The Use of Artificial Intelligence (AI) in Internal Supervision to Reduce Fraud in the Public Procurement System in Indonesia
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Abstract
Corruption in the public procurement system is a significant challenge in many developing countries, including Indonesia. Artificial Intelligence (AI) technology offers innovative solutions for strengthening internal oversight and early detection of fraudulent activities. This article presents a systematic review of the literature that discusses the application of AI in internal control, focusing on fraud prevention in public procurement in Indonesia. Using the Systematic Literature Review (SLR) method with PRISMA guidelines, this article compiles, identifies, and synthesises relevant research to evaluate the effectiveness of AI and the challenges in its application. The study results show that although AI has great potential to improve accuracy and efficiency in detecting complex fraud patterns, significant barriers related to implementation costs, limited technology infrastructure, and low readiness of human resources in Indonesia are still significant challenges. Policy recommendations include accelerating AI adoption by developing supportive regulations and improving technological competence in the public sector.
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How to Cite
Amiruddin, A., Syamsuddin, S., Muntu, S. J., Helda, W. O., & Rahadi, G. M. D. P. (2024). The Use of Artificial Intelligence (AI) in Internal Supervision to Reduce Fraud in the Public Procurement System in Indonesia. ProBisnis : Jurnal Manajemen, 15(5), 769–780. Retrieved from https://ejournal.joninstitute.org/index.php/ProBisnis/article/view/715
References
A. Leite, R., Gschwandtner, T., Miksch, S., Gstrein, E., & Kuntner, J. (2020). NEVA: Visual Analytics to Identify Fraudulent Networks. Computer Graphics Forum, 39(6), 344–359. https://doi.org/10.1111/cgf.14042
Abidi, W. U. H., Daoud, M. S., Ihnaini, B., Khan, M. A., Alyas, T., Fatima, A., & Ahmad, M. (2021). Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning. IEEE Access, 9, 113612–113621. https://doi.org/10.1109/ACCESS.2021.3098628
Ahn, J. M., Lee, W., & Mortara, L. (2020). Do government R&D subsidies stimulate collaboration initiatives in private firms? Technological Forecasting and Social Change, 151, 119840. https://doi.org/10.1016/j.techfore.2019.119840
Alshahrani, A., Dennehy, D., & Mäntymäki, M. (2022). An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia. Government Information Quarterly, 39(4), 101617. https://doi.org/10.1016/j.giq.2021.101617
Ångström, R. C., Björn, M., Dahlander, L., Mähring, M., & Wallin, M. W. (2023). Getting AI Implementation Right: Insights from a G lobal S urvey . California Management Review, 66(1), 5–22. https://doi.org/10.1177/00081256231190430
Ashfaq, T., Khalid, R., Yahaya, A. S., Aslam, S., Azar, A. T., Alsafari, S., & Hameed, I. A. (2022). A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. Sensors, 22(19), 7162. https://doi.org/10.3390/s22197162
Azhgaliyeva, D., Beirne, J., & Mishra, R. (2023). What matters for private investment in renewable energy? Climate Policy, 23(1), 71–87. https://doi.org/10.1080/14693062.2022.2069664
Baesens, B., Höppner, S., & Verdonck, T. (2021). Data engineering for fraud detection. Decision Support Systems, 150, 113492. https://doi.org/10.1016/j.dss.2021.113492
Bahasoan, A. N., Anwar, A. I., Lekas, M. N. J., & Asryad, R. (2024). Otonomi Daerah dan Pertumbuhan Ekonomi di Indonesia: Literature Review. Ekonomis: Journal of Economics and Business, 8(1), 43. https://doi.org/10.33087/ekonomis.v8i1.1119
BAO, Y., KE, B., LI, B., YU, Y. J., & ZHANG, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475-679X.12292
Benedick, P.-L., Robert, J., & Le Traon, Y. (2021). A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings. Sensors, 21(18), 6195. https://doi.org/10.3390/s21186195
Bin Mofidul, R., Alam, M. M., Rahman, M. H., & Jang, Y. M. (2022). Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI. Sensors, 22(22), 8980. https://doi.org/10.3390/s22228980
