice to the population of the Republic of Kazakhstan, using artificial intelligence tools”.
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. It is noted that Omarova, A. the publisher manager, is author of this paper. However, this position did not influence the editorial decision-making process. The manuscript was subject to independent peer review handled by a qualified editorial team member with no competing interests.
Data Availability Statement: The data that support the findings of this study are available from the official statistics of the Republic of Kazakhstan (2015–2024) and from publicly accessible government reports on construction, R&D, and environmental indicators. Derived data and analyses generated during this study are available from the corresponding author upon reasonable request.
Ethical Approval Statement: This study is based exclusively on the analysis of aggregated secondary data obtained from official national statistical sources and publicly available institutional records. The research did not involve human participants, clinical data, personal identifiers, or experimental interventions. All datasets were fully anonymized and processed in accordance with international standards of research integrity and data protection. Therefore, ethical approval was not required for this study.
Bachurin, S., Sidorova, N., Shulgin, E., Altaybayev, S., & Kussainova, L. (2025). AI Machine learning of artificial intelligence systems with acts of justice: Forecasting and ways to solution. International Journal of Innovative Research and Scientific Studies, 8(4), 1872–1881. https://doi.org/10.53894/ijirss.v8i4.8257
Bell, J. (2022). What is machine learning? In Machine Learning and the City: Applications in Architecture and Urban Design (ed. S. Carla), 207-216. John Wiley & Sons, https://doi.org/10.1002/9781119815075.ch18
Brooks, C., Gherhes, C., & Vorley, T. (2020). Artificial intelligence in the legal sector: Pressures and challenges of transformation. Cambridge Journal of Regions, Economy and Society, 13(1), 135–152. https://doi.org/10.1093/cjres/rsz026
Laukemann, B. (2026). Alternative Dispute Resolution and Artificial Intelligence. In B. Hess,M. Woo,L. Cadiet,S. MenétreyandE. Vallines García (eds), Comparative Procedural Law and Justice (Part IX Chapter 5), cplj.org/a/9-5. https://www.cplj.org/publications/9-5-alternative-dispute-resolution-and-artificial-intelligence
Delgado, F. A. (2019). Machine learning in legal practice: Notes from recent history. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 557–558. https://doi.org/10.1145/3306618.3314324
Essoujaa, M., Esghir, M., & Azhari, M. (2023). Using Machine Learning to Predict Public Prosecution Judges Decisions in Moroccan Courts. Procedia Computer Science, 220, 998-1002. https://doi.org/10.1016/j.procs.2023.03.138
Gellert, R. (2018). Understanding the notion of risk in the General Data Protection Regulation. Computer Law & Security Review, 34(2), 279-288. https://doi.org/10.1016/j.clsr.2020.105433
Golbin, I., Rao, S., Hadjarian, A., & Krittman, D. (2020). Responsible AI: A Primer for the Legal Community, 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, pp. 2121-2126. https://doi.org/10.1109/BigData50022.2020.9377738
Greenleaf, G., Mowbray, A., & Chung, P. (2018). Building sustainable free legal advisory systems: Experiences from the history of AI & law. Computer Law & Security Review, 34(2), 314–326. https://doi.org/10.1016/j.clsr.2018.02.007
Gambelin, O. (2021). Brave: what it means to be an AI Ethicist. AI Ethics, 1, 87–91. https://doi.org/10.1007/s43681-020-00020-5
Mergel, I., Dickinson, H., Stenvall, J., & Gasco, M. (2023). Implementing AI in the public sector. Public Management Review, 1-14. https://doi.org/10.1080/14719037.2023.2231950
Milaninia, N. (2020). Biases in machine learning models and big data analytics: The international criminal and humanitarian law implications. International Review of the Red Cross, 102(913), 199–234. https://doi.org/10.1017/S1816383121000096
Mowbray, A., Chung, P., & Greenleaf, G. (2020). Utilising AI in the legal assistance sector - Testing a role for legal information institutes. Computer Law & Security Review, 38, 105407. https://doi.org/10.1016/j.clsr.2020.105407
Nicola-Gavrilă, L. & Popîrlan C. I. (2025). Knowledge Management in Virtual Organisations Using Mobile Agents. Journal of Research, Innovation and Technologies, 4(1), 53-67. https://doi.org/10.57017/jorit.v4.1(7).04
Păvăloaia, V.-D., & Necula, S.-C. (2023). Artificial Intelligence as a Disruptive Technology-A Systematic Literature Review. Electronics, 12(5), 1102. https://doi.org/10.3390/electronics12051102
Quezada-Tavárez, K., Vogiatzoglou, P., & Royer, S. (2021). Legal challenges in bringing AI evidence to the criminal courtroom. New Journal of European Criminal Law, 12(4), 531-551. https://doi.org/10.1177/20322844211057019
Razmetaeva, Y., & Razmetaev, S. (2021). Justice in the digital age: technological solutions, hidden threats and enticing opportunities. Access to Justice in Eastern Europe, 2(10), 104-117. https://doi.org/10.33327/AJEE-18-4.2-a000061
Remus, D., & Levy, F. S. (2016). Can Robots be Lawyers? Computers, Lawyers, and the Practice of Law. https://doi.org/10.2139/ssrn.2701092
Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260–278. https://doi.org/10.1080/12460125.2020.1819094
Sedkaoui, S., & Benaichouba, R. (2024). Generative AI as a transformative force for innovation: a review of opportunities, applications and challenges. European Journal of Innovation Management. https://doi.org/10.1108/EJIM-02-2024-0129
Sousa Antunes, H., Freitas, P. M., Oliveira, A.L., Martins Pereira, C., Vaz de Sequeira, E., Barreto & Xavier, L. (eds). (2024). Multidisciplinary Perspectives on Artificial Intelligence and the Law. Law, Governance and Technology Series, Volume 58. Springer, Cham. https://doi.org/10.1007/978-3-031-41264-6
Spalevic, Z., Milosavljevic, S., Dubljanin, D., Popovic, G., & Ilic, M. (2024). The role of artificial intelligence in judicial systems. International Journal of Cognitive Research in Science, Engineering and Education, 12(3), 561-569. https://doi.org/10.23947/2334-8496-2024-12-3-561-569
William, P., Shrivastava, A., Shunmuga Karpagam, N., Mohanaprakash, T. A., Tongkachok, K., & Kumar, K. (2023). Crime Analysis Using Computer Vision Approach with Machine Learning. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, Volume 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_25
Zavrsnik, A. (2021). Algorithmic justice: algorithms and big data in criminal justice settings. European Journal of Criminology, 18 (5), 623-642. https://doi.org/10.1177/1477370819876762