AI in the Treasury Function: Optimizing Cash Forecasting, Liquidity Management, and Hedging Strategies

References

[1] Abayomi, A.A., Mgbame, A.C., Akpe, O.E.E., Ogbuefi, E. and Adeyelu, O.O., 2021. Advancing equity through technology: Inclusive design of BI platforms for small businesses. Iconic Research and Engineering Journals, 5(4), pp.235–241.

[2] Ackoff, R.L., 1989. From data to wisdom. Journal of Applied Systems Analysis, 16(1), pp.3–9.

[3] Ajayi, S.A.O. and Akanji, O.O., 2021. Impact of BMI and Menstrual Cycle Phases on Salivary Amylase: A Physiological and Biochemical Perspective.

[4] Alonge, E.O., Eyo-Udo, N.L., Ubanadu, B.C., Daraojimba, A.I., Balogun, E.D. and Ogunsola, K.O., 2021. Enhancing data security with machine learning: A study on fraud detection algorithms. Journal of Data Security and Fraud Prevention, 7(2), pp.105–118.

[5] Alonge, E.O., Eyo-Udo, N.L., CHIBUNNA, B., UBANADU, A.I.D., BALOGUN, E.D. and OGUNSOLA, K.O., 2021. Digital transformation in retail banking to enhance customer experience and profitability. Iconic Research and Engineering Journals, 4(9).

[6] Altman, E.I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), pp.589–609.

[7] Andreou, P.C., Louca, C. and Petrou, A.P., 2017. Corporate governance, financial management decisions and firm performance: Evidence from Cyprus. Managerial Finance, 43(1), pp.34–57.

[8] Aven, T., 2016. Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), pp.1–13.

[9] Aveni, T., 2015. Digital credit in emerging markets: A snapshot of the current landscape and key issues. CGAP Brief, World Bank Group.

[10] Ayumu, M. T., & Ohakawa, T. C. (2021). Optimizing public-private partnerships (PPP) in *5*(6), 332–339.

[11] Bahrammirzaee, A., 2010. A literature review of artificial intelligence in finance and economics. International Journal of Financial Studies, 1(1), pp.55–79.

[12] Basel Committee on Banking Supervision, 2004. International Convergence of Capital Measurement and Capital Standards. Bank for International Settlements.

[13] Berg, T., Burg, V., Gombović, A. and Puri, M., 2020. On the rise of fintechs–Credit scoring using digital footprints. The Review of Financial Studies, 33(7), pp.2845–2897.

[14] Bessis, J., 2010. Risk management in banking. 3rd ed. Hoboken: John Wiley & Sons.

[15] Bhatia, M., Königstorfer, J. & Thalmann, C., 2020. Behavioral finance and AI: The role of investor sentiment in algorithmic forecasting. Journal of Behavioral Finance, 21(3), pp.222–240.

[16] Bikker, J.A. and Metzemakers, P.A., 2005. Bank provisioning behaviour and procyclicality. Journal of International Financial Markets, Institutions and Money, 15(2), pp.141–157.

[17] Bolton, R., Chen, H. & Wang, N., 2021. Supervising financial risk with machine learning: A new approach to systemic risk prediction. Journal of Financial Economics, 141(3), pp.603–624.

[18] Braun, V. & Clarke, V., 2006. Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), pp.77–101.

[19] Brynjolfsson, E. & McAfee, A., 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton & Company.

[20] Brynjolfsson, E. & McElheran, K., 2016. The rapid adoption of data-driven decision-making. American Economic Review, 106(5), pp.133–139.

[21] Bucci, F., 2020. Forecasting volatility: Comparing deep neural networks and traditional methods in derivatives pricing. Journal of Derivatives, 28(3), pp.45–58.

[22] Buehler, K., Freeman, A., Hulme, R., Nottebohm, O. and Quiring, R., 2018. The new CFO mandate: Prioritize digitization to create value. McKinsey & Company.

[23] Bughin, J. et al., 2018. Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute, Discussion Paper.