Brown, S., Davidovic, J., & Hasan, A. (2021). The algorithm audit: Scoring the algorithms that score us. Big Data & Society, 8(1). https://doi.org/10.1177/2053951720983865
Caiazzo, B., Murino, T., Petrillo, A., Piccirillo, G., & Santini, S. (2023). An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation. Journal of Manufacturing Technology Management, 34(4), 507–534. https://doi.org/10.1108/JMTM-02-2022-0092
Calvano, E., Calzolari, G., Denicolò, V., Harrington, J. E., & Pastorello, S. (2020). Protecting consumers from collusive prices due to AI. Science, 370(6520), 1040–1042. https://doi.org/10.1126/science.abe3796
Cao, S., Yang, X., Chen, C., Zhou, J., Li, X., & Qi, Y. (2019). TitAnt. Proceedings of the VLDB Endowment, 12(12), 2082–2093. https://doi.org/10.14778/3352063.3352126
Chandel, S., & Sarkar, S. (2023). Corruption in multidimensional procurement auctions under asymmetry. Economic Modelling, 120, 106187. https://doi.org/10.1016/j.econmod.2022.106187
Chassang, S., & Ortner, J. (2019). Collusion in Auctions with Constrained Bids: Theory and Evidence from Public Procurement. Journal of Political Economy, 127(5), 2269–2300. https://doi.org/10.1086/701812
Cheng, H., Zhang, Z., Liao, Z., Wei, Y., & Nkongo Mvondo, J. M. (2020). Different policy instruments and the threshold effects on collaboration efficiency in China. Science and Public Policy, 47(3), 348–359. https://doi.org/10.1093/scipol/scaa016
Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421
Cui, R., Li, M., & Zhang, S. (2020). AI and Procurement. SSRN Electronic Journal, 691–706. https://doi.org/10.2139/ssrn.3570967
Czako, Z., Sebestyen, G., & Hangan, A. (2021). AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning. Expert Systems with Applications, 182, 115225. https://doi.org/10.1016/j.eswa.2021.115225
Decarolis, F., & Giorgiantonio, C. (2022). Corruption red flags in public procurement: new evidence from Italian calls for tenders. EPJ Data Science, 11(1), 16. https://doi.org/10.1140/epjds/s13688-022-00325-x
Desouza, K. C., Dawson, G. S., & Chenok, D. (2020). Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Business Horizons, 63(2), 205–213. https://doi.org/10.1016/j.bushor.2019.11.004
Dunan, A., & Mudjiyanto, B. (2020). "THE REPUBLIC OF INDONESIA GOVERNMENT PUBLIC RELATIONS COMMUNICATION STRATEGY IN THE ERA OF THE INDUSTRIAL REVOLUTION 4.0 ". Journal of Southeast Asian Studies, 25(1), 58–78. https://doi.org/10.22452/jati.vol25no1.4
Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A., Gupta, J., Hart, C., Jirotka, M., Johnson, H., LaPointe, C., Llorens, A. J., Mackworth, A. K., Maple, C., Pálsson, S. E., Pasquale, F., Winfield, A., & Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566–571. https://doi.org/10.1038/s42256-021-00370-7
Fatimah, Y. A., Govindan, K., Murniningsih, R., & Setiawan, A. (2020). Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia. Journal of Cleaner Production, 269, 122263. https://doi.org/10.1016/j.jclepro.2020.122263
Fazekas, M., & Wachs, J. (2020). Corruption and the Network Structure of Public Contracting Markets across Government Change. Politics and Governance, 8(2), 153–166. https://doi.org/10.17645/pag.v8i2.2707
Furqan, A. C., Wardhani, R., Martani, D., & Setyaningrum, D. (2020). The effect of audit findings and audit recommendation follow-up on the financial report and public service quality in Indonesia. International Journal of Public Sector Management, 33(5), 535–559. https://doi.org/10.1108/IJPSM-06-2019-0173
Gallego, J., Rivero, G., & Martínez, J. (2021). Preventing rather than punishing: An early warning model of malfeasance in public procurement. International Journal of Forecasting, 37(1), 360–377. https://doi.org/10.1016/j.ijforecast.2020.06.006
Gayatri, G., Jaya, I. G. N. M., & Rumata, V. M. (2022). The Indonesian Digital Workforce Gaps in 2021–2025. Sustainability, 15(1), 754. https://doi.org/10.3390/su15010754
