From Financial Stewardship to AI-Enabled Strategic Intelligence: Reimagining Finance for A Sustainable Indonesian Palm Oil Supply Chain
Downloads
Artificial intelligence is changing the finance function from a predominantly transactional, reporting, and control-oriented activity into a strategic intelligence capability that can combine financial, operational, geospatial, environmental, and supply-chain information. This transformation is particularly relevant to Indonesia's palm oil sector, where large corporate plantations, independent smallholders, cooperatives, mills, refiners, traders, financial institutions, and international buyers operate within a complex network characterised by commodity-price volatility, heterogeneous technological capabilities, sustainability requirements, traceability challenges, and unequal access to finance. This qualitative literature review examines the strategic role of finance in the artificial-intelligence era and synthesises the potential benefits, implications, implementation challenges, and governance requirements of AI-enabled finance within the Indonesian palm oil supply chain. The synthesis identifies major opportunities in financial planning and analysis, cash-flow forecasting, risk management, fraud detection, working-capital optimisation, smallholder credit assessment, capital budgeting, supply-chain finance, traceability, and sustainability-linked decision-making. However, technological value is conditional on data quality, organisational capabilities, human judgement, explainability, accountability, cybersecurity, institutional coordination, and safeguards against algorithmic exclusion. The article proposes an integrated framework in which finance acts as an organisational orchestration layer connecting AI-generated intelligence with capital allocation, risk governance, and sustainable supply-chain outcomes. A staged implementation pathway is recommended, beginning with data foundations and quick wins before advancing toward predictive finance, cross-functional decision intelligence, and ecosystem-level sustainability orchestration.
Adwiyah, R., Syaukat, Y., Indrawan, D. and Mulyati, H. (2023) ‘Examining sustainable supply chain management performance in the palm oil industry with the Triple Bottom Line approach’, Sustainability, 15(18), 13362. Available at:
https://doi.org/10.3390/su151813362.
Akhtar, M.N., Ansari, E., Alhady, S.S.N. and Abu Bakar, E. (2023) ‘Leveraging on advanced remote sensing- and artificial intelligence-based technologies to manage palm oil plantation for current global scenario: A review’, Agriculture, 13(2), 504. Available at:
https://doi.org/10.3390/agriculture13020504.
Almufadda, G. and Almezeini, N.A. (2022) ‘Artificial intelligence applications in the auditing profession: A literature review’, Journal of Emerging Technologies in Accounting, 19(2), pp. 29–42. Available at:
https://doi.org/10.2308/JETA-2020-083.
Al-Sartawi, A.M.A.M., Hussainey, K. and Razzaque, A. (2022) ‘The role of artificial intelligence in sustainable finance’, Journal of Sustainable Finance & Investment. Available at:
https://doi.org/10.1080/20430795.2022.2057405.
Apriani, E., Kim, Y.-S., Fisher, L.A. and Baral, H. (2020) ‘Non-state certification of smallholders for sustainable palm oil in Sumatra, Indonesia’, Land Use Policy, 99, 105112. Available at:
https://doi.org/10.1016/j.landusepol.2020.105112.
Attard-Frost, B., De los Ríos, A. and Walters, D.R. (2023) ‘The ethics of AI business practices: A review of 47 AI ethics guidelines’, AI and Ethics, 3, pp. 389–406. Available at:
https://doi.org/10.1007/s43681-022-00156-6.
Babina, T., Fedyk, A., He, A. and Hodson, J. (2024) ‘Artificial intelligence, firm growth, and product innovation’, Journal of Financial Economics, 151, 103745. Available at:
https://doi.org/10.1016/j.jfineco.2023.103745.
Bahoo, S., Cucculelli, M., Goga, X. and Mondolo, J. (2024) ‘Artificial intelligence in finance: A comprehensive review through bibliometric and content analysis’, SN Business & Economics, 4, 23. Available at:
https://doi.org/10.1007/s43546-023-00618-x.
Birkstedt, T., Minkkinen, M., Tandon, A. and Mäntymäki, M. (2023) ‘AI governance: Themes, knowledge gaps and future agendas’, Internet Research, 33(7), pp. 133–167. Available at: https://doi.org/10.1108/INTR-01-2022-0042.