[24] Cao, G., Duan, Y. and Li, G., 2015. Linking business analytics to decision making effectiveness: A path model analysis. IEEE Transactions on Engineering Management, 62(3), pp.384–395.

[25] Casu, B., Girardone, C. and Molyneux, P., 2006. Introduction to banking. Harlow: Pearson Education.

[26] Chaboud, A.P. et al., 2014. Rise of the machines: Algorithmic trading in the foreign exchange market. Journal of Finance, 69(5), pp.2045–2084.

[27] Chandrasekaran, R., 2013. Big data analytics in financial services: State of the practice and challenges. International Journal of Business Intelligence Research, 4(2), pp.1–14.

[28] Chong, E., Han, C. and Park, F.C., 2017. Deep learning networks for improved financial forecasting. Neurocomputing, 260 (Part A), pp.24–37.

[29] Choudhry, M., 2012. The principles of banking. Hoboken: John Wiley & Sons.

[30] Cios, K.J. and Moore, G.W., 2002. Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26(1–2), pp.1–24.

[31] Cohen, F. and Young, D., 2006. Auditing cybersecurity: A risk-based approach. ISACA Journal, 4, pp.1–8.

[32] Coombs, C.R. and Nicholson, B., 2013. Business models and value chains in digital banking. Information Systems Journal, 23(3), pp.239–259.

[33] Crouhy, M., Galai, D. and Mark, R., 2000. Risk Management. New York: McGraw-Hill.

[34] Damodaran, A., 2007. Strategic risk taking: A framework for risk management. Upper Saddle River: Pearson Education.

[35] Davenport, T.H. and Harris, J.G., 2007. Competing on analytics: The new science of winning. Boston: Harvard Business School Press.

[36] Deloitte, 2018. Treasury in the digital age: Modernizing for new challenges. Deloitte Insights.

[37] Dempsey, J.X., 2003. Privacy and data security: Regulatory challenges in the 21st century. The George Washington Law Review, 72(6), pp.1323–1345.

[38] Dermine, J., 2003. Banking with technology: The prospects and challenges. Journal of Financial Transformation, 9, pp.11–18.

[39] DeYoung, R., Lang, W.W. and Nolle, D.E., 2007. How the internet affects output and performance at community banks. Journal of Banking & Finance, 31(4), pp.1033–1060.

[40] Dhillon, G., 2001. Challenges in managing information security in the new millennium. In Managing Information Assurance in Financial Services (pp. 3–13). IGI Global.

[41] Do Prado, M.L. et al., 2016. Advancing research in financial machine learning. In: Proceedings of the 9th International Conference on Computational Finance.

[42] Doshi‑Velez, F. and Kim, B., 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

[43] Drucker, P.F., 1999. Management challenges for the 21st century. New York: HarperBusiness.

[44] Dutta, S. and Bose, I., 2006. Managing a financial services supply chain: The risk management perspective. Supply Chain Management: An International Journal, 11(4), pp.362–370.

[45] Dutta, S. and Shekhar, S., 1988. A neural network approach to bond rating prediction. Financial Analysts Journal, 44(2), pp.25–31.

[46] Eccles, R.G., Newquist, S.C. and Schatz, R., 2014. The impact of AI on sustainable investing. Journal of Applied Corporate Finance, 26(4), pp.56–64.

[47] Eisenhardt, K.M., 1989. Agency theory: An assessment and review. Academy of Management Review, 14(1), pp.57–74.

[48] Eling, M. and Lehmann, M., 2018. The impact of digitalization on the insurance value chain and the insurability of risks. The Geneva Papers on Risk and Insurance-Issues and Practice, 43(3), pp.359–396.

[49] Fabozzi, F.J., 2009. Quantitative Investment Analysis. 2nd ed. Hoboken, NJ: John Wiley & Sons.

[50] Fabozzi, F.J., Focardi, S.M. and Kolm, P.N., 2006. Financial modeling of the equity market: From CAPM to cointegration. Hoboken: John Wiley & Sons.

