Data-Driven Financial Governance in Energy Sector Audits: A Framework for Enhancing SOX Compliance and Cost Efficiency

References

[1] B. O. Otokiti and A. F. Akorede, “Advancing sustainability through change and innovation: A co-evolutionary perspective,” Innov. Tak. Creat. Mark. Book Read. Honour Profr. Otokiti, vol. 1, no. 1, pp. 161–167, 2018.

[2] B. O. Otokiti and O. A. Akinbola, “Effects of Lease Options on the Organizational Growth of Small and Medium Enterprise (SME’s) in Lagos State, Nigeria,” Asian J. Bus. Manag. Sci., vol. 3, no. 4, pp. 1–12, 2013.

[3] B. Schmarzo, Big Data: Understanding how data powers big business. John Wiley & Sons, 2013.

[4] Oladuji T.J. Nwangele C.R., Onifade O., Akintobi A.O., “Advancements in Financial Forecasting Models: Using AI for Predictive Business Analysis in Emerging Economies‬.” [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Zm0csPMAAAAJ&cstart=20&pagesize=80&authuser=1&citation_for_view=Zm0csPMAAAAJ:Zph67rFs4hoC‬

[5] M. Quinn and E. Strauss, the routledge companion to accounting information systems. Routledge, 2018. [Online]. Available: https://api.taylorfrancis.com/content/books/mono/download?identifierName=doi&identifierValue=10.4324/9781315647210&type=googlepdf

[6] O. T. Odofin, O. A. Agboola, E. Ogbuefi, J. C. Ogeawuchi, O. S. Adanigbo, and T. P. Gbenle, “Conceptual Framework for Unified Payment Integration in Multi-Bank Financial Ecosystems,” vol. 3, no. 12, 2020.

[7] A. OMBRA and G. MICHELONI, “Digital CFO within the Industry 4.0 paradigm: insights from a PLS-SEM behavioural investigation,” 2017, [Online]. Available: https://www.politesi.polimi.it/handle/10589/144525

[8] A. A. Lawal, H. A. Ajonbadi, and B. O. Otokiti, “Strategic importance of the Nigerian small and medium enterprises (SMES): Myth or reality”.

[9] M. Nagbiku, “Effective corporate governance practices and sustainable performance in the Nigerian banking industry,” PhD Thesis, Leeds Beckett University, 2016. [Online]. Available: https://figshare.leedsbeckett.ac.uk/articles/thesis/Effective_Corporate_Governance_Practices_and_Sustainable_Performance_in_the_Nigerian_Banking_Industry/21688376/1

[10] O. O. Fagbore, J. C. Ogeawuchi, O. Ilori, N. J. Isibor, A. Odetunde, and B. I. Adekunle, “Developing a Conceptual Framework for Financial Data Validation in Private Equity Fund Operations,” vol. 4, no. 5, 2020.

[11] W. Mbaluka, “Big data management and business value in the commercial banking sector in Kenya,” PhD Thesis, University of Nairobi, 2013. [Online]. Available: https://erepository.uonbi.ac.ke/handle/11295/58632

[12] A. A. Lawal, H. A. Ajonbadi, and B. O. Otokiti, “Leadership and organisational performance in the Nigeria small and medium enterprises (SMEs)”.

[13] B. O Otokiti, “View article.” [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=alrU_gAAAAJ&citation_for_view=alrU_gAAAAJ:KxtntwgDAa4C

[14] P. Malik, “Governing big data: principles and practices,” IBM J. Res. Dev., vol. 57, no. 3/4, pp. 1–1, 2013.

[15] Bisayo Otokiti, “A study of management practices and organisational performance of selected MNCs in emerging market - A Case of Nigeria.” [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=alrU_gAAAAJ&citation_for_view=alrU_gAAAAJ:CHSYGLWDkRkC

[16] S. C. Laval, “Communicating transparancy: a genre network approach: how do corporate governance codes-the SOX and the Tabaksblad Code-affect Dutch cross-listed companies’ corporate communication?,” PhD Thesis, Utrecht University, 2010. [Online]. Available: https://dspace.library.uu.nl/handle/1874/190313

[17] T. Adenuga, A. T. Ayobami, and F. C. Okolo, “AI-Driven Workforce Forecasting for Peak Planning and Disruption Resilience in Global Logistics and Supply Networks,” Int. J. Multidiscip. Res. Growth Eval., vol. 1, no. 2, pp. 71–87, 2020, doi: 10.54660/.ijmrge.2020.1.2.71-87.