Brandi, C. (2021) ‘The interaction of private and public governance: The case of sustainability standards for palm oil’, European Journal of Development Research, 33, pp. 1574–1595. Available at:
https://doi.org/10.1057/s41287-020-00306-8.
Bussmann, N., Giudici, P., Marinelli, D. and Papenbrock, J. (2020) ‘Explainable AI in Fintech risk management’, Frontiers in Artificial Intelligence, 3, 26. Available at:
https://doi.org/10.3389/frai.2020.00026.
Bussmann, N., Giudici, P., Marinelli, D. and Papenbrock, J. (2021) ‘Explainable machine learning in credit risk management’, Computational Economics, 57, pp. 203–216. Available at: https://doi.org/10.1007/s10614-020-10042-0.
Chen, J., Katchova, A.L. and Zhou, C. (2021) ‘Agricultural loan delinquency prediction using machine learning methods’, International Food and Agribusiness Management Review, 24(5), pp. 797–812. Available at:
https://doi.org/10.22434/IFAMR2020.0019.
Choiruzzad, S.A.B., Tyson, A. and Varkkey, H. (2021) ‘The ambiguities of Indonesian Sustainable Palm Oil certification: Internal incoherence, governance rescaling and state transformation’, Asia Europe Journal, 19, pp. 189–208. Available at: https://doi.org/10.1007/s10308-020-00593-0.
Craja, P., Kim, A. and Lessmann, S. (2020) ‘Deep learning for detecting financial statement fraud’, Decision Support Systems, 139, 113421. Available at: https://doi.org/10.1016/j.dss.2020.113421.
de Vos, R.E., Suwarno, A., Slingerland, M., van der Meer, P.J. and Lucey, J.M. (2023) ‘Pre-certification conditions of independent oil palm smallholders in Indonesia: Assessing prospects for RSPO certification’, Land Use Policy, 130, 106660. Available at:
https://doi.org/10.1016/j.landusepol.2023.106660.
Dong, M.M., Stratopoulos, T.C. and Wang, V.X. (2024) ‘A scoping review of ChatGPT research in accounting and finance’, International Journal of Accounting Information Systems, 55, 100715. Available at:
https://doi.org/10.1016/j.accinf.2024.100715.
Eisfeldt, A.L. and Schubert, G. (2025) ‘Generative AI and finance’, Annual Review of Financial Economics, 17, pp. 363–393. Available at:
https://doi.org/10.1146/annurev-financial-112923-020503.
Elhady, A.M. and Shohieb, S. (2025) ‘AI-driven sustainable finance: computational tools, ESG metrics, and global implementation’, Future Business Journal, 11, 209. Available at:
https://doi.org/10.1186/s43093-025-00610-x.
El Hathat, Z., Venkatesh, V.G., Zouadi, T., Sreedharan, V.R., Manimuthu, A. and Shi, Y. (2023) ‘Analyzing the greenhouse gas emissions in the palm oil supply chain in the VUCA world: A blockchain initiative’, Business Strategy and the Environment, 32(8), pp. 5563–5582. Available at: https://doi.org/10.1002/bse.3436.
Enholm, I.M., Papagiannidis, E., Mikalef, P. and Krogstie, J. (2022) ‘Artificial intelligence and business value: A literature review’, Information Systems Frontiers, 24, pp. 1709–1734. Available at: https://doi.org/10.1007/s10796-021-10186-w.
Fedyk, A., Hodson, J., Khimich, N. and Fedyk, T. (2022) ‘Is artificial intelligence improving the audit process?’, Review of Accounting Studies, 27, pp. 938–985. Available at:
https://doi.org/10.1007/s11142-022-09697-x.
Fügener, A., Grahl, J., Gupta, A. and Ketter, W. (2022) ‘Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation’, Information Systems Research, 33(2), pp. 678–696. Available at:
https://doi.org/10.1287/isre.2021.1079.
Fritz-Morgenthal, S., Hein, B. and Papenbrock, J. (2022) ‘Financial risk management and explainable, trustworthy, responsible AI’, Frontiers in Artificial Intelligence, 5, 779799. Available at:
https://doi.org/10.3389/frai.2022.779799.
Giudici, P. and Raffinetti, E. (2023) ‘SAFE artificial intelligence in finance’, Finance Research Letters, 56, 104088. Available at:
https://doi.org/10.1016/j.frl.2023.104088.