[51] Fagbore, O.O., Ogeawuchi, J.C., Ilori, O., Isibor, N.J., Odetunde, A. and Adekunle, B.I., 2020. Developing a Conceptual Framework for Financial Data Validation in Private Equity Fund Operations.

[52] Fernandes, V. et al., 2014. Forecasting realized volatility in the S&P 500 using deep learning. Journal of Banking & Finance, 40(1), pp.365–378.

[53] Floridi, L., 2010. Information: A very short introduction. Oxford: Oxford University Press.

[54] Forrester, J.W., 1961. Industrial Dynamics. Cambridge, MA: MIT Press.

[55] Fung, B., 2014. The demand and need for transparency and disclosure in corporate governance. Universal Journal of Management, 2(2), pp.72–80.

[56] Gatzert, N. and Schmit, J., 2016. Supporting strategic risk management with performance- and value-oriented risk management systems: The case of ERM. Journal of Risk Finance, 17(2), pp.136–150.

[57] Ghosh, S., 2012. Predictive analytics in treasury management. Journal of Treasury and Risk Management, 3(2), pp.45–59.

[58] Ghosh, S., 2007. Cyber-crimes: A global concern. Journal of Information, Law and Technology, 2007(2), pp.1–13.

[59] Ghosh, S., 2018. Cloud-based AI systems in corporate liquidity modeling. Journal of Financial Transformation, 49, pp.87–103.

[60] Gil-Garcia, J.R., Chengalur-Smith, I. and Duchessi, P., 2007. Collaborative e-Government: Impediments and benefits of information-sharing projects in the public sector. European Journal of Information Systems, 16(2), pp.121–133.

[61] Goel, S., 2005. Information security risk analysis: A matrix-based approach. Communications of the ACM, 48(12), pp.64–71.

[62] Goodell, J.W., McNelis, P. and Martins, R.P., 2021. Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646.

[63] Gorry, G.A. and Scott Morton, M.S., 1971. A framework for management information systems. Sloan Management Review, 13(1), pp.55–70.

[64] Greenstein, M. and McKee, T.E., 2004. Assurance practitioners’ and educators’ self-perceived IT knowledge level: An empirical assessment. International Journal of Accounting Information Systems, 5(2), pp.213–243.

[65] Gregory, R. and Keen, P., 2002. Competing in time: Using telecommunications for competitive advantage. Ballinger Publishing Company.

[66] Halliday, N.N., 2021. Assessment of Major Air Pollutants, Impact on Air Quality and Health Impacts on Residents: Case Study of Cardiovascular Diseases (Master's thesis, University of Cincinnati).

[67] Hamilton, J.D. and Susmel, R., 1994. Autoregressive Conditional Heteroskedasticity in Financial Time Series: Forecasting FX volatility. Journal of Financial Econometrics, 1(3), pp.285–327.

[68] Heeks, R., 2006. Implementing and managing eGovernment: An international text. London: SAGE.

[69] Hendershott, T., Jones, C.M. and Menkveld, A.J., 2011. Does algorithmic trading improve liquidity? Journal of Finance, 66(1), pp.1–33.

[70] Hernández Tinoco, L. and Wilson, N., 2013. Bankruptcy prediction using artificial neural networks. Expert Systems with Applications, 40(15), pp.6036–6043.

[71] Hodge, G., Greve, C. and Boardman, A., 2010. International handbook on public–private partnerships. Cheltenham: Edward Elgar.

[72] Holton, G.A., 2003. Value-at-risk: Theory and practice. San Diego: Academic Press.

[73] Hopwood, A.G., 1974. Accounting and human behavior. Englewood Cliffs: Prentice-Hall.

[74] Hull, J.C., 2009. Options, futures, and other derivatives. 7th ed. Upper Saddle River: Pearson Education.

[75] Ifinedo, P., 2006. Information systems security policies: A contextual analysis of organizational factors. In Proceedings of the 2006 ACM SIGMIS CPR Conference (pp. 1–7).

