Predictive Analytics in Financial Forecasting: Methods, Applications, and Challenges

Authors

Shree Chand Chhimpa

Abstract

Predictive analytics plays a crucial role in financial forecasting, offering organizations the ability to anticipate future trends, mitigate risks, and make data-driven decisions. This paper provides an in-depth exploration of predictive analytics in financial forecasting, covering methods, applications, challenges, and emerging trends. Through case studies and empirical examples, we illustrate the practical applications and tangible benefits of predictive analytics across various industries, including retail, banking, and telecommunications. We discuss key methodologies such as regression analysis, time series forecasting, and machine learning algorithms, highlighting their role in sales forecasting, stock market prediction, credit risk assessment, and customer churn prediction. Additionally, we examine challenges such as data quality issues, model complexity, and regulatory compliance, and discuss emerging trends such as the integration of artificial intelligence, real-time analytics, and ethical AI practices. By embracing these trends and leveraging advanced analytics techniques, organizations can enhance their predictive capabilities, drive strategic decision-making, and unlock new opportunities for value creation in the dynamic landscape of finance and business.

Keywords

Predictive analytics, financial forecasting, regression analysis, time series forecasting, machine learning, case studies, challenges, emerging trends

Predictive Analytics in Financial Forecasting: Methods, Applications, and Challenges. Shree Chand Chhimpa. 2024. IJIRCT, Volume 10, Issue 1. Pages 1-8. https://www.ijirct.org/viewPaper.php?paperId=2402008