Volume 3 • Issue 2 • PP: 35-45 • 2026
A Dual-Bank Hybrid Predictive Model (DBHPM) for Financial Forecasting
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Forecasting of the financial performance is significant mainly for the purpose of strategy formulation and identification of potential problems in banking institutions. This paper presents a new model of a predictive model for financial forecasting called the Dual-Bank Hybrid Predictive Model which consists of a Multiple Linear Regression and Random Forest Regression. This model is also validated on two actual financial datasets of Agrobank and NBU Bank from the year 2021 to 2025. It also relies on the analysis of such financial ratiosas Net profit, Equity, and Solvency which have been forecasted up to the year 2027. Specifically, while the DBHPM consists of linear modeling through MLR in the first step, and then, nonlinear residuals thru RFR in the second step of the analysis, the former provides increased generalizations and predictive strength as compared to the later stage solely. The experimental results show that DBHPM minimizes MAE and RMSE achieving the coefficient of determination (R2) amounting to 0.95 and above if compared to the models trained independently. Statistical modelling shows that the two banks go up with Agrobank at approximately 1.18 billion sum and NBU Bank at 3.66 billion sum of the net profit by the end of 2027. The outlined hybrid model presents the possibility of better predictive analytics financial modelling in the banking industry for purposes of, decision-making, risk alertness, and economic forecast.
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