Huang, Y., & Xia, J. (2019). Procurement auctions under quality manipulation corruption. European Economic Review, 111, 380–399. https://doi.org/10.1016/j.euroecorev.2018.11.001
Huber, M., & Imhof, D. (2019). Machine learning with screens for detecting bid-rigging cartels. International Journal of Industrial Organization, 65, 277–301. https://doi.org/10.1016/j.ijindorg.2019.04.002
Imhof, D. (2019). DETECTING BID-RIGGING CARTELS WITH DESCRIPTIVE STATISTICS. Journal of Competition Law & Economics, 15(4), 427–467. https://doi.org/10.1093/joclec/nhz019
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Knack, S., Biletska, N., & Kacker, K. (2019). Deterring Kickbacks and Encouraging Entry in Public Procurement Markets: Evidence from Firm Surveys in 90 Developing Countries. The World Bank Economic Review, 33(2), 287–309. https://doi.org/10.1093/wber/lhy016
Križić, I. (2021). Regulating public procurement in Brazil, India, and China: Toward the regulatory‐developmental state. Regulation & Governance, 15(3), 561–580. https://doi.org/10.1111/rego.12243
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 36–49. https://doi.org/10.1037/amp0000972
Li, Y., Yu, Y., Min, G., Susilo, W., Ni, J., & Choo, K.-K. R. (2019). Fuzzy Identity-Based Data Integrity Auditing for Reliable Cloud Storage Systems. IEEE Transactions on Dependable and Secure Computing, 16(1), 72–83. https://doi.org/10.1109/TDSC.2017.2662216
Lyra, M. S., Curado, A., Damásio, B., Bação, F., & Pinheiro, F. L. (2021). Characterization of the firm–firm public procurement co-bidding network from the State of Ceará (Brazil) municipalities. Applied Network Science, 6(1), 77. https://doi.org/10.1007/s41109-021-00418-y
Lyu, Y., Rajbahadur, G. K., Lin, D., Chen, B., & Jiang, Z. M. (Jack). (2022). Towards a Consistent Interpretation of AIOps Models. ACM Transactions on Software Engineering and Methodology, 31(1), 1–38. https://doi.org/10.1145/3488269
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
Naja, I., Markovic, M., Edwards, P., Pang, W., Cottrill, C., & Williams, R. (2022). Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information. IEEE Access, 10, 74383–74411. https://doi.org/10.1109/ACCESS.2022.3188967
Narayan, P. K., Narayan, S., Eki Rahman, R., & Setiawan, I. (2019). Bitcoin price growth and Indonesia’s monetary system. Emerging Markets Review, 38, 364–376. https://doi.org/10.1016/j.ememar.2018.11.005
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ, n160. https://doi.org/10.1136/bmj.n160
Pardamean, B., Suparyanto, T., Cenggoro, T. W., Sudigyo, D., & Anugrahana, A. (2022). AI-Based Learning Style Prediction in Online Learning for Primary Education. IEEE Access, 10, 35725–35735. https://doi.org/10.1109/ACCESS.2022.3160177
Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303. https://doi.org/10.1016/j.dss.2020.113303
Pramono, A. J., Suwarno, S., Amyar, F., & Friska, R. (2023). Exploring Technology Acceptance in Management Accounting Tools’ Adoption in Public Sector Accounting: A Sustainability Perspective for Organizations. Sustainability, 15(21), 15334. https://doi.org/10.3390/su152115334
Qi, N., Shiping, L., & Jing, H. (2022). An Evolutionary Game Simulation of a Composite Subsidy Policy to Promote Military-Civilian Integration: A National System of Innovation With Chinese Characteristics. IEEE Access, 10, 88276–88294. https://doi.org/10.1109/ACCESS.2022.3200383
Raca, D., Zahran, A. H., Sreenan, C. J., Sinha, R. K., Halepovic, E., Jana, R., & Gopalakrishnan, V. (2020). On Leveraging Machine and Deep Learning for Throughput Prediction in Cellular Networks: Design, Performance, and Challenges. IEEE Communications Magazine, 58(3), 11–17. https://doi.org/10.1109/MCOM.001.1900394
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., Koffel, J. B., Blunt, H., Brigham, T., Chang, S., Clark, J., Conway, A., Couban, R., de Kock, S., Farrah, K., Fehrmann, P., Foster, M., Fowler, S. A., Glanville, J., … Young, S. (2021). PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Systematic Reviews, 10(1), 39. https://doi.org/10.1186/s13643-020-01542-z