[76] Ilori, O., Lawal, C.I., Friday, S.C., Isibor, N.J. and Chukwuma-Eke, E.C., 2021. Enhancing Auditor Judgment and Skepticism through Behavioral Insights: A Systematic Review.

[77] ISO/IEC 27001, 2013. Information technology — Security techniques — Information security management systems — Requirements. Geneva: International Organization for Standardization.

[78] Ittner, C.D. and Larcker, D.F., 2003. Coming up short on nonfinancial performance measurement. Harvard Business Review, 81(11), pp.88–95.

[79] Jagtiani, J. and Lemieux, C., 2018. Big data in fintech: Automated customer acquisition, pricing, and risk management. Journal of Economics and Business, 100, pp.52–63.

[80] Jain, R. and Jain, S., 2010. ERP in treasury operations: An analytical study. International Journal of Business Research and Management, 1(1), pp.1–15.

[81] Kaiser, J. and Schlarbaum, G.G., 1995. Expert systems in treasury risk management: Early implementations and lessons. Journal of Applied Finance, 5(2), pp.63–74.

[82] Kambil, A. and van Heck, E., 2002. Making markets: How firms can design and profit from online auctions and exchanges. Harvard Business Press.

[83] Kanas, A., 2001. Predicting stock returns: neural networks vs traditional models. Journal of Forecasting, 20(1), pp.33–46.

[84] Kaplan, R.S. and Norton, D.P., 1996. The balanced scorecard: Translating strategy into action. Boston: Harvard Business School Press.

[85] Knight, F.H., 1921. Risk, uncertainty and profit. Boston: Houghton Mifflin.

[86] Kokina, J., Mancha, R. and Pachamanova, D., 2017. Financial forecasting with deep learning. International Journal of Accounting Information Systems, 26, pp.37–55.

[87] Krauss, C., Do, X.A. and Huck, N., 2017. Deep neural networks, gradient‑boosted trees, random forests: Statistical arbitrage on daily data. European Journal of Operational Research, 259(2), pp.689–702.

[88] Lahmiri, S. and Bekiros, S., 2019. Nonlinear classification of financial distress using big data analytics. Physica A, 518, pp.120–131.

[89] Lane, W.R., Looney, S.W. and Wansley, J.W., 1986. An application of the Cox proportional hazards model to bank failure. Journal of Banking & Finance, 10(4), pp.511–531.

[90] Laudon, K.C. and Laudon, J.P., 2012. Management information systems: Managing the digital firm. 12th ed. Upper Saddle River: Pearson Prentice Hall.

[91] Leavitt, N., 2002. Will strong passwords survive? IEEE Computer, 35(6), pp.16–19.

[92] Lee, J., Lee, H. and Kang, J., 2020. Recurrent neural networks for cash flow forecasting in multinational companies. Journal of Corporate Treasury Management, 15(1), pp.112–127.

[93] Lessig, L., 2006. Code: Version 2.0. New York: Basic Books.

[94] Litzenberger, R.H., Ramaswamy, K. and Urrutia, J.L., 2012. Market liquidity and algorithmic trading: Evidence from global exchanges. Journal of Financial Economics, 103(1), pp.158–182.

[95] Luenendonk, M., 2013. Corporate governance and ethical behavior: The role of transparency and accountability. Journal of Business Ethics, 14(3), pp.211–223.

[96] Manogna, A. and Mishra, B., 2021. Commodity price prediction using machine learning: Oil, gold, and agriculture. Energy Economics, 92, 104952.

[97] Manson, S. and O'Malley, M., 2003. Audit committees and information systems: The case for IT knowledge. International Journal of Auditing, 7(1), pp.37–52.

[98] Martin, K., 2008. The role of data stewardship in effective data governance. Journal of Data and Information Quality, 1(1), pp.1–5.

[99] McAfee, A. and Brynjolfsson, E., 2012. Big data: The management revolution. Harvard Business Review, 90(10), pp.60–68.

