Innovative analytical and statistical technology as a corruption counteraction tool: conceptual analysis
DOI:
https://doi.org/10.34069/AI/2023.67.07.7Palavras-chave:
analytics, anti-corruption, statistics, integrity, quality control.Resumo
The article is devoted to conceptual analysis of the problem of innovative analytical and statistical technologies implementations as a corruption prevention tool. This study defines corruption as the unlawful use of administrative resources for personal or group benefits, violating both formal and informal norms. It is stated that “corruption counteraction” means actions to prevent, combat, and mitigate corruption in society. The paper introduces several approaches for analytical and statistical technologies classification with grouping such technologies into high, middle and low technologies. Hi-tech is applied to the most advanced technologies based on scientific and technical progress and associated with automated technology. Automated analytical and statistical technologies are innovative in utilizing machine learning, deep learning, neural networks, NLP, network analysis, and real-time data analysis. The use of such technologies, which autonomously perform tasks previously reserved for humans, has shown potential for more effective corruption counteraction. So, “innovative analytical and statistical technology” is defined as a modern collection of methods and tools for data analysis, designed to identify complex dependencies and useful patterns in data, improving decision-making, and detecting anomalies.
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Referências
ACFE (2022). Occupational Fraud 2022: A Report to the Nations. Retrieved from https://acortar.link/keVxFM
Chornutskyi, S. P. (2011). The essence and method of detecting facts of financial fraud. Economy and the state, 7, 127 131. (in Ukrainian)
Hastie, T., Tibshirani, R., & Friedman, J. (2016) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. New York, NY: Springer. https://doi.org/10.1007/978-0-387-84858-7
Huturov, O. I. (2019). Strategic innovation: teaching manual. Kharkiv: KhNAU. (in Ukrainian)
IDIA (2019). Artificial Intelligence in International Development: A Discussion Paper. Retrieved from https://acortar.link/Dssg1r
ISA (2010). International Standard on Auditing 240: The Auditor’s Responsibilities Relating to Fraud in an Audit of Financial Statements. 166-209. [File PDF]. Retrieved from https://acortar.link/P1T4Gd
Ishikawa, K. (1989). Introduction to Quality Control. London: Chapman & Hall.
Kikalishvili, M. V. (2021). Theoretical and applied principles of the formation and implementation of strategy and tactics of combating corruption crime: dissertation. Dnipro: University of Customs Service and Finance; Dnipropetrovsk State University of Internal Affairs. (in Ukrainian)
Köbis, N., Starke, C., & Rahwan, I. (2021). Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) Potentials and Pitfalls for Top-down and Bottom-up Approaches. Cornell University. https://doi.org/10.48550/arXiv.2102.11567
Köbis, N., Starke, C., & Rahwan, I. (2022). The promise and perils of using artificial intelligence to fight corruption. Nature Machine Intelligence, 4(5), 418 424.
https://doi.org/10.1038/s42256-022-00489-1
Kovtun, N. (2011). Methods of indicative assessment of possible fraud in the financial sphere. Bulletin of Taras Shevchenko Kyiv National University. "Economy" series, 123, 11 15. (in Ukrainian)
Lima, M. S. M., & Delen, D. (2020). Predicting and Explaining Corruption Across Countries: A Machine Learning Approach. Government Information Quarterly, 37(1). https://doi.org/10.1016/j.giq.2019.101407
López-Iturriaga, F. J., & Sanz, I. P. (2017). Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces. Social Indicators Research, 140(3), 975-998. https://doi.org/10.1007/s11205-017-1802-2
Lozynskyi, O. M. (2021). Psychological factors of citizens' intolerant attitude towards corruption: dissertation. Kyiv: Institute of Social and Political Psychology of the National Academy of Sciences of Ukraine. (in Ukrainian)
Marych, M. I. (2013). Concept and essence of corruption and corruption crime. State and law. Legal and political sciences, 61, 310-317. (In Ukranian)
Mazur, I. (2005). Corruption as an institution of the shadow economy. Economics and law, 8, 34 38. (In Ukranian)
Nevmerezhytskyi, Y. V. (2008). Corruption in Ukraine: causes, consequences, countermeasures: monograph. Kyiv: KNT. (In Ukranian)
Novikov, O. V. (2020). Public-legal protection against corruption in the conditions of regional development: dissertation. Kyiv: Research Institute of Public Law; Dnipropetrovsk State University of Internal Affairs. (In Ukranian)
Odilla, F. (2023). Bots Against Corruption: Exploring the Benefits and Limitations of AI-based Anti-corruption Technology. Crime, Law and Social Change. https://doi.org/10.1007/s10611-023-10091-0
Okuniev, O. Y., Boiko, O. I., & Lukin, S. Y. (2018). Anti-corruption compliance: a guide for the training program for persons responsible for the implementation of the anti-corruption program. Kyiv: Professional Association of Corporate Management, Center for International Private Entrepreneurship (CIPE). (In Ukranian)
Paul, A., Jolley, C., & Anthony, A. (2020). Reflecting the Past, Shaping the Future: Making AI Work for International Development. USAID. Retrieved from https://acortar.link/JBCxu6
Prykhodko, A. A. (2020). Administrative and legal support for preventing and countering corruption in Ukraine under the conditions of European integration: dissertation. Kyiv: Research Institute of Public Law, Dnipropetrovsk State University of Internal Affairs of Ukraine. (In Ukranian)
PWC (2011). The World Review of Economic Crimes. Retrieved from https://acortar.link/1Nc9WY
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Wellman, M. (2019). Machine Behaviour. Nature, 568(7753), 477-486.