Sari, D. A., Margules, C., Lim, H. S., Sayer, J. A., Boedhihartono, A. K., Macgregor, C. J., Dale, A. P., & Poon, E. (2022). Performance Auditing to Assess the Implementation of the Sustainable Development Goals (SDGs) in Indonesia. Sustainability, 14(19), 12772. https://doi.org/10.3390/su141912772
Signor, R., Ballesteros-Perez, P., & Love, P. E. D. (2023). Collusion Detection in Infrastructure Procurement: A Modified Order Statistic Method for Uncapped Auctions. IEEE Transactions on Engineering Management, 70(2), 464–477. https://doi.org/10.1109/TEM.2021.3049129
Soekamto, H., Nikolaeva, I., Abbood, A. A. A., Grachev, D., Kosov, M., Yumashev, A., Kostyrin, E., Lazareva, N., Kvitkovskaja, A., & Nikitina, N. (2022). Professional Development of Rural Teachers Based on Digital Literacy. Emerging Science Journal, 6(6), 1525–1540. https://doi.org/10.28991/ESJ-2022-06-06-019
Sri, M., & Solimun, S. (2019). The relationship between audit quality and risk taking toward value creation in Indonesia. Journal of Accounting in Emerging Economies, 9(2), 251–267. https://doi.org/10.1108/JAEE-11-2017-0110
Sumiyana, S., Hendrian, H., Jayasinghe, K., & Wijethilaka, C. (2023). Public sector performance auditing in a political hegemony: A case study of Indonesia. Financial Accountability & Management, 39(4), 691–714. https://doi.org/10.1111/faam.12296
Suwarto, D. H., Setiawan, B., & Machmiyah, S. (2022). Developing Digital Literacy Practices in Yogyakarta Elementary Schools. Electronic Journal of E-Learning, 20(2), pp101-111. https://doi.org/10.34190/ejel.20.2.2602
Suyono, E., & Farooque, O. Al. (2019). Auditors’ professionalism and factors affecting it: insights from Indonesia. Journal of Asia Business Studies, 13(4), 543–558. https://doi.org/10.1108/JABS-03-2018-0073
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
Velasco, R. B., Carpanese, I., Interian, R., Paulo Neto, O. C. G., & Ribeiro, C. C. (2021). A decision support system for fraud detection in public procurement. International Transactions in Operational Research, 28(1), 27–47. https://doi.org/10.1111/itor.12811
Wang, H. (2020). Quality manipulation and limit corruption in competitive procurement. European Journal of Operational Research, 283(3), 1124–1135. https://doi.org/10.1016/j.ejor.2019.11.053
Wang, K., Zipperle, M., Becherer, M., Gottwalt, F., & Zhang, Y. (2020). An AI-Based Automated Continuous Compliance Awareness Framework (CoCAF) for Procurement Auditing. Big Data and Cognitive Computing, 4(3), 23. https://doi.org/10.3390/bdcc4030023
Werner, M., Wiese, M., & Maas, A. (2021). Embedding process mining into financial statement audits. International Journal of Accounting Information Systems, 41, 100514. https://doi.org/10.1016/j.accinf.2021.100514
Westerski, A., Kanagasabai, R., Shaham, E., Narayanan, A., Wong, J., & Singh, M. (2021). Explainable anomaly detection for procurement fraud identification—lessons from practical deployments. International Transactions in Operational Research, 28(6), 3276–3302. https://doi.org/10.1111/itor.12968
Whang, S. E., Roh, Y., Song, H., & Lee, J.-G. (2023). Data collection and quality challenges in deep learning: a data-centric AI perspective. The VLDB Journal, 32(4), 791–813. https://doi.org/10.1007/s00778-022-00775-9
Wu, S., Chen, Y., Wang, Q., Li, M., Wang, C., & Luo, X. (2019). CReam: A Smart Contract Enabled Collusion-Resistant e-Auction. IEEE Transactions on Information Forensics and Security, 14(7), 1687–1701. https://doi.org/10.1109/TIFS.2018.2883275
Wuryaningsih, W., Susilastuti, D. H., Darwin, M., & Pierewan, A. C. (2019). Effects of Web-Based Learning and F2F Learning on Teachers Achievement in Teacher Training Program in Indonesia. International Journal of Emerging Technologies in Learning (IJET), 14(21), 123. https://doi.org/10.3991/ijet.v14i21.10736
Xiong, W., & Xiong, L. (2021). Anti-collusion data auction mechanism based on smart contract. Information Sciences, 555, 386–409. https://doi.org/10.1016/j.ins.2020.10.053
Xu, Y., Ren, J., Zhang, Y., Zhang, C., Shen, B., & Zhang, Y. (2019). Blockchain Empowered Arbitrable Data Auditing Scheme for Network Storage as a Service. IEEE Transactions on Services Computing, 1–1. https://doi.org/10.1109/TSC.2019.2953033