[18] H. A. Ajonbadi and B. Mojeed-Sanni, “A & Otokiti, BO (2015).‘Sustaining Competitive Advantage in Medium-sized Enterprises (MEs) through Employee Social Interaction and Helping Behaviours.,’” J. Small Bus. Entrep. Dev., vol. 3, no. 2, pp. 89–112.

[19] J. Ladley, Data governance: How to design, deploy, and sustain an effective data governance program. Academic Press, 2019.

[20] Ajuwon A., Onifade O., Oladuji T.J., Akintobi A.O., “Blockchain-Based Models for Credit and Loan System Automation in Financial Institutions‬.” [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Zm0csPMAAAAJ&cstart=20&pagesize=80&authuser=1&citation_for_view=Zm0csPMAAAAJ:ULOm3_A8WrAC‬

[21] Akinbola, Olufemi Amos; Otokiti, Bisayo Oluwatosin; Akinbola, Omolola Sariat; Sanni, Sekinat Arike., “NEXUS OF BORN GLOBAL ENTREPRENEURSHIP FIRMS AND ECONOMIC DEVELOPMENT IN NIGERIA - ProQuest.” [Online]. Available: https://www.proquest.com/openview/81adc74d18d0d149474095698194233a/1?pq-origsite=gscholar&cbl=5261234

[22] V. Krishnamohan, “HUMAN RESOURCE ANALYTICS THE CHANGE CATALYST FOR ENHANCING DECISION MAKING AND EFFICIENCY WITH REFERENCE TO INDIAN IT INDUSTRY,” Emerg. Trends Bus. Manag., p. 169.

[23] O. Amos, O. Adeniyi, and B. Oluwatosin, “MARKET BASED CAPABILITIES AND RESULTS: INFERENCE FOR TELECOMMUNICATION SERVICE BUSINESSES IN NIGERIA,” 2014.

[24] M. A. Adewoyin, E. O. Ogunnowo, J. E. Fiemotongha, T. O. Igunma, and A. K. Adeleke, “A Conceptual Framework for Dynamic Mechanical Analysis in High-Performance Material Selection,” vol. 4, no. 5, 2020.

[25] J. Koreff, “Three Studies Examining Auditors’ Use of Data Analytics,” 2018, [Online].Available:https://stars.library.ucf.edu/etd/5949/

[26] M. Oyedele, O. Awoyemi, F. A. Atobatele, and C. A. Okonkwo, “Leveraging Multimodal Learning: The Role of Visual and Digital Tools in Enhancing French Language Acquisition,” Iconic Res. Eng. J., vol. 4, no. 1, pp. 197–211, July 2020.

[27] E. O. Ogunnowo, M. A. Adewoyin, J. E. Fiemotongha, T. O. Igunma, and A. K. Adeleke, “Systematic Review of Non-Destructive Testing Methods for Predictive Failure Analysis in Mechanical Systems,” vol. 4, no. 4, 2020.

[28] B. A. Karpf, “Dead reckoning: where we stand on privacy and security controls for the Internet of Things,” PhD Thesis, Massachusetts Institute of Technology, 2017. [Online]. Available: https://dspace.mit.edu/handle/1721.1/111231

[29] M. A. Adewoyin, E. O. Ogunnowo, J. E. Fiemotongha, T. O. Igunma, and A. K. Adeleke, “Advances in Thermofluid Simulation for Heat Transfer Optimization in Compact Mechanical Devices,” vol. 4, no. 6, 2020.

[30] “Intelligent Credit Risk Decision Support: Architecture and Implementations,” in Artificial Intelligence in Financial Markets, London: Palgrave Macmillan UK, 2016, pp. 179–210. doi: 10.1057/978-1-137-48880-0_7.

[31] “Enterprise Operational Analysis Using DEMO and the Enterprise Operating System,” in Lecture Notes in Business Information Processing, Cham: Springer International Publishing, 2015, pp. 3–18. doi: 10.1007/978-3-319-19297-0_1.

