Machine Learning and Hybrid Optimization Approaches for Predicting Investor Behavior in the Digital Finance Ecosystem

Ashwini V. Rathi; Rajkumar Jha

doi:https://doi.org/10.14445/22315381/IJETT-V74I8P106

Research Article | Open Access | Download PDF

Volume 74 | Issue 8 | Year 2026 | Article Id. IJETT-V74I8P106 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I8P106

Machine Learning and Hybrid Optimization Approaches for Predicting Investor Behavior in the Digital Finance Ecosystem

Ashwini V. Rathi, Rajkumar Jha

Received	Revised	Accepted	Published
30 Mar 2026	19 Jun 2026	24 Jun 2026	29 Aug 2026

Citation :

Ashwini V. Rathi, Rajkumar Jha, "Machine Learning and Hybrid Optimization Approaches for Predicting Investor Behavior in the Digital Finance Ecosystem," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 8, pp. 80-98, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I8P106

Abstract

Interest in the effects of digitalization on investment behavior has increased as researchers have focused on the ramifications of the digitalization of financial ecosystems. Although the majority of the literature has employed survey-based methods with PLS-SEM, these studies are limited by small sample sizes, geographical self-reporting biases, and issues related to the statistical power of the studies. This paper offers the first complete integrative framework demonstrating, along all ten evaluative dimensions, the dominance of an AI-driven hybrid optimization method over traditional survey-based methods. This paper compares the results of the analysis of two methodologies: one using PLS SEM method (N=709, Vidharbha Maharashtra, PLS-SEM) and other using ALO- PG algorithm (N=3,000, World Bank Global Findex India 2021, ALO-PG, AI driven). Methodology-wise, both tests the same six structural hypotheses. The paper also offers engineering-grade proof of ALO-PG superiority through Convergence analysis with training loss and accuracy curves, analysis of the confusion matrix, ROC and AUC comparisons, an example of an ALO-PG mathematical formulation and pseudocode, an ablation study, McNemar’s significance tests with Cohen’s h effect sizes, ALO-PG and dataset demographic profile, computational complexity and runtime analysis, and 10-fold cross-validation for ALO-PG to confirm generalizability. ALO-PG records astonishing results with an accuracy of 95.67% (AUC=0.9771, F1=0.9565) and a t- statistic 12.1 times stronger than PLS-SEM. It also validates, for the first time, Hypothesis H6, with β=+0.959 (p<0.001).

Keywords

ALO-PG, Digitalization, Hybrid optimization, Investment behavior, Machine Learning.

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