Yao, H., & Huang, W. (2022). Effect of R&D Subsidies on External Collaborative Networks and the Sustainable Innovation Performance of Strategic Emerging Enterprises: Evidence from China. Sustainability, 14(8), 4722. https://doi.org/10.3390/su14084722
Zanetti, M., Jamhour, E., Pellenz, M., Penna, M., Zambenedetti, V., & Chueiri, I. (2019). A Tunable Fraud Detection System for Advanced Metering Infrastructure Using Short-Lived Patterns. IEEE Transactions on Smart Grid, 10(1), 830–840. https://doi.org/10.1109/TSG.2017.2753738
Zdyrko, N., & Ostapchuk, S. (2020). Analysis and State Audit of Public Procurement: The Criteria of Effectiveness. Accounting and Finance, 1(87), 146–157. https://doi.org/10.33146/2307-9878-2020-1(87)-146-157
Zhou, X., Liang, W., Shimizu, S., Ma, J., & Jin, Q. (2021). Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Informatics, 17(8), 5790–5798. https://doi.org/10.1109/TII.2020.3047675
Abidi, W. U. H., Daoud, M. S., Ihnaini, B., Khan, M. A., Alyas, T., Fatima, A., & Ahmad, M. (2021). Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning. IEEE Access, 9, 113612–113621. https://doi.org/10.1109/ACCESS.2021.3098628
Ahn, J. M., Lee, W., & Mortara, L. (2020). Do government R&D subsidies stimulate collaboration initiatives in private firms? Technological Forecasting and Social Change, 151, 119840. https://doi.org/10.1016/j.techfore.2019.119840
Alshahrani, A., Dennehy, D., & Mäntymäki, M. (2022). An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia. Government Information Quarterly, 39(4), 101617. https://doi.org/10.1016/j.giq.2021.101617
Ångström, R. C., Björn, M., Dahlander, L., Mähring, M., & Wallin, M. W. (2023). Getting AI Implementation Right: I
Ashfaq, T., Khalid, R., Yahaya, A. S., Aslam, S., Azar, A. T., Alsafari, S., & Hameed, I. A. (2022). A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. Sensors, 22(19), 7162. https://doi.org/10.3390/s22197162
Azhgaliyeva, D., Beirne, J., & Mishra, R. (2023). What matters for private investment in renewable energy? Climate Policy, 23(1), 71–87. https://doi.org/10.1080/14693062.2022.2069664
Baesens, B., Höppner, S., & Verdonck, T. (2021). Data engineering for fraud detection. Decision Support Systems, 150, 113492. https://doi.org/10.1016/j.dss.2021.113492
Bahasoan, A. N., Anwar, A. I., Lekas, M. N. J., & Asryad, R. (2024). Otonomi Daerah dan Pertumbuhan Ekonomi di Indonesia: Literature Review. Ekonomis: Journal of Economics and Business, 8(1), 43. https://doi.org/10.33087/ekonomis.v8i1.1119
BAO, Y., KE, B., LI, B., YU, Y. J., & ZHANG, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475-679X.12292
Benedick, P.-L., Robert, J., & Le Traon, Y. (2021). A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings. Sensors, 21(18), 6195. https://doi.org/10.3390/s21186195
Bin Mofidul, R., Alam, M. M., Rahman, M. H., & Jang, Y. M. (2022). Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI. Sensors, 22(22), 8980. https://doi.org/10.3390/s22228980
Brown, S., Davidovic, J., & Hasan, A. (2021). The algorithm audit: Scoring the algorithms that score us. Big Data & Society, 8(1). https://doi.org/10.1177/2053951720983865
Caiazzo, B., Murino, T., Petrillo, A., Piccirillo, G., & Santini, S. (2023). An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation. Journal of Manufacturing Technology Management, 34(4), 507–534. https://doi.org/10.1108/JMTM-02-2022-0092
Calvano, E., Calzolari, G., Denicolò, V., Harrington, J. E., & Pastorello, S. (2020). Protecting consumers from collusive prices due to AI. Science, 370(6520), 1040–1042. https://doi.org/10.1126/science.abe3796
Cao, S., Yang, X., Chen, C., Zhou, J., Li, X., & Qi, Y. (2019). TitAnt. Proceedings of the VLDB Endowment, 12(12), 2082–2093. https://doi.org/10.14778/3352063.3352126
Chandel, S., & Sarkar, S. (2023). Corruption in multidimensional procurement auctions under asymmetry. Economic Modelling, 120, 106187. https://doi.org/10.1016/j.econmod.2022.106187