[100] McKinsey & Company, 2019. The future of work: AI and automation in finance. McKinsey Global Institute Report.

[101] Mearian, L., 2007. Data retention and compliance: A legal and logistical challenge. Computerworld, 41(3), pp.23–28.

[102] Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), pp.449–470.

[103] Moeller, R.R., 2011. COSO enterprise risk management: Establishing effective governance, risk, and compliance (GRC) processes. Hoboken: John Wiley & Sons.

[104] Morini, M. and Prampolini, A., 2011. Risk-neutral versus real-world: The pitfalls of scenario generators. Journal of Risk Management in Financial Institutions, 4(4), pp.365–372.

[105] Mullainathan, S. and Obermeyer, Z., 2017. Does machine learning improve decision making in financial services? Harvard Business Review, July–August, pp.58–65.

[106] Muro, M., Maxim, R. and Whiton, J., 2019. Automation and anxiety: Will AI eliminate jobs in finance? Brookings Institution Report.

[107] Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y. and Sun, X., 2011. The application of data mining techniques in financial fraud detection: A classification framework and case study. Decision Support Systems, 50(3), pp.559–569.

[108] Nolan, R.L. and McFarlan, F.W., 2005. Information technology and the board of directors. Harvard Business Review, 83(10), pp.96–106.

[109] O'Leary, D.E. and Watkins, P.J., 1989. Review of expert systems in auditing. USC Expert Systems Review, Spring–Summer.

[110] Ogunsola, K.O., Balogun, E.D. and Ogunmokun, A.S., 2021. Enhancing financial integrity through an advanced internal audit risk assessment and governance model. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.781–790.

[111] Ogunmokun, A.S., Balogun, E.D. and Ogunsola, K.O., 2021. A Conceptual Framework for AI-Driven Financial Risk Management and Corporate Governance Optimization. International Journal of Multidisciplinary Research and Growth Evaluation.

[112] Ojonugwa, B.M., Abiola-Adams, O., Otokiti, B.O. and Ifeanyichukwu, F., Developing a Risk Assessment Modeling Framework for Small Business Operations in Emerging Economies.

[113] Ojonugwa, B.M., Otokiti, B.O., Abiola-Adams, O. and Ifeanyichukwu, F., Constructing Data-Driven Business Process Optimization Models Using KPI-Linked Dashboards and Reporting Tools.

[114] Okolie, C.I., Hamza, O., Eweje, A., Collins, A., Babatunde, G.O. and Ubamadu, B.C., 2021. Leveraging digital transformation and business analysis to improve healthcare provider portal. Iconic Research and Engineering Journals, 4(10), pp.253–257.

[115] Oni, O., Adeshina, Y.T., Iloeje, K.F. and Olatunji, O.O., ARTIFICIAL INTELLIGENCE MODEL FAIRNESS AUDITOR FOR LOAN SYSTEMS. Journal ID, 8993, p.1162.

[116] Orieno, O.H., Oluoha, O.M., Odeshina, A., Reis, O., Okpeke, F. and Attipoe, V., 2021. Project management innovations for strengthening cybersecurity compliance across complex enterprises. Open Access Research Journal of Multidisciplinary Studies, 2(1), pp.871–881.

[117] Orlikowski, W.J. and Barley, S.R., 2001. Technology and institutions: What can research on information technology and research on organizations learn from each other? MIS Quarterly, 25(2), pp.145–165.

[118] Osabuohien, F.O., 2017. Review of the environmental impact of polymer degradation. Communication in Physical Sciences, 2(1).

[119] Osabuohien, F.O., Omotara, B.S. and Watti, O.I., 2021. Mitigating Antimicrobial Resistance through Pharmaceutical Effluent Control: Adopted Chemical and Biological Methods and Their Global Environmental Chemistry Implications.

[120] Osabuohien, F.O., 2019. Green Analytical Methods for Monitoring APIs and Metabolites in Nigerian Wastewater: A Pilot Environmental Risk Study. Communication In Physical Sciences, 4(2), pp.174-186.

