Volume 5 • Issue 2 • PP: 38–54 • 2025
Accurate Customer Financial Prediction Using Data-Driven Analytics in Retail Economics
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The growing availability of granular customer-level data has intensified the demand for accurate and robust predictive models in retail economics and consumer finance, particularly for forecasting financially relevant indicators such as savings capacity and credit-related measures, where prediction inaccuracies can lead to inefficient pricing strategies, misallocation of financial resources, and distorted risk assessments. Traditional statistical and econometric approaches often struggle to model the nonlinear and high-dimensional relationships inherent in such data, motivating the use of advanced deep learning techniques combined with intelligent optimization strategies. This study proposes an integrated economic and financial analytics framework that couples a sequence-to-sequence deep learning architecture (Sequence-to-Sequence, Seq2Seq) with state-of-the-art metaheuristic optimization algorithms for automated hyperparameter tuning, with particular emphasis on the Puma Optimizer–Seq2Seq (PO + Seq2Seq) configuration. The framework systematically evaluates multiple baseline deep learning models and enhances them through metaheuristic-driven optimization to address challenges related to convergence stability, generalization capability, and model sensitivity in customer-level financial prediction. Empirical analysis shows that the PO + Seq2Seq model consistently outperforms all baseline and alternative optimized configurations across all evaluation stages, achieving a Mean Squared Error of 2.05 × 10−5, Root Mean Squared Error of 4.52 × 10−3, Mean Absolute Error of 2.05 × 10−4, and a very small Mean Bias Error of 5.40 × 10−5, together with strong goodness-of-fit and efficiency indicators, including a correlation coefficient of 0.987, R2 of 0.983, Nash–Sutcliffe Efficiency of 0.986, and Willmott Index of 0.988. From an economic and financial perspective, these findings demonstrate that the proposed PO + Seq2Seq framework provides a reliable and scalable predictive tool for customer analytics, enabling more accurate assessment of financial behavior, improved customer segmentation, and enhanced decision support in retail finance and consumer-oriented financial systems, while highlighting the critical role of metaheuristic optimization in unlocking the full predictive potential of deep learning models for real-world economic applications.
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