Chassang, S., & Ortner, J. (2019). Collusion in Auctions with Constrained Bids: Theory and Evidence from Public Procurement. Journal of Political Economy, 127(5), 2269–2300. https://doi.org/10.1086/701812
Cheng, H., Zhang, Z., Liao, Z., Wei, Y., & Nkongo Mvondo, J. M. (2020). Different policy instruments and the threshold effects on collaboration efficiency in China. Science and Public Policy, 47(3), 348–359. https://doi.org/10.1093/scipol/scaa016
Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421
Cui, R., Li, M., & Zhang, S. (2020). AI and Procurement. SSRN Electronic Journal, 691–706. https://doi.org/10.2139/ssrn.3570967
Czako, Z., Sebestyen, G., & Hangan, A. (2021). AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning. Expert Systems with Applications, 182, 115225. https://doi.org/10.1016/j.eswa.2021.115225
Decarolis, F., & Giorgiantonio, C. (2022). Corruption red flags in public procurement: new evidence from Italian calls for tenders. EPJ Data Science, 11(1), 16. https://doi.org/10.1140/epjds/s13688-022-00325-x
Desouza, K. C., Dawson, G. S., & Chenok, D. (2020). Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Business Horizons, 63(2), 205–213. https://doi.org/10.1016/j.bushor.2019.11.004
Dunan, A., & Mudjiyanto, B. (2020). "THE REPUBLIC OF INDONESIA GOVERNMENT PUBLIC RELATIONS COMMUNICATION STRATEGY IN THE ERA OF THE INDUSTRIAL REVOLUTION 4.0 ". Journal of Southeast Asian Studies, 25(1), 58–78. https://doi.org/10.22452/jati.vol25no1.4
Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A., Gupta, J., Hart, C., Jirotka, M., Johnson, H., LaPointe, C., Llorens, A. J., Mackworth, A. K., Maple, C., Pálsson, S. E., Pasquale, F., Winfield, A., & Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566–571. https://doi.org/10.1038/s42256-021-00370-7
Fatimah, Y. A., Govindan, K., Murniningsih, R., & Setiawan, A. (2020). Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia. Journal of Cleaner Production, 269, 122263. https://doi.org/10.1016/j.jclepro.2020.122263
Fazekas, M., & Wachs, J. (2020). Corruption and the Network Structure of Public Contracting Markets across Government Change. Politics and Governance, 8(2), 153–166. https://doi.org/10.17645/pag.v8i2.2707
Furqan, A. C., Wardhani, R., Martani, D., & Setyaningrum, D. (2020). The effect of audit findings and audit recommendation follow-up on the financial report and public service quality in Indonesia. International Journal of Public Sector Management, 33(5), 535–559. https://doi.org/10.1108/IJPSM-06-2019-0173
Gallego, J., Rivero, G., & Martínez, J. (2021). Preventing rather than punishing: An early warning model of malfeasance in public procurement. International Journal of Forecasting, 37(1), 360–377. https://doi.org/10.1016/j.ijforecast.2020.06.006
Gayatri, G., Jaya, I. G. N. M., & Rumata, V. M. (2022). The Indonesian Digital Workforce Gaps in 2021–2025. Sustainability, 15(1), 754. https://doi.org/10.3390/su15010754
Huang, Y., & Xia, J. (2019). Procurement auctions under quality manipulation corruption. European Economic Review, 111, 380–399. https://doi.org/10.1016/j.euroecorev.2018.11.001
Huber, M., & Imhof, D. (2019). Machine learning with screens for detecting bid-rigging cartels. International Journal of Industrial Organization, 65, 277–301. https://doi.org/10.1016/j.ijindorg.2019.04.002
Imhof, D. (2019). DETECTING BID-RIGGING CARTELS WITH DESCRIPTIVE STATISTICS. Journal of Competition Law & Economics, 15(4), 427–467. https://doi.org/10.1093/joclec/nhz019
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Knack, S., Biletska, N., & Kacker, K. (2019). Deterring Kickbacks and Encouraging Entry in Public Procurement Markets: Evidence from Firm Surveys in 90 Developing Countries. The World Bank Economic Review, 33(2), 287–309. https://doi.org/10.1093/wber/lhy016
Križić, I. (2021). Regulating public procurement in Brazil, India, and China: Toward the regulatory‐developmental state. Regulation & Governance, 15(3), 561–580. https://doi.org/10.1111/rego.12243
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 36–49. https://doi.org/10.1037/amp0000972
