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Agile-Predictive Convergence: A new paradigm for smart Investment and Risk Management Platforms

Neliswa Clementine Matsebula Carlos Postigo Toledo Judith Saungweme Mabasa Masunungure Munashe Naphtali Mupa

Subject area: Science,Engineering and Technology  ·  Area of research: Finance and Investments

Abstract

Older investment and risk management systems are struggling with rising market volatility, increased indignity of rules practice, and a rapid rate of technological advances. This paper will discuss the possibilities of combining predictive analysis with agile product delivery approaches to streamline systems' responsiveness and improve decision-making practices between various industries in the financial and energy domains. Based on a thorough literature review, framework development, and case studies, we suggest a four-step framework for systematically integrating predictive intelligence into agile workflows. When transferring to energy sector investment systems, this system has shown improved adaptability, shorter iteration cycles, better anticipation of risk, and enhanced fit-gap between technical capacity and business goals. The results indicate that agile-predictive integration is a strategic need of companies that work in an environment of uncertainty and where data-intensive operations do not respond to traditional developmental strategies.

References

[1] Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., ... & Thomas, D. (2001). Manifesto for agile software development. Agile Alliance. Retrieved from https://agilemanifesto.org/

[2] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to significant impact. MIS Quarterly, 36(4), 1165–1188.

[3] Deloitte. (2023). Energy transition and digital transformation: Navigating complexity in the new energy landscape. Deloitte Insights. Retrievedfromhttps://www2.deloitte.com/insigh ts/us/en/industry/power-and-utilities/energy- transition-digital-transformation.html

[4] Dingsøyr, T., Nerur, S., Balijepally, V., & Moe, N. B. (2012). A decade of agile methodologies: Towards explaining agile software development. Journal of Systems and Software, 85(6), 1213-1221.

[5] Härdle, W. K., Harvey, C. R., & Reule, R. C. (2018). Understanding cryptocurrencies. Journal of Financial Econometrics, 18(2), 181– 208.

[6] McKinsey & Company. (2023). Global Energy Perspective 2023: Energy insights. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/industries/oil-and- gas/our-insights/global-energy-perspective-2023

[7] Nandhakumar, J., & Avison, D. E. (1999). The fiction of methodological development: A field study of information systems development. Information Technology & People, 12(2), 176– 191.

[8] Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media.

[9] PwC. (2023). 26th Annual Global CEO Survey: Energy transition survey . PricewaterhouseCoopers. Retrieved from https://www.pwc.com/gx/en/ceo- agenda/ceosurvey/2023/energy.html

[10] Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.

[11] Sioshansi, R. (2019). Consumer, prosumer, prosumager: How service innovations will disrupt the utility business model. Academic Press.

[12] Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019). Review of smart meter data analytics: Applications, methodologies, and challenges. IEEE Transactions on Smart Grid, 10(3), 3125– 3148.

[13] Mupa, M. N. (2024). Corporate governance and firm performance: A study of selected South African energy companies. IRE Journals, 8(2). https://www.researchgate.net/publication/38303 9611_Corporate_Governance_and_Firm_Perfor mance_A_Study_of_Selected_South_African_E nergy_Companies

[14] MUPA, M. N., TAFIRENYIKA, S., RUDAVIRO, M., NYAJEKA, T., MOYO, M., & ZHUWANKINYU, E. K. (2025). Machine Learning in Actuarial Science: Enhancing Predictive Models for Insurance Risk Management. vol, 8, 493504.https://www.researchgate.net/profile/M unasheNaphtaliMupa/publication/389132064_ Machine_Learning_in_Actuarial_Science_Enha ncing_Predictive_Models_for_Insurance_Risk_ Management/links/67b60a83645ef274a4897f9a/ Machine-Learning-in-Actuarial-Science- Enhancing-Predictive-Models-for-Insurance- Risk-Management.pdf

[15] Mupa, M. N., Chiganze, F. R., Mpofu, T. I., Mangeya, R., & Mubvuta, M. (2024). The evolving role of management accountants in risk management and internal controls in the energy sector. IRE Journals, 8(2). https://www.researchgate.net/publication/38405 5180_The_Evolving_Role_of_Management_Ac countants_in_Risk_Management_and_Internal_ Controls_in_the_Energy_Sector

[16] Mupa, M. N., Kalu-Mba, N., & Tafirenyika, S. (2025). Artificial intelligence as a catalyst for innovation in the public sector: Opportunities, risks, and policy imperatives. IRE Journals, 8(11). https://www.researchgate.net/publication/39173 6874_Artificial_Intelligence_as_a_Catalyst_for _Innovation_in_the_Public_Sector_Opportuniti es_Risks_and_Policy_Imperatives

How to cite this paper

Neliswa Clementine Matsebula, Carlos Postigo Toledo, Judith Saungweme, Mabasa Masunungure, Munashe Naphtali Mupa "Agile-Predictive Convergence: A new paradigm for smart Investment and Risk Management Platforms" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 1247-1257
Neliswa Clementine Matsebula, Carlos Postigo Toledo, Judith Saungweme, Mabasa Masunungure, Munashe Naphtali Mupa "Agile-Predictive Convergence: A new paradigm for smart Investment and Risk Management Platforms" Iconic Research And Engineering Journals, vol. 9, no. 1, Jul. 2025
Neliswa Clementine Matsebula, Carlos Postigo Toledo, Judith Saungweme, Mabasa Masunungure, Munashe Naphtali Mupa (2025). Agile-Predictive Convergence: A new paradigm for smart Investment and Risk Management Platforms. Iconic Research And Engineering Journals, 9(1).
Neliswa Clementine Matsebula, Carlos Postigo Toledo, Judith Saungweme, Mabasa Masunungure, Munashe Naphtali Mupa "Agile-Predictive Convergence: A new paradigm for smart Investment and Risk Management Platforms" Iconic Research And Engineering Journals, vol. 9, no. 1, Jul. 2025.
@article{1709796,
      author = {Neliswa Clementine Matsebula, Carlos Postigo Toledo, Judith Saungweme, Mabasa Masunungure, Munashe Naphtali Mupa},
      title = {Agile-Predictive Convergence: A new paradigm for smart Investment and Risk Management Platforms},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {1247-1257},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709796.pdf},
      abstract = {Older investment and risk management systems are struggling with rising market volatility, increased indignity of rules practice, and a rapid rate of technological advances. This paper will discuss the possibilities of combining predictive analysis with agile product delivery approaches to streamline systems' responsiveness and improve decision-making practices between various industries in the financial and energy domains. Based on a thorough literature review, framework development, and case studies, we suggest a four-step framework for systematically integrating predictive intelligence into agile workflows. When transferring to energy sector investment systems, this system has shown improved adaptability, shorter iteration cycles, better anticipation of risk, and enhanced fit-gap between technical capacity and business goals. The results indicate that agile-predictive integration is a strategic need of companies that work in an environment of uncertainty and where data-intensive operations do not respond to traditional developmental strategies.},
      month = {July},
  }