Optimizing Liquidity and Cash Flow Management in Retail Banking Through Predictive Analytics: ARIMA, Regression, and Real-Time Forecasting Approaches

Authors: Preetham Reddy Kaukuntla

DOI: https://doi.org/10.5281/zenodo.14762771

Short DOI: https://doi.org/g83jc8

Country: USA

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Abstract: Proper liquidity and cash flow management are fundamental to the survival of retail banking institutions. This paper shall focus on various ways through which predictive analytics applies to the optimization of the forecasting of liquidity and cash flow in a retail banking setup. Major techniques used in these discussions shall include ARIMA, regression analysis, and real-time approaches to forecasting. This paper aims to appraise the accuracy and applicability of such models concerning the predictability of levels of liquidity, enhanced operational efficiency, and diminished risks from cash flow volatility. It uses real data on banks to show how this method can improve decision-making mitigate risks and optimize the use of cash management practices. The results are significant since they imply that using models of ARIMA and regression methods may significantly enhance the accuracy of forecasting. Real-time approaches in forecasting increase the flexibility for short-term decisions. The paper elaborates on the role and responsibility of liquidity management in retail banking, offering insight into its potential to enhance innovation and financial stability.

Keywords: Liquidity Management in Banking, Analytics in Finance, Cash Flow Forecasting Models, Machine Learning in Retail Banking, ARIMA, and Regression in Banking Forecasting.

Paper Id: 232075

Published On: 2019-10-08

Published In: Volume 7, Issue 5, September-October 2019

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