Hybrid Predictive Analytics for Short-Term Financial Forecasting in Digital Banking Environments

  • Authors

    • Karthick Quality Analyst, USA. Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-V9I3P103

    Published 08-22-2026

  • Hybrid Predictive Analytics, Short-Term Financial Forecasting, Digital Banking, Machine Learning, Time-Series Analysis, Financial Data Analytics, Predictive Modelling

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    Digital banking produces massive quantities of financial data; therefore, accurate and timely forecasts are essential for making informed decisions. Traditional methods of forecasting are often unable to identify complicated financial trends, non-linear relationships, and the rapid pace of changes within markets. To address these limitations, this study presents a hybrid predictive analytics model which combines statistical forecasting with machine learning models to provide better short-term financial forecasts in digital banking systems. A comprehensive predictive analytics process is established by integrating the preprocessing of financial data, selection of features, development of predictive models, and assessment of performance. MAE, RMSE and MAPE can be used to evaluate how well each model performs. This hybrid model can be utilized for predicting transactions, managing cash flow and developing financial plans. However, the success of this hybrid model will depend upon its validation through application of real-world banking data sets and experimentation.

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