Harnessing Big Data and AI for Predictive Insights: Assessing Bankruptcy Risk in Indonesian Stocks
DOI:
https://doi.org/10.56294/dm2024.622Keywords:
Big Data, artificial intelligence, bankruptcy prediction, Altman Z-Score, Indonesian Stock ExchangeAbstract
Introduction: This research aims to investigate the use of financial Big Data and artificial intelligence (AI) in predicting the bankruptcy risk of companies listed on the Indonesia Stock Exchange (BEI), with the Altman Z-Score model as the main framework.
Objective: In this research, an intervening variable in the form of financial data quality is introduced to assess the role of mediation in increasing the accuracy of bankruptcy predictions..
Method: The research method used is quantitative with the analytical method used is Structural Equation Modeling Partial Least Squares (SEM-PLS), which allows analysis of the relationship between independent variables (Big Data and AI), intervening variables (quality of financial data), and dependent variables (bankruptcy risk prediction).
Result: The research results show that the integration of financial Big Data and AI significantly increases the accuracy of company bankruptcy risk predictions on the IDX, with the quality of financial data acting as an intervening variable that strengthens this relationship. The influence of Big Data and AI on bankruptcy prediction through the quality of financial data has also been proven to provide more precise and faster results compared to the conventional Altman Z-Score model.
Conclusion: These findings confirm that the quality of financial data is a key factor that must be considered in optimizing bankruptcy predictions in the capital market. This research has implications for the development of financial technology (Fintech) and risk management strategies in public companies, especially in identifying bankruptcy risks more effectively by utilizing the latest technology.
References
1. Mahmud H, Islam AKMN, Mitra RK. What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness. Technol Forecast Soc Change. 2023;193(May):122641. DOI: https://doi.org/10.1016/j.techfore.2023.122641
2. Bodaghi A, Oliveira J. A financial anomaly prediction approach using semantic space of news flow on twitter. Decis Anal J. 2024;10(January):100422. DOI: https://doi.org/10.1016/j.dajour.2024.100422
3. Graham B, Bonner K. The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach. J Bus Res. 2024;175(February):114567. DOI: https://doi.org/10.1016/j.jbusres.2024.114567
4. Brenes RF, Johannssen A, Chukhrova N. An intelligent bankruptcy prediction model using a multilayer perceptron. Intell Syst with Appl. 2022;16(September). DOI: https://doi.org/10.1016/j.iswa.2022.200136
5. Hartley N, Kunz W, Tarbit J. The corporate digital responsibility (CDR) calculus: How and why organizations reconcile digital and ethical trade-offs for growth. Organ Dyn. 2024;53(2):101056. DOI: https://doi.org/10.1016/j.orgdyn.2024.101056
6. Yin Y, Wang X, Wang H, Lu B. Application of edge computing and IoT technology in supply chain finance. Alexandria Eng J. 2024;108(August):754–63. DOI: https://doi.org/10.1016/j.aej.2024.09.016
7. Gallego-García S, Gallego-García D, García-García M. Sustainability in the agri-food supply chain: a combined digital twin and simulation approach for farmers. Procedia Comput Sci. 2022;217(2022):1280–95. DOI: https://doi.org/10.1016/j.procs.2022.12.326
