Accurate Customer Financial Prediction Using Data-Driven Analytics
in Retail Economics
Shahid Mahmood1,*, Mahmoud Elshabrawy Mohamedr2
1School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China
2Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
Emails: shahidnajam786@live.com; mshabrawy@jcsis.org
Abstract
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.
Keywords: Customer Financial Analytics; Retail Economics; Deep Learning Forecasting; Metaheuristic
Optimization; Consumer Finance Prediction