@article{4762,
  author = {Nguyen Thi Thanh Huong, Dinh Duy Linh},
  title = {Integrating Ontologies and Streaming Machine Learning for Dynamic FinTech Competitiveness Evaluation},
  journal = {Journal of Digital Information Management},
  year = {2026},
  volume = {24},
  number = {2},
  doi = {https://doi.org/10.6025/jdim/2026/24/2/85-96},
  url = {https://www.dline.info/fpaper/jdim/v24i2/jdimv24i2_2.pdf},
  abstract = {The rapid evolution of the Financial Technology (FinTech) sector has intensified market competition and
increased the need for enterprises to continuously evaluate and strengthen their competitive advantage.
Traditional competitiveness assessment methods often rely on static historical data and subjective managerial
judgment, limiting their effectiveness in highly dynamic digital environments. This study proposes a realtime
ontology-based machine learning framework designed to evaluate FinTech competitiveness with
enhanced accuracy, adaptability, semantic interpretability, and scalability. The proposed framework
integrates three interconnected layers: (1) a semantic knowledge representation layer that employs FinTechspecific
ontologies to model entities, relationships, KPIs, technologies, and regulatory concepts within the
FinTech ecosystem; (2) a real-time data ingestion and preprocessing layer that collects and standardizes
heterogeneous data streams from financial statements, digital transactions, customer interactions, APIs,
and social media platforms; and (3) a machine learning layer that combines supervised, unsupervised, and
streaming learning techniques to classify, predict, and monitor competitive positioning dynamically.
The framework is validated using KPI-driven datasets collected from Vietnamese FinTech enterprises between
2019 and 2024. Experimental results demonstrate that ontology integration improves semantic consistency,
reduces data noise, enhances feature relevance, and increases model interpretability. The hybrid framework
achieved high classification accuracy and effective real-time responsiveness, enabling stakeholders to identify
emerging competitive opportunities, operational risks, and market trends. This study presents a scalable
and intelligent competitiveness assessment framework that bridges semantic technologies and artificial
intelligence to support strategic decision-making in digital financial ecosystems.},
}