A Predictive Analytics Model for Strategic Business Decision-Making: A Framework for Financial Risk Minimization and Resource Optimization

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@article{1704971,
      author = {Favour Uche Ojika, Osazee  Onaghinor, Oluwafunmilayo Janet Esan, Andrew Ifesinachi Daraojimba, Bright Chibunna Ubamadu},
      title = {A Predictive Analytics Model for Strategic Business Decision-Making: A Framework for Financial Risk Minimization and Resource Optimization},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {2},
      pages = {764-776},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704971.pdf},
      abstract = {This paper presents a comprehensive framework for a predictive analytics model to enhance strategic business decision-making, specifically focusing on financial risk minimization and resource optimization. The increasing complexity of the business environment necessitates the adoption of advanced analytics to navigate uncertainties and improve organizational performance. This study outlines the development of a hybrid predictive analytics model that integrates regression analysis with machine learning techniques, allowing for accurate forecasting of financial risks and identification of key resource allocation strategies. The methodology involved rigorous model testing and validation, utilizing historical data to establish significant predictors of financial risk, including economic indicators and customer demographics. Results indicate that the model demonstrates high predictive accuracy, as evidenced by low error metrics, and effectively facilitates resource optimization by enabling organizations to allocate investments strategically based on predicted financial outcomes. The implications of these findings highlight the necessity for organizations to adopt a data-driven culture and leverage predictive insights for improved decision-making. Practical recommendations for implementing the model include fostering cross-functional collaboration, investing in data management infrastructure, and regularly updating the predictive framework to reflect real-time conditions. Future research directions emphasize exploring the impact of external factors, industry-specific applications, and the ethical considerations surrounding predictive analytics. While this study acknowledges limitations such as reliance on historical data and potential model overfitting, it contributes to the growing body of knowledge on predictive analytics and offers a valuable tool for organizations seeking to enhance their strategic decision-making capabilities.},
      keywords = {Predictive Analytics, Financial Risk Minimization, Resource Optimization, Machine Learning, Decision-Making, Business Strategy},
      month = {August},
  }