Advanced Customer Behavior Forecasting for Retail and Financial
Decision-Making Using Physics-Based Intelligence
Ebrahim A. Mattar 1,*, S. K. Towfek2,3
1College of Engineering University of Bahrain, Bahrain
2Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
3Jadara Research Center, Jadara University, Irbid 21110, Jordan
Emails: ebmattar@uob.edu.bh; sktowfek@jcsis.org
Abstract
Accurate prediction of customer behavior remains a core methodological and operational challenge in retail
economics and financial decision-making, particularly as institutions increasingly depend on data-driven
forecasting systems to improve credit risk assessment, refine customer segmentation, and deliver targeted
financial services in competitive and rapidly changing markets. In practice, the economic value of customer
behavior prediction lies in its direct connection to profit maximization, loss minimization, and resource
allocation efficiency: retailers seek to anticipate spending tendencies and product affinities to reduce marketing
waste and optimize inventory, while financial institutions aim to infer creditworthiness and repayment
capacity to reduce default exposure and enhance portfolio stability. Despite the demonstrated advantages
of data-driven approaches, the predictive performance of advanced learning systems in such contexts is
frequently constrained by the dual challenge of high-dimensional, heterogeneous feature spaces and the
sensitivity of model outcomes to hyperparameter choices, often resulting in limited generalization, unstable
convergence, or performance degradation when applied to unseen customer groups. To address these
constraints, this study develops an integrated optimization framework that couples a high-capacity predictive
model with a physics-inspired search mechanism, namely the Kirchhoff’s Law Algorithm (KLA), and employs
it for automated hyperparameter optimization of an End-to-End Attention Long Short-Term Memory model
(EALSTM), thereby reducing reliance on manual tuning and improving model reliability under financially
meaningful data complexity. In addition to introducing the KLA-driven optimization pipeline, the study
conducts a rigorous comparative evaluation against established state-of-the-art metaheuristic optimizers,
including Particle Swarm Optimizer (PSO), Biogeography Based Optimizer (BBO), Whale Optimization
Algorithm (WOA), Bat Algorithm (BA), Artificial Protozoa Optimizer (APO), Genetic Algorithm (GA),
and Stochastic Fractal Search (SFS), enabling a systematic assessment of how different search dynamics
influence predictive quality in customer analytics applications. Experimental evaluation is performed using
an enhanced customer dataset that integrates demographic descriptors, behavioral spending indicators, and
financially meaningful constructs—thereby better reflecting real-world decision environments where customer
profiling depends on both consumption behavior and financial capacity—and the results demonstrate that
the KLA + EALSTM configuration consistently achieves the strongest predictive performance across the
full suite of regression metrics. Specifically, KLA + EALSTM attains a Mean Squared Error (MSE)
of 3.60 × 10−7, a Root Mean Squared Error (RMSE) of 0.00728, a Mean Absolute Error (MAE) of
0.000372, a Mean Bias Error (MBE) of 0.000091, a correlation coefficient (r) of 0.972, a coefficient of
determination (R2) of 0.969, a Relative RMSE (RRMSE) of 0.095, a Nash–Sutcliffe Efficiency (NSE) of
0.971, and a Willmott Index (WI) of 0.977, collectively indicating extremely low error magnitude, minimal
systematic bias, strong explanatory power, and high agreement between predicted and observed outcomes,
and representing a substantial improvement over the unoptimized EALSTM baseline. From an economic
and financial viewpoint, these gains are practically consequential because they strengthen the reliability
of predictive decision-support systems used for credit scoring, personalized marketing, customer value
DOI: https://doi.org/10.54216/JSDGT.050202 30
Journal of Sustainable Development and Green Technology (JSDGT) Vol. 05, No. 02, PP. 30-58, 2025
assessment, and financially efficient resource allocation, where even small prediction errors can translate
into measurable cost, risk, or revenue impacts. Overall, the findings provide strong empirical support for
physics-inspired, non-parametric optimization as a robust mechanism for improving predictive accuracy,
stability, and generalization in customer analytics, and they position KLA-based optimization as a scalable
and methodologically efficient solution for next-generation retail and financial analytics systems operating
under high-dimensional behavioral and financial data conditions.
Keywords: Customer behavior analytics; Financial prediction; Deep learning optimization; Metaheuristic
algorithms; Retail and financial decision-making