[32] P. Karagiannidis, “Data-driven ship propulsion modelling with applications in the performance analysis and fuel consumption prediction,” 2019, [Online]. Available: https://dspace.lib.ntua.gr/xmlui/bitstream/handle/123456789/49260/Thesis_Karagiannidis_Pavlos.pdf

[33] J. Zhou, “Continuous audit data analytics and internal control intelligence,” PhD Thesis, Rutgers University-Graduate School-Newark, 2020. [Online]. Available: https://rucore.libraries.rutgers.edu/rutgers-lib/64938/

[34] R. S. Kaplan and D. P. Norton, the execution premium: Linking strategy to operations for competitive advantage. Harvard business press, 2008. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=qTg5R5GXEZoC&oi=fnd&pg=PR3&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=irCzWzGlnP&sig=AJyn792m6ojgaRp2-ZizsO0JO8Y

[35] D. A. Zetzsche, W. A. Birdthistle, D. W. Arner, and R. P. Buckley, “Financial Operating Systems,” 2020, doi: 10.2139/ssrn.3532975.

[36] E. C. Yen, “Warning signals for potential accounting frauds in blue chip companies–An application of adaptive resonance theory,” Inf. Sci., vol. 177, no. 20, pp. 4515–4525, 2007.

[37] M. Jofre, R. Gerlach, and D. M. Scharth, “FIGHTING ACCOUNTING FRAUD THROUGH FORENSIC ANALYTICS”.

[38] Y. Xu, “Using Machine Learning to Target Retrofits in Commercial Buildings under Alternative Climate Change Scenarios,” PhD Thesis, Carnegie Mellon University, 2020. [Online]. Available: https://search.proquest.com/openview/9ad06cf371b9702a18ad8c3b5ee25cce/1?pq-origsite=gscholar&cbl=18750&diss=y

[39] K. R. Holdaway, Harness oil and gas big data with analytics: Optimize exploration and production with data-driven models. John Wiley & Sons, 2014. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=imdiAwAAQBAJ&oi=fnd&pg=PR11&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=DuIaOCZVS2&sig=ZvSMhAiDv69yE9Dd97Zi3jZCF3w

[40] T. van den Broek and A. F. van Veenstra, “Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation,” Technol. Forecast. Soc. Change, vol. 129, pp. 330–338, 2018.

[41] C. Hirsch and J.-N. Ezingeard, “Perceptual and cultural aspects of risk management alignment: a case study,” J. Inf. Syst. Secur., vol. 4, no. 1, pp. 1551–0123, 2008.

[42] A. Hamdani, N. Hashai, E. Kandel, and Y. Yafeh, “Technological Progress and the Future of the Corporation,” J Br. Acad, vol. 6, pp. 215–225, 2018.

[43] B. K. Gudepu and R. Eichler, “The Power of Business Metadata, Driving Better Decision Making in Business Intelligence,” The Computertech, pp. 58–74, 2019.

[44] I. Graham, Business rules management and service-oriented architecture: a pattern language. John wiley & sons, 2007. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=_InzFf5XpeQC&oi=fnd&pg=PR5&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=sSg5QiQZqC&sig=ngbi_ys5zwEIahID52V1MwPAdQg

[45] K. C. Gonugunta and T. Sotirios, “Data Warehousing-More Than Just a Data Lake,” The Computertech, pp. 52–61, 2020.

[46] P. Ghavami, Big data management: Data governance principles for big data analytics. Walter de Gruyter GmbH & Co KG, 2020.

[47] T. Fitzgerald, CISO COMPASS: navigating cybersecurity leadership challenges with insights from pioneers. Auerbach Publications, 2018. [Online]. Available: https://www.taylorfrancis.com/books/mono/10.1201/9780429399015/ciso-compass-todd-fitzgerald

[48] T. Fisher, the data asset: how smart companies govern their data for business success. John Wiley & Sons, 2009. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=Zplvl7Q2C2UC&oi=fnd&pg=PR7&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=dZZfUGHug1&sig=oM4_akGwQ3VgnxyeqCXkRiMGpa0

[49] M. FAHY and J. ROCHE, “Creating Corporate Value through Performance, Conformance and Responsibility”, [Online]. Available: https://download.itadmins.net/Technology/John%20Wiley%20&%20Sons%20%202005%20%20Beyond%20Governance.%20Creating%20Corporate%20Value%20through%20Performance,%20Conformance%20and%20Responsibility%20%20ISBN%200470011513%20-%20339s%20-%20TLFeBOOK.pdf