Li, Y., Yu, Y., Min, G., Susilo, W., Ni, J., & Choo, K.-K. R. (2019). Fuzzy Identity-Based Data Integrity Auditing for Reliable Cloud Storage Systems. IEEE Transactions on Dependable and Secure Computing, 16(1), 72–83. https://doi.org/10.1109/TDSC.2017.2662216
Lyra, M. S., Curado, A., Damásio, B., Bação, F., & Pinheiro, F. L. (2021). Characterization of the firm–firm public procurement co-bidding network from the State of Ceará (Brazil) municipalities. Applied Network Science, 6(1), 77. https://doi.org/10.1007/s41109-021-00418-y
Lyu, Y., Rajbahadur, G. K., Lin, D., Chen, B., & Jiang, Z. M. (Jack). (2022). Towards a Consistent Interpretation of AIOps Models. ACM Transactions on Software Engineering and Methodology, 31(1), 1–38. https://doi.org/10.1145/3488269
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
Naja, I., Markovic, M., Edwards, P., Pang, W., Cottrill, C., & Williams, R. (2022). Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information. IEEE Access, 10, 74383–74411. https://doi.org/10.1109/ACCESS.2022.3188967
Narayan, P. K., Narayan, S., Eki Rahman, R., & Setiawan, I. (2019). Bitcoin price growth and Indonesia’s monetary system. Emerging Markets Review, 38, 364–376. https://doi.org/10.1016/j.ememar.2018.11.005
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ, n160. https://doi.org/10.1136/bmj.n160
Pardamean, B., Suparyanto, T., Cenggoro, T. W., Sudigyo, D., & Anugrahana, A. (2022). AI-Based Learning Style Prediction in Online Learning for Primary Education. IEEE Access, 10, 35725–35735. https://doi.org/10.1109/ACCESS.2022.3160177
Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303. https://doi.org/10.1016/j.dss.2020.113303
Pramono, A. J., Suwarno, S., Amyar, F., & Friska, R. (2023). Exploring Technology Acceptance in Management Accounting Tools’ Adoption in Public Sector Accounting: A Sustainability Perspective for Organizations. Sustainability, 15(21), 15334. https://doi.org/10.3390/su152115334
Qi, N., Shiping, L., & Jing, H. (2022). An Evolutionary Game Simulation of a Composite Subsidy Policy to Promote Military-Civilian Integration: A National System of Innovation With Chinese Characteristics. IEEE Access, 10, 88276–88294. https://doi.org/10.1109/ACCESS.2022.3200383
Raca, D., Zahran, A. H., Sreenan, C. J., Sinha, R. K., Halepovic, E., Jana, R., & Gopalakrishnan, V. (2020). On Leveraging Machine and Deep Learning for Throughput Prediction in Cellular Networks: Design, Performance, and Challenges. IEEE Communications Magazine, 58(3), 11–17. https://doi.org/10.1109/MCOM.001.1900394
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., Koffel, J. B., Blunt, H., Brigham, T., Chang, S., Clark, J., Conway, A., Couban, R., de Kock, S., Farrah, K., Fehrmann, P., Foster, M., Fowler, S. A., Glanville, J., … Young, S. (2021). PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Systematic Reviews, 10(1), 39. https://doi.org/10.1186/s13643-020-01542-z
Sari, D. A., Margules, C., Lim, H. S., Sayer, J. A., Boedhihartono, A. K., Macgregor, C. J., Dale, A. P., & Poon, E. (2022). Performance Auditing to Assess the Implementation of the Sustainable Development Goals (SDGs) in Indonesia. Sustainability, 14(19), 12772. https://doi.org/10.3390/su141912772
Signor, R., Ballesteros-Perez, P., & Love, P. E. D. (2023). Collusion Detection in Infrastructure Procurement: A Modified Order Statistic Method for Uncapped Auctions. IEEE Transactions on Engineering Management, 70(2), 464–477. https://doi.org/10.1109/TEM.2021.3049129
Soekamto, H., Nikolaeva, I., Abbood, A. A. A., Grachev, D., Kosov, M., Yumashev, A., Kostyrin, E., Lazareva, N., Kvitkovskaja, A., & Nikitina, N. (2022). Professional Development of Rural Teachers Based on Digital Literacy. Emerging Science Journal, 6(6), 1525–1540. https://doi.org/10.28991/ESJ-2022-06-06-019
Sri, M., & Solimun, S. (2019). The relationship between audit quality and risk taking toward value creation in Indonesia. Journal of Accounting in Emerging Economies, 9(2), 251–267. https://doi.org/10.1108/JAEE-11-2017-0110