Giudici, P. and Wu, L. (2025) ‘Sustainable artificial intelligence in finance: impact of ESG factors’, Frontiers in Artificial Intelligence, 8, 1566197. Available at:
https://doi.org/10.3389/frai.2025.1566197.
Glader, M. and Strömsten, T. (2020) ‘Digitalization of the finance function’, Controlling & Management Review, 64(6), pp. 64–67. Available at:
https://doi.org/10.1007/s12176-020-0128-0.
Grabs, J. and Garrett, R.D. (2023) ‘Goal-based private sustainability governance and its paradoxes in the Indonesian palm oil sector’, Journal of Business Ethics, 188, pp. 467–507. Available at:
https://doi.org/10.1007/s10551-023-05377-1.
Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T. and Osterrieder, J. (2021) ‘Audience-dependent explanations for AI-based risk management tools: A survey’, Frontiers in Artificial Intelligence, 4, 794996. Available at:
https://doi.org/10.3389/frai.2021.794996.
Hadley, E., Blatecky, A. and Comfort, M. (2025) ‘Investigating algorithm review boards for organizational responsible artificial intelligence governance’, AI and Ethics, 5, pp. 2485–2495. Available
at: https://doi.org/10.1007/s43681-024-00574-8.
Harrast, S.A. (2020) ‘Robotic process automation in accounting systems’, Journal of Corporate Accounting & Finance, 31(4), pp. 209–213. Available at: https://doi.org/10.1002/jcaf.22457.
Hilal, Y.Y. et al. (2021) ‘Neural networks method in predicting oil palm FFB yields for the Peninsular States of Malaysia’, Journal of Oil Palm Research, 33(3), pp. 400–412. Available at:
https://doi.org/10.21894/jopr.2020.0105.
Imbiri, S., Rameezdeen, R., Chileshe, N. and Statsenko, L. (2023) ‘Stakeholder perspectives on supply chain risks: The case of Indonesian palm oil industry in West Papua’, Sustainability, 15(12), 9605. Available at:
https://doi.org/10.3390/su15129605.
Jain, R., Garg, N. and Khera, S.N. (2023) ‘Effective human–AI work design for collaborative decision-making’, Kybernetes, 52(11), pp. 5017–5040. Available at: https://doi.org/10.1108/K-04-2022-0548.
Khan, N., Kamaruddin, M.A., Sheikh, U.U., Yusup, Y. and Bakht, M.P. (2021) ‘Oil palm and machine learning: Reviewing one decade of ideas, innovations, applications, and gaps’, Agriculture, 11(9), 832. Available at:
https://doi.org/10.3390/agriculture11090832.
Kluge Corrêa, N., Galvão, C., Santos, J.W., Del Pino, C., Pinto, E.P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E. and de Oliveira, N. (2023) ‘Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance’, Patterns, 4(10), 100857. Available at: https://doi.org/10.1016/j.patter.2023.100857.
Lehner, O.M., Ittonen, K., Silvola, H., Ström, E. and Wührleitner, A. (2022) ‘Artificial intelligence based decision-making in accounting and auditing: Ethical challenges and normative thinking’, Accounting, Auditing & Accountability Journal, 35(9), pp. 109–135. Available at: https://doi.org/10.1108/AAAJ-09-2020-4934.
Li, J.-P., Mirza, N., Rahat, B. and Xiong, D. (2020) ‘Machine learning and credit ratings prediction in the age of fourth industrial revolution’, Technological Forecasting and Social Change, 161, 120309. Available at:
https://doi.org/10.1016/j.techfore.2020.120309.
Li, Y., Zhong, H. and Tong, Q. (2024) ‘Artificial intelligence, dynamic capabilities, and corporate financial asset allocation’, International Review of Financial Analysis, 96, 103773. Available at:
https://doi.org/10.1016/j.irfa.2024.103773.
Mathen, M.P. (2025) ‘Toward an evolving framework for responsible AI for credit scoring in the banking industry’, Journal of Information, Communication and Ethics in Society, 23(1), pp. 148–163. Available at:
https://doi.org/10.1108/JICES-08-2024-0122.