[50] C. T. C. R. ENERGY, “SUSTAIN ABILITY,” Final Proj. Rep. Assesment Piezoelectric Mater. Roadway Energy Harvest. Cost Energy Demonstr. Roadmap, 2013, [Online]. Available: https://www.malakoff.com.my/ir/wpcontent/uploads/Sustainability_Review.pdf

[51] R. Dwivedi, Intellectual Structure of Business Analytics and Data Driven Insights for Information Security Breaches. The University of Texas at Arlington, 2018. [Online]. Available: https://search.proquest.com/openview/985da4256064a4a5b21004d034b206d1/1?pq-origsite=gscholar&cbl=18750&diss=y

[52] G. H. Duckert, Practical enterprise risk management: a business process approach, vol. 15. John Wiley & Sons, 2010. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=PEsPlzNZuM8C&oi=fnd&pg=PT7&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=O9kRhYzwl1&sig=5oaJ78762CKUp7PQw-sHVzSXHf0

[53] T. E. Drabczyk Sr, how grant recipients can satisfy compliance requirements for US federal awards. Walden University, 2016. [Online]. Available: https://search.proquest.com/openview/3a69293da204267064f39d409d240ff4/1?pq-origsite=gscholar&cbl=18750

[54] J. Djumalieva and C. Sleeman, “An open and data-driven taxonomy of skills extracted from online job adverts,” ESCoE DP-2018-13). https://EconPapers. repec. org/RePEc: nsr: escoed: escoe …, 2018. [Online]. Available: http://escoe-website.s3.amazonaws.com/wp-content/uploads/2020/07/13161304/ESCoE-DP-2018-13.pdf

[55] A. Dey, Corporate governance and financial reporting credibility. Northwestern University, 2005. [Online]. Available: https://search.proquest.com/openview/9a2b15dbdb6f054822b57f255dc8b0ce/1?pq-origsite=gscholar&cbl=18750&diss=y

[56] D. R. Desai and J. A. Kroll, “Trust but verify: A guide to algorithms and the law,” Harv JL Tech, vol. 31, p. 1, 2017.

[57] T. Davenport and J. Harris, competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Press, 2017.

[58] S. P. A. Datta, J. Lyu, and H.-C. Jen, “Bio-inspired Energy Future: Quest for Efficient Intelligent Mitochondria and New Liquid Fuels.,” Int. J. Electron. Bus. Manag., vol. 9, no. 1, 2011, [Online]. Available: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=17282047&AN=59523468&h=RJmFpvzJtfQradJVBda%2BSym2ewO1EYnLhf%2FEd1IFrcP%2FVAwgwiZOudX6FtBLqXI34xFXh958%2FUX9WfT24LQCow%3D%3D&crl=c

[59] S. Datta, “Energy 2050: Bio-inspired Renewable Non-Fossil Liquid Fuel,” 2011, [Online]. Available: https://dspace.mit.edu/handle/1721.1/59804

[60] S. Datta, “Bio-Inspired Energy Dynamics,” 2010, [Online]. Available: https://dspace.mit.edu/handle/1721.1/53329

[61] S. DATTA, “UNFCCC Observer: Submission of Information and Views,” 2009, [Online]. Available: https://dspace.mit.edu/bitstream/handle/1721.1/45551/Submission%20of%20Information%20and%20Views%20to%20UNFCCC.pdf?sequence=1

[62] D. Apgar, “The False Promise of Big Data: Can Data Mining Replace Hypothesis-Driven Learning in the Identification of Predictive Performance Metrics?,” Syst. Res. Behav. Sci., vol. 32, no. 1, pp. 28–49, Jan. 2015, doi: 10.1002/sres.2219.

[63] J. W. Bagby and N. G. Packin, “RegTech and predictive lawmaking: Closing the RegLag between prospective regulated activity and regulation,” Mich Bus Entrep. Rev, vol. 10, p. 127, 2020.

[64] I. Ajunwa, “An auditing imperative for automated hiring systems,” Harv JL Tech, vol. 34, p. 621, 2020.

