The study was determined by the growing need to quantify behavioural and institutional transformations driven by algorithmic digital finance platforms. This justified developing an integrative analytical framework for behavioural institutional assessment within artificial intelligence (AI)-driven financial ecosystems.
The aim of the study was to develop and empirically substantiate a multidimensional framework for assessing the transformative influence of algorithmic digital finance systems on financial behaviour and responsibility, integrating cognitive, institutional, and ethical dimensions of digital financial governance.
Research methods: Comparative Qualitative Critical Assessment, ITI Modelling of Behavioural and Institutional Transformation, Structural Decomposition of Robo-Advisory Systems, Optimized Logic-Structural Architecture, ITI Verification of the Optimized Framework.
The integrated research validated the systemic transformation capacity of algorithmic digital finance platforms through behavioural and institutional dimensions. Comparative analysis revealed asymmetric configurations of behavioural rationalization and responsibility formalization, emphasizing architecture-specific functional differentiation. ITI modelling quantified these interdependencies, producing average indices of behavioural transformation (0.81) and responsibility institutionalization (0.77). Structural and optimization modelling of Robo-Advisory Systems demonstrated enhanced algorithmic transparency, cognitive traceability, and compliance integrity. Final ITI verification confirmed the optimized Robo-Advisory System as the most effective configuration (ITI* = 0.86; Bₚ = 0.85; Rₚ = 0.90), substantiating its role as a scalable paradigm for ethical and cognitively rational digital financial governance.
The academic novelty of the study lies in the development of an integrated behavioural-institutional assessment framework for evaluating algorithmic digital finance systems. The Integral Transformative Index (ITI*) is not positioned as a new mathematical operator; rather, it operationalizes a theoretically grounded construct that combines behavioural transformation and responsibility institutionalization within a single analytical architecture. The innovation is determined by the original conceptual linkage, indicator taxonomy, and platform-specific interpretation of cognitive rationalization, algorithmic transparency, and institutional accountability. The Optimized Robo-Advisory System (ORAS) model embodied this construct through explainability, bias-correction, traceability, and ethical governance mechanisms.
The next stage of research will focus on the algorithmic improvement and structural enhancement of the ORAS framework developed in this study. Controlled pilot implementation will be conducted to test behavioural adaptability, ethical compliance, and regulatory resilience. Subsequent multi-phase empirical validation will ensure the full verification of cognitive, normative, and operational dimensions of the optimized model.