[121] Patton, M.Q., 1999. Enhancing the quality and credibility of qualitative analysis. Qualitative Inquiry, 5(4), pp.403–415.

[122] Power, M., 2004. The risk management of everything: Rethinking the politics of uncertainty. London: Demos.

[123] Puschmann, T., 2017. Fintech. Business & Information Systems Engineering, 59(1), pp.69–76.

[124] Rappaport, A., 1998. Creating shareholder value: A guide for managers and investors. New York: Simon and Schuster.

[125] Reeb, D.M., Mansi, S.A. and Allee, J.M., 2001. Firm internationalization and the cost of debt financing: Evidence from non-provisional borrowers. Journal of Financial Economics, 59(1), pp.125–153.

[126] Richards, D., Yeoh, W. and Teoh, S.Y., 2019. Big data analytics for treasury risk: Framework and case study. International Journal of Accounting Information Systems, 33, pp.45–60.

[127] Ross, S.A., Westerfield, R.W. and Jaffe, J., 2005. Corporate finance. 7th ed. New York: McGraw-Hill.

[128] Schroeck, G., 2002. Risk management and value creation in financial institutions. Hoboken: Wiley Finance.

[129] Smith, J. and Walter, I., 2002. Global banking. Oxford: Oxford University Press.

[130] Tufano, P., 1996. Who manages risk? An empirical examination of risk management practices in the gold mining industry. The Journal of Finance, 51(4), pp.1097–1137.

[131] Vasarhelyi, M.A., Kogan, A. and Tuttle, B.M., 2015. Big data in accounting: An overview. Accounting Horizons, 29(2), pp.381–396.

[132] Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234–246.

@article{1709992,
      author = {Ayoola Olamilekan Sikiru, Onyeka Kelvin Chima, Mary Otunba, Olatunde Gaffar, Adedoyin Adeola Adenuga},
      title = {AI in the Treasury Function: Optimizing Cash Forecasting, Liquidity Management, and Hedging Strategies},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {4},
      pages = {395-421},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709992.pdf},
      abstract = {The integration of Artificial Intelligence (AI) into the treasury function represents a transformative shift in how organizations manage financial operations, especially in the domains of cash forecasting, liquidity management, and hedging strategies. This paper examines the impact of AI-driven solutions on enhancing the accuracy, efficiency, and strategic depth of treasury processes. Traditional treasury practices often rely on manual data collection, static models, and reactive decision-making, which limit responsiveness and expose firms to financial risk. However, with the rise of AI technologies?particularly machine learning, natural language processing, and predictive analytics?treasurers can now process large datasets in real time, identify patterns with higher precision, and forecast cash positions with greater confidence. In cash forecasting, AI enables dynamic models that adjust to market shifts and internal transactional behavior, reducing forecasting errors and enhancing short-term liquidity planning. In liquidity management, AI tools provide continuous visibility into global cash positions, automate surplus allocation, and ensure real-time optimization of working capital. These improvements support strategic decision-making by enabling treasury teams to respond quickly to market volatility and regulatory changes. Furthermore, in the area of hedging, AI algorithms can analyze market trends, correlate risk exposures, and recommend optimal hedging instruments, leading to more resilient and cost-effective risk mitigation strategies. The paper also discusses the challenges of AI adoption, including data quality, integration with legacy systems, and governance considerations. By leveraging case studies and empirical findings, this research underscores the critical role AI plays in reshaping the future of corporate treasury and driving financial agility. As organizations increasingly prioritize digital transformation, embedding AI in treasury workflows is no longer optional but essential for maintaining competitive advantage, improving stakeholder confidence, and achieving operational resilience.},
      keywords = {Artificial Intelligence, Treasury Function, Cash Forecasting, Liquidity Management, Hedging Strategies, Machine Learning, Financial Risk, Predictive Analytics, Corporate Finance, Digital Transformation.},
      month = {October},
  }