https://doi.org/10.1038/s41586-019-1138-y
Rogel-Salazar, J. (2023). Statistics and Data Visualisation with Python. Boca Raton, FL: CRC Press.
Rogers, E. M. (1983). Diffusion of Innovations. New York, N.Y.: The Free Press.
Rose-Ackerman, S., & Palifka, B. J. (2018). Corruption, Organized Crime, and Money Laundering. In K. Basu & T. Cordella (Eds.), Institutions, Governance and the Control of Corruption (pp. 75-111). Cham, Switzerland: Palgrave Macmillan; Springer International Publishing AG.
Si?áková-Beblavá, E., & Beblavý, M. (2007). Approaches to defining corruption. Sociológia, 39(4), 316 336. (In Slovakian)
The Ukranian Week (2022, 04.07.2022). Methods of technological process improvement. Retrieved from https://tyzhden.ua/metody-vdoskonalennia-tekhnolohichnoho-protsesu/ (In Ukranian)
Transparency International (2023). What is Corruption? Retrieved from https://www.transparency.org/en/what-is-corruption
Trepak, V. M. (2020). Theoretical and applied problems of preventing and countering corruption in Ukraine: dissertation] Kyiv: Lviv Ivan Franko National University. (In Ukranian)
Wu, X., Zhu, X., Wu, G.-Q. & Ding, W. (2014). Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97 107. https://doi.org/10.1109/TKDE.2013.109
Zadorozhnyi, S. A. (2016). Mechanisms of preventing and countering corruption in local authorities: dissertation. Kyiv: Ivano-Frankivsk National Technical University of Oil and Gas. (In Ukranian)
Zakharova, I. V., & Filipova, L. Y. (2013). Basics of information and analytical activity: teaching manual. Kyiv: Center for Educational Literature. (In Ukranian)
Chornutskyi, S. P. (2011). The essence and method of detecting facts of financial fraud. Economy and the state, 7, 127 131. (in Ukrainian)
Hastie, T., Tibshirani, R., & Friedman, J. (2016) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. New York, NY: Springer. https://doi.org/10.1007/978-0-387-84858-7
Huturov, O. I. (2019). Strategic innovation: teaching manual. Kharkiv: KhNAU. (in Ukrainian)
IDIA (2019). Artificial Intelligence in International Development: A Discussion Paper. Retrieved from https://acortar.link/Dssg1r
ISA (2010). International Standard on Auditing 240: The Auditor’s Responsibilities Relating to Fraud in an Audit of Financial Statements. 166-209. [File PDF]. Retrieved from https://acortar.link/P1T4Gd
Ishikawa, K. (1989). Introduction to Quality Control. London: Chapman & Hall.
Kikalishvili, M. V. (2021). Theoretical and applied principles of the formation and implementation of strategy and tactics of combating corruption crime: dissertation. Dnipro: University of Customs Service and Finance; Dnipropetrovsk State University of Internal Affairs. (in Ukrainian)
Köbis, N., Starke, C., & Rahwan, I. (2021). Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) Potentials and Pitfalls for Top-down and Bottom-up Approaches. Cornell University. https://doi.org/10.48550/arXiv.2102.11567
Köbis, N., Starke, C., & Rahwan, I. (2022). The promise and perils of using artificial intelligence to fight corruption. Nature Machine Intelligence, 4(5), 418 424.