Mehraban, N., Kubitza, C., Alamsyah, Z. and Qaim, M. (2021) ‘Oil palm cultivation, household welfare, and exposure to economic risk in the Indonesian small farm sector’, Journal of Agricultural Economics, 72(3), pp. 901–915. Available at:
https://doi.org/10.1111/1477-9552.12433.
Mikalef, P. and Gupta, M. (2021) ‘Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance’, Information & Management, 58(3), 103434. Available at:
https://doi.org/10.1016/j.im.2021.103434.
Mökander, J. (2023) ‘Auditing of AI: Legal, ethical and technical approaches’, Digital Society, 2, 49. Available at: https://doi.org/10.1007/s44206-023-00074-y.
Mökander, J. and Floridi, L. (2023) ‘Operationalising AI governance through ethics-based auditing: An industry case study’, AI and Ethics, 3, pp. 451–468. Available at:
https://doi.org/10.1007/s43681-022-00171-7.
Möller, K., Schäffer, U. and Verbeeten, F. (2020) ‘Digitalization in management accounting and control: An editorial’, Journal of Management Control, 31, pp. 1–8. Available at:
https://doi.org/10.1007/s00187-020-00300-5.
Mäntymäki, M., Minkkinen, M., Birkstedt, T. and Viljanen, M. (2022) ‘Defining organizational AI governance’, AI and Ethics, 2, pp. 603–609. Available at: https://doi.org/10.1007/s43681-022-00143-x.
Munoko, I., Brown-Liburd, H.L. and Vasarhelyi, M. (2020) ‘The ethical implications of using artificial intelligence in auditing’, Journal of Business Ethics, 167, pp. 209–234. Available at:
https://doi.org/10.1007/s10551-019-04407-1.
Naz, F., Agrawal, R., Kumar, A., Gunasekaran, A., Majumdar, A. and Luthra, S. (2022) ‘Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions’, Business Strategy and the Environment, 31(5), pp. 2400–2423. Available at: https://doi.org/10.1002/bse.3034.
Papagiannidis, E., Mikalef, P. and Conboy, K. (2025) ‘Responsible artificial intelligence governance: A review and research framework’, Journal of Strategic Information Systems, 34(2), 101885. Available at: https://doi.org/10.1016/j.jsis.2024.101885.
Pramudya, E.P., Wibowo, L.R., Nurfatriani, F., Nawireja, I.K., Kurniasari, D.R., Hutabarat, S., Kadarusman, Y.B., Iswardhani, A.O. and Rafik, R. (2022) ‘Incentives for palm oil smallholders in mandatory certification in Indonesia’, Land, 11(4), 576. Available at: https://doi.org/10.3390/land11040576.
Pribadi, D.O., Rustiadi, E., Iman, L.O.S., Nurdin, M., Supijatno, Saad, A., Pravitasari, A.E., Mulya, S.P. and Ermyanyla, M. (2023) ‘Mapping smallholder plantation as a key to sustainable oil palm: A deep learning approach to high-resolution satellite imagery’, Applied Geography, 153, 102921. Available at: https://doi.org/10.1016/j.apgeog.2023.102921.
Puranam, P. (2021) ‘Human–AI collaborative decision-making as an organization design problem’, Journal of Organization Design, 10, pp. 75–80. Available at: https://doi.org/10.1007/s41469-021-00095-2.
Putri, E.I.K. et al. (2022) ‘The oil palm governance: Challenges of sustainability policy in Indonesia’, Sustainability, 14(3), 1820. Available at: https://doi.org/10.3390/su14031820.
Raisch, S. and Fomina, K. (2024) ‘Combining human and artificial intelligence: Hybrid problem-solving in organizations’, Academy of Management Review. Available at: https://doi.org/10.5465/amr.2021.0421.
Ramli, U.S. et al. (2020) ‘Sustainable palm oil—The role of screening and advanced analytical techniques for geographical traceability and authenticity verification’, Molecules, 25(12), 2927. Available at: https://doi.org/10.3390/molecules25122927.
Rashid, M., Bari, B.S., Yusup, Y., Kamaruddin, M.A. and Khan, N. (2021) ‘A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction’, IEEE Access, 9, pp. 63406–63439. Available at: https://doi.org/10.1109/ACCESS.2021.3075159.
Reich, C. and Mußhoff, O. (2025) ‘Oil palm smallholders and the road to certification: Insights from Indonesia’, Journal of Environmental Management, 375, 124303. Available at: https://doi.org/10.1016/j.jenvman.2025.124303.
