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<p>In contemporary Financial Planning and Analysis (FP&amp;A), profitability forecasting is a core component of the process that enables organizations to make informed, evidence-based strategic decisions, allocate resources effectively, and navigate unstable markets. Nevertheless, current models tend to fail to describe the nonlinear and volatile trend of the financial data, especially in cases of uncertainty. These constraints lead to less than optimal forecasting accuracy, along with the lack of ability to make decisions in real time. This paper contributes to solving all these issues by introducing innovative ideas of the hybrid intelligence framework, which involves the use of the best of both worlds, deep learning, and nature-inspired optimization to achieve better results in predicting profitability. This paper proposes the Variational Reinforced Integrated Computation Network (VARICO-Net), which is a deep reinforcement-based model, incorporating both convolutional and sequential modeling layers in order to learn both time and space features of financial data. Coupled with this, we make the Black-winged Kite Tuned Engine (B-KiTE), a new optimization engine that is based on the predatory foraging behavior of the black-winged kite, and can generate the most accurate divergent weight estimations and optimality of convergence in large function surfaces. In unison, VARICO-Net and B-KiTE combine into a strong and sound predictive model that can carry out effective and accurate profitability projections. The offered work plan includes the preprocessing of the historical financial information available in the UCI Bank Marketing Dataset and the Corporate Bankruptcy Prediction dataset, as well as their feature selection and sequential representation. The experiment demonstrates that the suggested approach is highly effective and has an accuracy of 98.9%, a recall of 98.95%, and an F1-score of 99%, making it effective in identifying trends in profitability.</p>

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Divergence-driven forecasting of financial profitability using perception neural learning and adaptive heuristic tuning models

  • 期刊论文

  • 发表日期：

    2026年7月12日

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  • Suresh Suggula,
  • Kiran Mannem,
  • S. Ravi Chand,
  • Pasumarthy Nageswara Rao

摘要

In contemporary Financial Planning and Analysis (FP&A), profitability forecasting is a core component of the process that enables organizations to make informed, evidence-based strategic decisions, allocate resources effectively, and navigate unstable markets. Nevertheless, current models tend to fail to describe the nonlinear and volatile trend of the financial data, especially in cases of uncertainty. These constraints lead to less than optimal forecasting accuracy, along with the lack of ability to make decisions in real time. This paper contributes to solving all these issues by introducing innovative ideas of the hybrid intelligence framework, which involves the use of the best of both worlds, deep learning, and nature-inspired optimization to achieve better results in predicting profitability. This paper proposes the Variational Reinforced Integrated Computation Network (VARICO-Net), which is a deep reinforcement-based model, incorporating both convolutional and sequential modeling layers in order to learn both time and space features of financial data. Coupled with this, we make the Black-winged Kite Tuned Engine (B-KiTE), a new optimization engine that is based on the predatory foraging behavior of the black-winged kite, and can generate the most accurate divergent weight estimations and optimality of convergence in large function surfaces. In unison, VARICO-Net and B-KiTE combine into a strong and sound predictive model that can carry out effective and accurate profitability projections. The offered work plan includes the preprocessing of the historical financial information available in the UCI Bank Marketing Dataset and the Corporate Bankruptcy Prediction dataset, as well as their feature selection and sequential representation. The experiment demonstrates that the suggested approach is highly effective and has an accuracy of 98.9%, a recall of 98.95%, and an F1-score of 99%, making it effective in identifying trends in profitability.