8. Pattnaik D, Ray S, Raman R. Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon. 2024;10(1):e23492. DOI: https://doi.org/10.1016/j.heliyon.2023.e23492
9. Emrouznejad A, Abbasi S, Sıcakyüz Ç. Supply chain risk management: A content analysis-based review of existing and emerging topics. Supply Chain Anal. 2023;3(August). DOI: https://doi.org/10.1016/j.sca.2023.100031
10. Abioye SO, Oyedele LO, Akanbi L, Ajayi A, Davila Delgado JM, Bilal M, et al. Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. J Build Eng. 2021;44(April 2020):103299. DOI: https://doi.org/10.1016/j.jobe.2021.103299
11. Guo Y, Liu F, Song JS, Wang S. Supply chain resilience: A review from the inventory management perspective. Fundam Res. 2024;(xxxx). DOI: https://doi.org/10.1016/j.fmre.2024.08.002
12. Zhang J, Cai K, Wen J. A survey of deep learning applications in cryptocurrency. iScience. 2024;27(1):108509. DOI: https://doi.org/10.1016/j.isci.2023.108509
13. Attah-Boakye R, Adams K, Hernandez-Perdomo E, Yu H, Johansson J. Resource Re-orchestration and firm survival in crisis periods: The role of business models of technology MNEs during COVID-19. Technovation. 2023;125(May):102769. DOI: https://doi.org/10.1016/j.technovation.2023.102769
14. Citterio A. Bank failure prediction models: Review and outlook. Socioecon Plann Sci. 2024;92(March 2023):101818. DOI: https://doi.org/10.1016/j.seps.2024.101818
15. Zhu W, Ouyang P, Kong M. Research on the evolution mechanism of intelligent manufacturing transformation of Chinese pharmaceutical manufacturing enterprises based on system dynamics. Heliyon. 2024;10(13):e33959. DOI: https://doi.org/10.1016/j.heliyon.2024.e33959
16. Nissim D. Big data, accounting information, and valuation. J Financ Data Sci. 2022;8:69–85. DOI: https://doi.org/10.1016/j.jfds.2022.04.003
17. Voorneveld M, de Groot M. Optimal investment strategy on data analytics capabilities of startups via Markov decision analysis. Decis Anal J. 2024;10(February):100438. DOI: https://doi.org/10.1016/j.dajour.2024.100438
18. Cheng X, Liu S, Sun X, Wang Z, Zhou H, Shao Y, et al. Combating emerging financial risks in the big data era: A perspective review. Fundam Res. 2021;1(5):595–606. DOI: https://doi.org/10.1016/j.fmre.2021.08.017
19. Doumpos M, Zopounidis C, Gounopoulos D, Platanakis E, Zhang W. Operational research and artificial intelligence methods in banking. Eur J Oper Res. 2023;306(1):1–16. DOI: https://doi.org/10.1016/j.ejor.2022.04.027
20. Filip FG, Shi Y, Pocatilu P, Ciurea C, Li J, Tien JM, et al. ScienceDirect Table of Contents. 2024;242. DOI: https://doi.org/10.1016/j.procs.2024.08.092
21. Zeb A, Kortelainen J, Rantala T, Saunila M, Ukko J. On the alleviation of imminent technical and business challenges of long-lasting functional digital twins. Comput Ind. 2022;141:103701. DOI: https://doi.org/10.1016/j.compind.2022.103701
22. Byrapu Reddy SR, Kanagala P, Ravichandran P, Pulimamidi DR, Sivarambabu PV, Polireddi NSA. Effective fraud detection in e-commerce: Leveraging machine learning and big data analytics. Meas Sensors. 2024;33(March):101138. DOI: https://doi.org/10.1016/j.measen.2024.101138
23. Singh V, Chen S-S, Singhania M, Nanavati B, kar A kumar, Gupta A. How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda. Int J Inf Manag Data Insights. 2022;2(2):100094. DOI: https://doi.org/10.1016/j.jjimei.2022.100094