[65] V. R. Boppana, “Adoption of CRM in Regulated Industries: Compliance and Challenges,” Available SSRN 5135109, 2020, [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5135109

[66] A. Borek, A. K. Parlikad, J. Webb, and P. Woodall, Total information risk management: maximizing the value of data and information assets. Newnes, 2013. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=GEaUoHtJ1b8C&oi=fnd&pg=PP1&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=ymEzRmwuXq&sig=pUSjDXcYXqZkQ5pWx3kof9bHMzU

[67] K. Q. Bui and L. P. Perera, “A decision support framework for cost-effective and energy-efficient shipping,” in International Conference on Offshore Mechanics and Arctic Engineering, American Society of Mechanical Engineers, 2020, p. V06AT06A026. [Online]. Available: https://asmedigitalcollection.asme.org/OMAE/proceedings-abstract/OMAE2020/V06AT06A026/1092851

[68] F. Caldwell, T. Eid, and C. Casper, “Magic quadrant for enterprise governance, risk and compliance platforms,” Gart. Res. G, vol. 158295, 2008, [Online]. Available: http://www.nexdimension.net/wpcontent/uploads/2013/04/ibmopenpagesgartnerreportgovernance-risk-compliance-2011.pdf

[69] V. Chang, R. Valverde, M. Ramachandran, and C.-S. Li, “Toward business integrity modeling and analysis framework for risk measurement and analysis,” Appl. Sci., vol. 10, no. 9, p. 3145, 2020.

[70] K. Charles-Guzman, “Air Pollution Control Strategies in New York City: A Case Study of the Role of Environmental Monitoring, Data Analysis, and Stakeholder Networks in Comprehensive Government Policy Development,” Dec. 2012, [Online]. Available: http://deepblue.lib.umich.edu/handle/2027.42/94532

[71] K. S. Church, P. J. Schmidt, and K. Ajayi, “Forecast cloudy—Fair or stormy weather: Cloud computing insights and issues,” J. Inf. Syst., vol. 34, no. 2, pp. 23–46, 2020.

[72] G. G. Creamer and Y. Freund, “Using Adaboost for Equity Investment Scorecards,” 2005, Social Science Research Network, Rochester, NY: 940729. doi: 10.2139/ssrn.940729.

[73] L. Dagilienė and L. Klovienė, “Motivation to use big data and big data analytics in external auditing,” Manag. Audit. J., vol. 34, no. 7, pp. 750–782, 2019.

[74] H. R. Zargarian, CEO Compensation and Performance in Publicly-Traded Hospitals: 2011-2016. Northcentral University, 2018. [Online]. Available: https://search.proquest.com/openview/3507084fd509346e7816065baed429be/1?pq-origsite=gscholar&cbl=18750

[75] M. A. M. Wahdan, “Automatic formulation of the auditor’s opinion; AREX: a knowledge-based approach,” 2006, [Online]. Available: https://cris.maastrichtuniversity.nl/files/1074659/guid-94e10534-8228-41e9-82fb-543f6a7553ec-ASSET2.0.pdf

[76] F. G. Viens, M. C. Mariani, and I. Florescu, Eds., Handbook of Modeling High‐Frequency Data in Finance, 1st ed. Wiley, 2011. doi: 10.1002/9781118204580.

[77] A. M. J. van Rensburg, Knowledge and skills required by entry-level accountants for integrated reporting. University of Johannesburg (South Africa), 2018. [Online]. Available: https://search.proquest.com/openview/d06f8592a35e585ec3bb8fccb9f3bd7b/1?pq-origsite=gscholar&cbl=2026366&diss=y

[78] J. Taylor and N. Raden, Smart Enough Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions. Pearson Education, 2007.

[79] C. W. Smith, The Impact of International Financial Reporting Standards on Key Financial Indicators of Canadian Companies. Walden University, 2016. [Online]. Available: https://search.proquest.com/openview/6a7dedb13fb9429fee72ce4c32aaf955/1?pq-origsite=gscholar&cbl=18750

[80] R. H. M. Romo, A provisional taxonomy of revenue assurance: a grounded theory approach. University of Johannesburg (South Africa), 2010. [Online]. Available: https://search.proquest.com/openview/9396171485f44c97d81f4bbc827635ed/1?pqorigsite=gscholar&cbl=2026366&diss=y

[81] J. Ladley, making enterprise information management (EIM) work for business: A guide to understanding information as an asset. Morgan Kaufmann, 2010. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=ck4BVZuw_jcC&oi=fnd&pg=PP1&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=qxSKSdzHa&sig=IFDoFc7Jkpni7Ng4GmbuSIEkYqk

[82] R. Kepczynski, R. Jandhyala, G. Sankaran, and A. Dimofte, Integrated Business Planning: How to Integrate Planning Processes, Organizational Structures and Capabilities, and Leverage SAP IBP Technology. in Management for Professionals. Cham: Springer International Publishing, 2018. doi: 10.1007/978-3-319-75665-3.

