An ai-enabled strategy artefact for digital gold ecosystems: A design science study

Abstract

Background: Digital financial ecosystems generate high-volume, heterogeneous, and fast-moving behavioral data, yet many firms still lack a coherent strategic mechanism for converting these signals into timely, governable, and value-creating decisions. This study aimed to design and evaluate an artificial intelligence-enabled strategy artifact for digital gold ecosystems that operationalize algorithmic sensing, seizing, and reconfiguring to support customer engagement prediction, product uptake orchestration, and repayment risk anticipation. Methods: The study employed Design Science Research and proceeded through iterative problem identification, artifact specification, architecture development, proof-of-concept demonstration, and performance evaluation. A multimodal data configuration combining transaction-like records, behavioral event sequences, complaint-text signals, and market context variables was used to test the artifact under realistic digital ecosystem conditions. Findings: The findings showed that the artifact produced an integrated decision architecture with stronger predictive, strategic, and governance capabilities than conventional isolated models and rule-based approaches. Conclusion:In conclusion, the artifact provides transferable design knowledge for digital financial strategy. Policy recommendations include strengthening data governance, explainability, fairness auditing, and institutional readiness for responsible artificial intelligence deployment. Novelty/Originality of this article: Its novelty lies in translating dynamic capabilities into algorithmic routines embedded in a governable strategic system rather than a standalone predictive model.