24. Ranta M, Ylinen M. Employee benefits and company performance: Evidence from a high-dimensional machine learning model. Manag Account Res. 2024;(March 2022):100876. DOI: https://doi.org/10.1016/j.mar.2023.100876
25. Freeman S, Vissak T, Nummela N, Trudgen R. Do technology-focused fast internationalizers’ performance measures change as they mature? Int Bus Rev. 2023;32(5). DOI: https://doi.org/10.1016/j.ibusrev.2023.102168
26. Musarat MA, Alaloul WS, Khan MHF, Ayub S, Guy CPL. Evaluating cloud computing in construction projects to avoid project delay. J Open Innov Technol Mark Complex. 2024;10(2):100296. DOI: https://doi.org/10.1016/j.joitmc.2024.100296
27. Kumar A, Naz F, Luthra S, Vashistha R, Kumar V, Garza-Reyes JA, et al. Digging DEEP: Futuristic building blocks of omni-channel healthcare supply chains resiliency using machine learning approach. J Bus Res. 2023;162(March 2022):113903. DOI: https://doi.org/10.1016/j.jbusres.2023.113903
28. Murphy B, Feeney O, Rosati P, Lynn T. Exploring accounting and AI using topic modelling. Int J Account Inf Syst. 2024;55(July 2023):100709. DOI: https://doi.org/10.1016/j.accinf.2024.100709
29. Parhizkar H, Taddei P, Weziak-bialowolska D, Mcneely E, Spengler D, Guillermo J, et al. Jo ur l P re of. Build Environ. 2023;110984. DOI: https://doi.org/10.1016/j.buildenv.2023.110984
30. Shahroodi K, Avakh Darestani S, Soltani S, Eisazadeh Saravani A. Developing strategies to retain organizational insurers using a clustering technique: Evidence from the insurance industry. Technol Forecast Soc Change. 2024;201(March 2023):123217. DOI: https://doi.org/10.1016/j.techfore.2024.123217
31. Sanga B, Aziakpono M. FinTech and SMEs financing: A systematic literature review and bibliometric analysis. Digit Bus. 2023;3(2):100067. DOI: https://doi.org/10.1016/j.digbus.2023.100067
32. Faltein SA, Sukdeo NI. Culture-driven quality enhancement: Uncovering the impact of robotics integration in the South African construction sector. Ain Shams Eng J. 2024;15(6):102728. DOI: https://doi.org/10.1016/j.asej.2024.102728
33. Yang J, Amrollahi A, Marrone M. Harnessing the Potential of Artificial Intelligence: Affordances, Constraints, and Strategic Implications for Professional Services. J Strateg Inf Syst. 2024;33(4):101864. DOI: https://doi.org/10.1016/j.jsis.2024.101864
34. Veríssimo C, Pereira L, Fernandes A, Martinho R. Complex problem solving as a source of competitive advantage. J Open Innov Technol Mark Complex. 2024;10(2). DOI: https://doi.org/10.1016/j.joitmc.2024.100258
35. Amato A, Osterrieder JR, Machado MR. How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review. Int J Inf Manag Data Insights. 2024;4(2):100234. DOI: https://doi.org/10.1016/j.jjimei.2024.100234
36. Johnston C, Pratt G. Automating adult social care in the UK: Extracting value from a crisis. Geoforum. 2024;151(April):103997. DOI: https://doi.org/10.1016/j.geoforum.2024.103997
37. KV DS. Neural network-based liquidity risk prediction in Indian private banks. Intell Syst with Appl. 2024;21(December 2023):200322. DOI: https://doi.org/10.1016/j.iswa.2023.200322
38. Huang F, No WG, Vasarhelyi MA, Yan Z. Audit data analytics, machine learning, and full population testing. J Financ Data Sci. 2022;8:138–44. DOI: https://doi.org/10.1016/j.jfds.2022.05.002
39. Muparuri L, Gumbo V. On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates. Sustain Anal Model. 2022;2(May):100006. DOI: https://doi.org/10.1016/j.samod.2022.100006