Rikhardsson, P., Thórisson, K.R., Bergthorsson, G. and Batt, C. (2022) ‘Artificial intelligence and auditing in small- and medium-sized firms: Expectations and applications’, AI Magazine, 43, pp. 323–336. Available at: https://doi.org/10.1002/aaai.12066.
Rosalina, L., Kartodiharjo, H. and Sudjito, A. (2023) ‘Sustainable finance in financing plantation companies by banking: Case study of palm oil corporation in Donggala Central Sulawesi’, Journal of Natural Resources and Environmental Management, 13(2), pp. 290–304. Available at: https://doi.org/10.29244/jpsl.13.2.290-304.
Santika, T. et al. (2021) ‘Impact of palm oil sustainability certification on village well-being and poverty in Indonesia’, Nature Sustainability, 4, pp. 109–119. Available at: https://doi.org/10.1038/s41893-020-00630-1.
Stratopoulos, T.C. and Wang, V.X. (2025) ‘Artificial intelligence and accounting research: a framework and agenda’, International Journal of Accounting Information Systems, 56, 100760. Available at: https://doi.org/10.1016/j.accinf.2025.100760.
Suhardjo, I., Akroyd, C. and Suparman, M. (2024) ‘Unpacking environmental, social, and governance score disparity: A study of Indonesian palm oil companies’, Journal of Risk and Financial Management, 17(7), 296. Available at: https://doi.org/10.3390/jrfm17070296.
Tapia, J.F.D., Doliente, S.S. and Samsatli, S. (2021) ‘How much land is available for sustainable palm oil?’, Land Use Policy, 102, 105187. Available at: https://doi.org/10.1016/j.landusepol.2020.105187.
Tey, Y.S., Brindal, M.K., Abdul Hadi, A.H.I. and Darham, S. (2022) ‘Financial costs and benefits of the Roundtable on Sustainable Palm Oil certification among independent smallholders: A probabilistic view of the Monte Carlo approach’, Sustainable Production and Consumption, 30, pp. 377–386. Available at: https://doi.org/10.1016/j.spc.2021.12.020.
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P. and Fischl, M. (2021) ‘Artificial intelligence in supply chain management: A systematic literature review’, Journal of Business Research, 122, pp. 502–517. Available at: https://doi.org/10.1016/j.jbusres.2020.09.009.
Wahid, W.W.C., Aprillia, K.R., Herdiansyah, H., Ramatia, D., Setiawati, N., Subkhi, S., Swastika, A.B.D.P., Pranindita, N. and Rianawati, E. (2024) ‘Improving Indonesia’s palm oil sustainability through financing: A study on disconnects and potential policy solutions’, Indonesian Journal of International Law, 21(5), pp. 121–150. Available at: https://doi.org/10.17304/IJIL.VOL21.5.1880.
Wasserbacher, H. and Spindler, M. (2022) ‘Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls’, Digital Finance, 4, pp. 63–88. Available at: https://doi.org/10.1007/s42521-021-00046-2.
Watts, J.D. et al. (2021) ‘Challenges faced by smallholders in achieving sustainable palm oil certification in Indonesia’, World Development, 146, 105565. Available at: https://doi.org/10.1016/j.worlddev.2021.105565.
Westphal, M., Vössing, M., Satzger, G., Yom-Tov, G.B. and Rafaeli, A. (2023) ‘Decision control and explanations in human-AI collaboration: Improving user perceptions and compliance’, Computers in Human Behavior, 144, 107714. Available at: https://doi.org/10.1016/j.chb.2023.107714.
Xu, K., Qian, J., Hu, Z., Duan, Z., Chen, C., Liu, J., Sun, J., Wei, S. and Xing, X. (2021) ‘A new machine learning approach in detecting the oil palm plantations using remote sensing data’, Remote Sensing, 13(2), 236. Available at: https://doi.org/10.3390/rs13020236.
Zhao, J.J. and Wang, X. (2024) ‘Unleashing efficiency and insights: Exploring the potential applications and challenges of ChatGPT in accounting’, Journal of Corporate Accounting & Finance, 35(1), pp. 269–276. Available at: https://doi.org/10.1002/jcaf.22663.
