Vol. 1 No. 1 (2021): African Journal of Artificial Intelligence and Sustainable Development
Articles

Leveraging Predictive Analytics for Financial Forecasting in a Post-COVID World

Piyushkumar Patel
Accounting Consultant at Steelbro International Co., Inc, USA
Hetal Patel
Manager- finance department at Jamaica hospital, USA
Deepu Jose
Audit - Manager at Baker Tilly , USA
Disha Patel
CPA Tax Manager at Deloitte, USA
Cover

Published 13-01-2021

Keywords

  • Predictive Analytics,
  • Financial Forecasting

How to Cite

[1]
Piyushkumar Patel, Hetal Patel, Deepu Jose, and Disha Patel, “Leveraging Predictive Analytics for Financial Forecasting in a Post-COVID World”, African J. of Artificial Int. and Sust. Dev., vol. 1, no. 1, pp. 331–350, Jan. 2021, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/225

Abstract

The post-COVID world has brought unprecedented challenges and opportunities for financial forecasting, requiring businesses to adopt more sophisticated tools and strategies to navigate uncertainty. Predictive analytics has emerged as a cornerstone in this transformation, empowering organizations to leverage historical data, machine learning algorithms, and real-time insights to anticipate financial trends more accurately. By identifying patterns and anomalies in large datasets, predictive analytics enables businesses to make informed decisions, optimize cash flow, manage risks, and respond proactively to market fluctuations. Retail, banking, and manufacturing industries have benefited from using predictive models to forecast demand, assess credit risk, and streamline supply chains. The pandemic underscored the need for agile forecasting approaches as traditional methods struggled to account for rapid changes in consumer behaviour and economic conditions. Predictive analytics fills this gap by integrating external variables like macroeconomic indicators, social sentiment, and global events into forecasting models. Organizations can now scenario-plan effectively, preparing for potential disruptions while seizing growth opportunities. However, implementing predictive analytics in financial forecasting requires overcoming challenges such as data quality issues, integration complexities, and the need for skilled talent. Companies that invest in robust data pipelines, scalable technologies, and interdisciplinary collaboration are better positioned to harness their full potential. As businesses continue to adapt to the pandemic's ripple effects, predictive analytics is a vital tool, helping leaders build resilience, drive strategic initiatives, and navigate an increasingly volatile global economy. Bridging the gap between data and decision-making transforms financial forecasting from a reactive process into a proactive, insight-driven strategy, ensuring organizations remain competitive and agile in an evolving landscape.

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