40. Afriyie JK, Tawiah K, Pels WA, Addai-Henne S, Dwamena HA, Owiredu EO, et al. A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decis Anal J. 2023;6(November 2022):100163. DOI: https://doi.org/10.1016/j.dajour.2023.100163
41. Hajek P, Henriques R. Predicting M&A targets using news sentiment and topic detection. Technol Forecast Soc Change. 2024;201(March 2023):123270. DOI: https://doi.org/10.1016/j.techfore.2024.123270
42. Cooke P. Questionable relations: On aggressive financialised ‘assemblages’ in creative and ecologically-challenged space-economies. J Open Innov Technol Mark Complex. 2024;10(2):100293. DOI: https://doi.org/10.1016/j.joitmc.2024.100293
43. Najem R, Amr MF, Bahnasse A, Talea M. Artificial Intelligence for Digital Finance, Axes and Techniques. Procedia Comput Sci. 2022;203(2021):633–8. DOI: https://doi.org/10.1016/j.procs.2022.07.092
44. Allen F, Barbalau A. Security design: A review. J Financ Intermediation. 2024;60(December 2023):101113. DOI: https://doi.org/10.1016/j.jfi.2024.101113
45. Esposito P, Marrasso E, Martone C, Pallotta G, Roselli C, Sasso M, et al. A roadmap for the implementation of a renewable energy community. Heliyon. 2024;10(7):e28269. DOI: https://doi.org/10.1016/j.heliyon.2024.e28269
46. Zhao J, Ouenniche J, De Smedt J. Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction. Mach Learn with Appl. 2024;15(January):100527. DOI: https://doi.org/10.1016/j.mlwa.2024.100527
47. Sun X, Zheng C, Wandelt S, Zhang A. Airline competition: A comprehensive review of recent research. J Air Transp Res Soc. 2024;2:100013. DOI: https://doi.org/10.1016/j.jatrs.2024.100013
48. Pham TN, Powell R, Bannigidadmath D. Tail risk network analysis of Asian banks. Glob Financ J. 2024;62(October 2023):101017. DOI: https://doi.org/10.1016/j.gfj.2024.101017
49. Bilotto F, Harrison MT, Vibart R, Mackay A, Christie-Whitehead KM, Ferreira CSS, et al. Towards resilient, inclusive, sustainable livestock farming systems. Trends Food Sci Technol. 2024;152(July 2023):104668. DOI: https://doi.org/10.1016/j.tifs.2024.104668
50. Giwa AS, Maurice NJ, Luoyan A, Liu X, Yunlong Y, Hong Z. Advances in sewage sludge application and treatment: Process integration of plasma pyrolysis and anaerobic digestion with the resource recovery. Heliyon. 2023;9(9):e19765. DOI: https://doi.org/10.1016/j.heliyon.2023.e19765
51. Armenia S, Franco E, Iandolo F, Maielli G, Vito P. Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management. Technol Forecast Soc Change. 2024;200(December 2022):123131. DOI: https://doi.org/10.1016/j.techfore.2023.123131
52. Nuryanto UW, Basrowi, Quraysin I, Pratiwi I. Magnitude of digital adaptability role: Stakeholder engagement and costless signaling in enhancing sustainable MSME performance. Heliyon [Internet]. 2024;10(13):e33484. Tersedia pada: https://doi.org/10.1016/j.heliyon.2024.e33484 DOI: https://doi.org/10.1016/j.heliyon.2024.e33484
53. Himmatul I, Nugroho I, Mardian T, Syakina D, Suryo A, Sutoto A, et al. Uncertain Supply Chain Management Enhancing company performance and profitability through agile practices : A comprehensive analysis of three key perspectives. 2024;12:1205–24. DOI: https://doi.org/10.5267/j.uscm.2023.11.014
54. Ayamga M, Annosi MC, Kassahun A, Dolfsma W, Tekinerdogan B. Adaptive organizational responses to varied types of failures: Empirical insights from technology providers in Ghana. Technovation. 2024;129(August 2023):102887. DOI: https://doi.org/10.1016/j.technovation.2023.102887
