Authors
Iman Supriadi,Mochamad Fatchurrohman,Alfiyatussholichah,Parwita Setya Wardhani.
STIE Mahardhika Surabaya, Indonesia
Abstract
The rapid expansion of neo-banks challenges the conventional assumption that a lower Cost-to-Income Ratio (CIR) always improves profitability. This study investigates the non-linear relationship between CIR and Return on Equity (ROE) and examines how digital capabilities reshape financial performance during the transition to digital banking maturity. It introduces the Digital Efficiency Threshold and proposes the Digital Moat Theory by integrating Dynamic Capabilities Theory, Stochastic Frontier Analysis, and Explainable Artificial Intelligence (XAI) to identify the tipping point at which technology investments shift from cost burdens to value creators. Using panel data from 100 global neo-banks across five regions (2023–2026), the study applies a Random Forest model with Shapley Additive explanations (SHAP) to capture and interpret complex non-linear relationships among CIR, Loan-to-Deposit Ratio (LDR), regional characteristics, and ROE.
Keywords
Digital Banking Cost-to-Income Ratio Explainable Artificial Intelligence (XAI) ADynamic Capabilities Theory Digital Efficiency Threshold
How to Cite This Article
APA Citation
Supriadi, I. (2026). Explainable AI Unveils the Digital Efficiency Threshold in the Nonlinear Profitability Dynamics of Global Neo-Banks. International Journal of Economics and Management Intellectuals, 2(1), 64–78.
Conclusion
This study successfully deconstructs the non-linear relationship between the Cost-to-Income Ratio (CIR) and Return on Equity (ROE) across 100 global neo-banks during the 2023–2026 period by employing an Explainable Artificial Intelligence (XAI) framework based on Random Forest and Shapley Additive explanations (SHAP). The findings demonstrate that the relationship between operational efficiency and profitability no longer conforms to the linear paradigm assumed by traditional banking theory. Instead, technology investments that initially increase the Cost-to-Income Ratio (CIR) represent a process of digital capability accumulation that serves as a prerequisite for the development of sustainable competitive advantage. The SHAP analysis identifies CIR as the primary determinant of profitability and provides empirical evidence for the existence of the Digital Efficiency Threshold, defined as the critical point at which digital investment transitions from a cost center to a revenue generator. These findings extend Dynamic Capabilities Theory through the introduction of the Digital Moat Theory, which posits that the economies of scale achieved by neo-banks are driven by the accumulation of technological capabilities capable of reducing marginal operating costs to near-zero levels, rather than by the expansion of physical assets alone. Accordingly, this study makes meaningful theoretical, methodological, and practical contributions to understanding the mechanisms through which profitability is created in the digital banking industry.
Nevertheless, several limitations should be acknowledged. First, the empirical model incorporates only three principal explanatory variables the Cost-to-Income Ratio (CIR), the Loan-to-Deposit Ratio (LDR), and regional characteristics and therefore does not fully capture other factors that may influence bank profitability, including corporate governance quality, the level of digital innovation, cybersecurity risk, macroeconomic conditions, and customer behavior. Second, the observation period is limited to 2023–2026, which does not permit an examination of the long-term dynamics throughout the entire life cycle of neo-banks. Future research is therefore encouraged to expand the analytical framework by incorporating a broader range of financial and non-financial indicators, extending the observation horizon, and comparing multiple Explainable Artificial Intelligence algorithms such as XGBoost, LightGBM, and CatBoost to evaluate the robustness and generalizability of the Digital Efficiency Threshold across different fintech business models and banking systems. Such efforts are expected to strengthen the external validity of the findings while further refining the Digital Moat Theory as a novel conceptual framework within the digital finance literature.
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