https://doi.org/10.1038/s42256-022-00489-1
Kovtun, N. (2011). Methods of indicative assessment of possible fraud in the financial sphere. Bulletin of Taras Shevchenko Kyiv National University. "Economy" series, 123, 11 15. (in Ukrainian)
Lima, M. S. M., & Delen, D. (2020). Predicting and Explaining Corruption Across Countries: A Machine Learning Approach. Government Information Quarterly, 37(1). https://doi.org/10.1016/j.giq.2019.101407
López-Iturriaga, F. J., & Sanz, I. P. (2017). Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces. Social Indicators Research, 140(3), 975-998. https://doi.org/10.1007/s11205-017-1802-2
Lozynskyi, O. M. (2021). Psychological factors of citizens' intolerant attitude towards corruption: dissertation. Kyiv: Institute of Social and Political Psychology of the National Academy of Sciences of Ukraine. (in Ukrainian)
Marych, M. I. (2013). Concept and essence of corruption and corruption crime. State and law. Legal and political sciences, 61, 310-317. (In Ukranian)
Mazur, I. (2005). Corruption as an institution of the shadow economy. Economics and law, 8, 34 38. (In Ukranian)
Nevmerezhytskyi, Y. V. (2008). Corruption in Ukraine: causes, consequences, countermeasures: monograph. Kyiv: KNT. (In Ukranian)
Novikov, O. V. (2020). Public-legal protection against corruption in the conditions of regional development: dissertation. Kyiv: Research Institute of Public Law; Dnipropetrovsk State University of Internal Affairs. (In Ukranian)
Odilla, F. (2023). Bots Against Corruption: Exploring the Benefits and Limitations of AI-based Anti-corruption Technology. Crime, Law and Social Change. https://doi.org/10.1007/s10611-023-10091-0
Okuniev, O. Y., Boiko, O. I., & Lukin, S. Y. (2018). Anti-corruption compliance: a guide for the training program for persons responsible for the implementation of the anti-corruption program. Kyiv: Professional Association of Corporate Management, Center for International Private Entrepreneurship (CIPE). (In Ukranian)
Paul, A., Jolley, C., & Anthony, A. (2020). Reflecting the Past, Shaping the Future: Making AI Work for International Development. USAID. Retrieved from https://acortar.link/JBCxu6
Prykhodko, A. A. (2020). Administrative and legal support for preventing and countering corruption in Ukraine under the conditions of European integration: dissertation. Kyiv: Research Institute of Public Law, Dnipropetrovsk State University of Internal Affairs of Ukraine. (In Ukranian)
PWC (2011). The World Review of Economic Crimes. Retrieved from https://acortar.link/1Nc9WY
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Wellman, M. (2019). Machine Behaviour. Nature, 568(7753), 477-486.
https://doi.org/10.1038/s41586-019-1138-y
Rogel-Salazar, J. (2023). Statistics and Data Visualisation with Python. Boca Raton, FL: CRC Press.
Rogers, E. M. (1983). Diffusion of Innovations. New York, N.Y.: The Free Press.
Rose-Ackerman, S., & Palifka, B. J. (2018). Corruption, Organized Crime, and Money Laundering. In K. Basu & T. Cordella (Eds.), Institutions, Governance and the Control of Corruption (pp. 75-111). Cham, Switzerland: Palgrave Macmillan; Springer International Publishing AG.
Si?áková-Beblavá, E., & Beblavý, M. (2007). Approaches to defining corruption. Sociológia, 39(4), 316 336. (In Slovakian)
The Ukranian Week (2022, 04.07.2022). Methods of technological process improvement. Retrieved from https://tyzhden.ua/metody-vdoskonalennia-tekhnolohichnoho-protsesu/ (In Ukranian)
Transparency International (2023). What is Corruption? Retrieved from https://www.transparency.org/en/what-is-corruption
Trepak, V. M. (2020). Theoretical and applied problems of preventing and countering corruption in Ukraine: dissertation] Kyiv: Lviv Ivan Franko National University. (In Ukranian)
Wu, X., Zhu, X., Wu, G.-Q. & Ding, W. (2014). Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97 107. https://doi.org/10.1109/TKDE.2013.109
Zadorozhnyi, S. A. (2016). Mechanisms of preventing and countering corruption in local authorities: dissertation. Kyiv: Ivano-Frankivsk National Technical University of Oil and Gas. (In Ukranian)
Zakharova, I. V., & Filipova, L. Y. (2013). Basics of information and analytical activity: teaching manual. Kyiv: Center for Educational Literature. (In Ukranian)
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Publicado
2023-08-30
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Yatsyna, Y., & Kudinov, I. (2023). Innovative analytical and statistical technology as a corruption counteraction tool: conceptual analysis. Amazonia Investiga, 12(67), 78–86. https://doi.org/10.34069/AI/2023.67.07.7
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