55. Lisaria R, Prapanca D, Amatul S, Arifin K. Uncertain Supply Chain Management Forging a resilient pathway : Uncovering the relationship between the supply chain sustainability and the tax compliance , and the sustainable future of the micro , small , and medium enterprise. 2024;12:1097–112. DOI: https://doi.org/10.5267/j.uscm.2023.11.023
56. Himmatul I, Junaedi A. International Journal of Data and Network Science Understanding Roblox ’ s business model and collaborative learning on participation in the deci- sion-making process : implications for enhancing cooperative literacy. 2024;8:1247–60. DOI: https://doi.org/10.5267/j.ijdns.2023.11.009
57. Shofwa Y, Hadi R, Isna A, Amaludin A. Uncertain Supply Chain Management Harmonization of social capital and philanthropic culture : A catalyst for smooth household supply chains and successful economic development. 2024;12:1053–64. DOI: https://doi.org/10.5267/j.uscm.2023.12.003
58. Kharis A, Masyhari A, Suci W, Priatnasari Y. Uncertain Supply Chain Management Optimizing state revenue through government-driven supply chain efficiency and fair corporate taxation practices. 2024;12:659–68. DOI: https://doi.org/10.5267/j.uscm.2024.1.018
59. Nuryanto UW, Basrowi, Quraysin I, Pratiwi I. Environmental management control system, blockchain adoption, cleaner production, and product efficiency on environmental reputation and performance: Empirical evidence from Indonesia. Sustain Futur [Internet]. 2024;7:100190. Tersedia pada: https://www.sciencedirect.com/science/article/pii/S2666188824000406 DOI: https://doi.org/10.1016/j.sftr.2024.100190
60. Saeri M, Burhansyah R, Kilmanun JC, Hanif Z. Uncertain Supply Chain Management Strategic resilience : Integrating scheduling , supply chain management , and advanced operations techniques in production risk analysis and technical efficiency of rice farming in flood-prone areas. 2024;12:1065–82. DOI: https://doi.org/10.5267/j.uscm.2023.12.002
61. Suseno BD, Sutisna, Hidyat S, Basrowi. Halal supply chain and halal tourism industry in forming economic growth Bambang. Uncertain Supply Chain Manag. 2018;6(4):407–22.
62. Basrowi B, Utami P. Building Strategic Planning Models Based on Digital Technology in the Sharia Capital Market. J Adv Res Law Econ Vol 11 No 3 JARLE Vol XI Issue 3(49) Summer 2020DO - 1014505/jarle.v113(49)06 [Internet]. 15 Juni 2020; Tersedia pada: https://journals.aserspublishing.eu/jarle/article/view/5154
63. Soenyono S, Basrowi B. Form and Trend of Violence against Women and the Legal Protection Strategy. Int J Adv Sci Technol [Internet]. 25 April 2020;29(05 SE-Articles):3165–74. Tersedia pada: http://sersc.org/journals/index.php/IJAST/article/view/11636
64. Marwanto IGGH, Basrowi B, Suwarno S. The Influence of Culture and Social Structure on Political Behavior in the Election of Mayor of Kediri Indonesia. Int J Adv Sci Technol [Internet]. 15 April 2020;29(05 SE-Articles):1035–47. Tersedia pada: http://sersc.org/journals/index.php/IJAST/article/view/9759
65. Basrowi B, Maunnah B. The Challenge of Indonesian Post Migrant Worker’s Welfare. J Adv Res Law Econ Vol 10 No 4 JARLE Vol X Issue 4(42) Summer 2019DO - 1014505//jarle.v104(42)07 [Internet]. 30 Juni 2019; Tersedia pada: https://journals.aserspublishing.eu/jarle/article/view/4716
66. Basrowi B, Utami P. Development of Market Distribution through Digital Marketing Transformation Trends to Maximize Sales Turnover for Traditional Beverage Products. J Distrib Sci. 2023;21(8):57–68.