[83] D. B. A. Joseph Aluya, The Influences of Big Data Analytics. Author House, 2014. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=3HhyBAAAQBAJ&oi=fnd&pg=PP1&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=0ldoh-7cm8&sig=To2QWYVsUUtLKGnai5-S_jii7mE

[84] K. Forensic, “Fraud risk management,” N. Y. NY KPMG, 2006, [Online]. Available: http://riskcue.id/uploads/ebook/184148.pdf

[85] G. CREAMER, “Using Analysis Boosting and Trading* for Financial,” Handb. Model. High-Freq. Data Finance, p. 47, 2011.

[86] B. Barnier, The Operational Risk Handbook for Financial Companies: A guide to the new world of performance-oriented operational risk. Harriman House Limited, 2011. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=CFDdAgAAQBAJ&oi=fnd&pg=PT2&dq=SOX+compliance,+energy+audit,+financial+governance,+datadriven+framework,+cost+efficiency,+predictive+controls&ots=VWpIWPwj3j&sig=22hLnWEIXKFofZzqkcBFYKdmXtw

[87] S. A. Baidoo, “Regulatory Effects on Traditional Financial Systems Versus Blockchain and Emerging Financial Systems,” PhD Thesis, Walden University, 2019. [Online]. Available: https://search.proquest.com/openview/1afd466fa1fd979259e2f10ae3ecc942/1?pq-origsite=gscholar&cbl=18750&diss=y

[88] A. Addo, S. Centhala, and M. Shanmugam, Artificial intelligence for risk management. Business Expert Press, 2020.

[89] L. Amoore, The politics of possibility: Risk and security beyond probability. Duke University Press, 2013.

[90] I. N. Sener, R. M. Pendyala, and C. R. Bhat, “Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior,” J. Transp. Geogr., vol. 19, no. 2, pp. 294–303, 2011.

[91] P. Mondal and M. Basu, “Adoption of precision agriculture technologies in India and in some developing countries: Scope, present status and strategies,” Prog. Nat. Sci., vol. 19, no. 6, pp. 659–666, 2009.

[92] T. A. Schenk, G. Löffler, and J. Rauh, “Agent-based simulation of consumer behavior in grocery shopping on a regional level,” J. Bus. Res., vol. 60, no. 8, pp. 894–903, 2007.

[93] T. Berger, “Agent‐based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis,” Agric. Econ., vol. 25, no. 2–3, pp. 245–260, Sept. 2001, doi: 10.1111/j.1574-0862. 2001.tb00205. x.

@article{1709972,
      author = {Nnadozie Odinaka, Chinelo Harriet Okolo, Onyeka Kelvin Chima, Oluwatobi Opeyemi Adeyelu},
      title = {Data-Driven Financial Governance in Energy Sector Audits: A Framework for Enhancing SOX Compliance and Cost Efficiency},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {10},
      pages = {465-480},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709972.pdf},
      abstract = {The energy sector, particularly in its intersection with regulatory compliance and cost-intensive operations, faces unprecedented challenges in aligning financial governance with Sarbanes-Oxley (SOX) mandates. This paper presents a data-driven audit framework tailored for energy companies to enhance SOX compliance and achieve cost efficiency. Drawing on a mixed-methods analysis incorporating financial analytics, machine learning audit tools, and regulatory policy models, the study constructs a multi-layered governance model grounded in real-time data orchestration and predictive controls. The proposed framework aims to equip energy sector CFOs, auditors, and compliance officers with actionable strategies to streamline internal controls, detect anomalies early, and drive down audit costs without compromising regulatory rigor. Empirical validation across five multinational energy firms confirms the model's effectiveness in improving audit reliability, compliance timelines, and resource allocation. The findings suggest that integrating advanced data governance principles into audit design not only improves compliance posture but also serves as a strategic lever for financial resilience.},
      keywords = {SOX compliance, energy audit, financial governance, data-driven framework, cost efficiency, predictive controls},
      month = {April},
  }