Sumiyana, S., Hendrian, H., Jayasinghe, K., & Wijethilaka, C. (2023). Public sector performance auditing in a political hegemony: A case study of Indonesia. Financial Accountability & Management, 39(4), 691–714. https://doi.org/10.1111/faam.12296
Suwarto, D. H., Setiawan, B., & Machmiyah, S. (2022). Developing Digital Literacy Practices in Yogyakarta Elementary Schools. Electronic Journal of E-Learning, 20(2), pp101-111. https://doi.org/10.34190/ejel.20.2.2602
Suyono, E., & Farooque, O. Al. (2019). Auditors’ professionalism and factors affecting it: insights from Indonesia. Journal of Asia Business Studies, 13(4), 543–558. https://doi.org/10.1108/JABS-03-2018-0073
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
Velasco, R. B., Carpanese, I., Interian, R., Paulo Neto, O. C. G., & Ribeiro, C. C. (2021). A decision support system for fraud detection in public procurement. International Transactions in Operational Research, 28(1), 27–47. https://doi.org/10.1111/itor.12811
Wang, H. (2020). Quality manipulation and limit corruption in competitive procurement. European Journal of Operational Research, 283(3), 1124–1135. https://doi.org/10.1016/j.ejor.2019.11.053
Wang, K., Zipperle, M., Becherer, M., Gottwalt, F., & Zhang, Y. (2020). An AI-Based Automated Continuous Compliance Awareness Framework (CoCAF) for Procurement Auditing. Big Data and Cognitive Computing, 4(3), 23. https://doi.org/10.3390/bdcc4030023
Werner, M., Wiese, M., & Maas, A. (2021). Embedding process mining into financial statement audits. International Journal of Accounting Information Systems, 41, 100514. https://doi.org/10.1016/j.accinf.2021.100514
Westerski, A., Kanagasabai, R., Shaham, E., Narayanan, A., Wong, J., & Singh, M. (2021). Explainable anomaly detection for procurement fraud identification—lessons from practical deployments. International Transactions in Operational Research, 28(6), 3276–3302. https://doi.org/10.1111/itor.12968
Whang, S. E., Roh, Y., Song, H., & Lee, J.-G. (2023). Data collection and quality challenges in deep learning: a data-centric AI perspective. The VLDB Journal, 32(4), 791–813. https://doi.org/10.1007/s00778-022-00775-9
Wu, S., Chen, Y., Wang, Q., Li, M., Wang, C., & Luo, X. (2019). CReam: A Smart Contract Enabled Collusion-Resistant e-Auction. IEEE Transactions on Information Forensics and Security, 14(7), 1687–1701. https://doi.org/10.1109/TIFS.2018.2883275
Wuryaningsih, W., Susilastuti, D. H., Darwin, M., & Pierewan, A. C. (2019). Effects of Web-Based Learning and F2F Learning on Teachers Achievement in Teacher Training Program in Indonesia. International Journal of Emerging Technologies in Learning (IJET), 14(21), 123. https://doi.org/10.3991/ijet.v14i21.10736
Xiong, W., & Xiong, L. (2021). Anti-collusion data auction mechanism based on smart contract. Information Sciences, 555, 386–409. https://doi.org/10.1016/j.ins.2020.10.053
Xu, Y., Ren, J., Zhang, Y., Zhang, C., Shen, B., & Zhang, Y. (2019). Blockchain Empowered Arbitrable Data Auditing Scheme for Network Storage as a Service. IEEE Transactions on Services Computing, 1–1. https://doi.org/10.1109/TSC.2019.2953033
Yao, H., & Huang, W. (2022). Effect of R&D Subsidies on External Collaborative Networks and the Sustainable Innovation Performance of Strategic Emerging Enterprises: Evidence from China. Sustainability, 14(8), 4722. https://doi.org/10.3390/su14084722
Zanetti, M., Jamhour, E., Pellenz, M., Penna, M., Zambenedetti, V., & Chueiri, I. (2019). A Tunable Fraud Detection System for Advanced Metering Infrastructure Using Short-Lived Patterns. IEEE Transactions on Smart Grid, 10(1), 830–840. https://doi.org/10.1109/TSG.2017.2753738
Zdyrko, N., & Ostapchuk, S. (2020). Analysis and State Audit of Public Procurement: The Criteria of Effectiveness. Accounting and Finance, 1(87), 146–157. https://doi.org/10.33146/2307-9878-2020-1(87)-146-157
Zhou, X., Liang, W., Shimizu, S., Ma, J., & Jin, Q. (2021). Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Informatics, 17(8), 5790–5798. https://doi.org/10.1109/TII.2020.3047675