67. Suwarno Basrowi, IGGHM. Technology of Qualitative Analysis to Understand Community Political Behaviors in Regional Head Election in Wates District, Kediri, Indonesia. Int J Adv Sci Technol [Internet]. 23 April 2020;29(05 SE-Articles):2624–35. Tersedia pada: http://sersc.org/journals/index.php/IJAST/article/view/11159
68. Marwanto IGGH, Basrowi, Suwarno. The Influence of Culture and Social Structure on Political Behavior in the Election of Mayor of Kediri Indonesia. Int J Adv Sci Technol [Internet]. 15 April 2020;29(05 SE-Articles):1035–47. Tersedia pada: http://sersc.org/journals/index.php/IJAST/article/view/9759
69. Kittie S, Basrowi B. Environmental education using SARITHA-Apps to enhance environmentally friendly supply chain efficiency and foster environmental knowledge towards sustainability. Uncertain Supply Chain Manag. 2024;12(1):359–72. DOI: https://doi.org/10.5267/j.uscm.2023.9.015
70. Junaidi A, Masdar A Zum, Basrowi B, Robiatun D, Situmorang JW, Lukas A, et al. Uncertain Supply Chain Management Enhancing sustainable soybean production in Indonesia : evaluating the environmental and economic benefits of MIGO technology for integrated supply chain sustainability. Uncertain Supply Chain Manag. 2024;12(1):221–34. DOI: https://doi.org/10.5267/j.uscm.2023.10.003
71. Miar M, Rizani A, Pardede RL, Basrowi B. Analysis of the effects of capital expenditure and supply chain on economic growth and their implications on the community welfare of districts and cities in central Kalimantan province. Uncertain Supply Chain Manag. 2024;12(1):489–504. DOI: https://doi.org/10.5267/j.uscm.2023.9.003
72. Hadi R, Shafrani YS, Hilyatin DL, Riyadi S, Basrowi B. Digital zakat management, transparency in zakat reporting, and the zakat payroll system toward zakat management accountability and its implications on zakat growth acceleration. Int J Data Netw Sci. 2019;8(1):103–8. DOI: https://doi.org/10.5267/j.ijdns.2023.8.025
73. Purwaningsih E, Muslikh M, Suhaeri S, Basrowi B. Utilizing blockchain technology in enhancing supply chain efficiency and export performance , and its implications on the financial performance of SMEs. Uncertain Supply Chain Manag. 2024;12(1):449–60. DOI: https://doi.org/10.5267/j.uscm.2023.9.007
74. Nuryanto UW, Basrowi B, Quraysin I. Big data and IoT adoption in shaping organizational citizenship behavior: The role of innovation organiza- tional predictor in the chemical manufacturing industry. Int J Data Netw Sci. 2019;8(1):103–8. DOI: https://doi.org/10.5267/j.ijdns.2023.9.026
75. Hamdan H, Basrowi B. Do community entrepreneurial development shape the sustainability of tourist villages? Hamdana*. Uncertain Supply Chain Manag. 2024;12(1):407–22. DOI: https://doi.org/10.5267/j.uscm.2023.9.014
76. Mulyani S, Basrowi B. The effect of environmentally oriented leadership and public sector management quality on supply chain performance : The moderating role of public sector environmental policy. Uncertain Supply Chain Manag. 2024;12:471–80. DOI: https://doi.org/10.5267/j.uscm.2023.9.005
77. Alexandro R, Basrowi B. Measuring the effectiveness of smart digital organizations on digital technology adoption : An em- pirical study of educational organizations in Indonesia. Int J Data Netw Sci. 2024;8(1):139–50. DOI: https://doi.org/10.5267/j.ijdns.2023.10.009
78. Yusuf ZFA, Yusuf FA, Nuryanto UW, Basrowi B. Assessing organizational commitment and organizational citizenship behavior in ensuring the smoothness of the supply chain for medical hospital needs towards a green hospital : Evidence from Indonesia. Uncertain Supply Chain Manag. 2024;12(1):181–94. DOI: https://doi.org/10.5267/j.uscm.2023.10.006
79. Junaidi A, Basrowi B, Sabtohadi J, Wibowo AM, Wiboho SS, Asgar A, et al. The role of public administration and social media educational socialization in influencing public satisfaction on population services : The mediating role of population literacy awareness. Int J Data Netw Sci. 2024;8(1):345–56. DOI: https://doi.org/10.5267/j.ijdns.2023.9.019
80. Alexandro R, Basrowi B. The influence of macroeconomic infrastructure on supply chain smoothness and national competitiveness and its implications on a country ’ s economic growth : evidence from BRICS. Uncertain Supply Chain Manag. 2024;12(1):167–80. DOI: https://doi.org/10.5267/j.uscm.2023.10.007
81. Beemamol M. Mapping the trends of Financial Statement Fraud detection research from the historical roots and seminal work. J Econ Criminol. 2024;6(July):100096. DOI: https://doi.org/10.1016/j.jeconc.2024.100096
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