INTEGRATION OF FINTECH INTO RISK MANAGEMENT OF PROJECT FINANCE

Abstract

Introduction. The accelerating proliferation of financial technologies is profoundly transforming the landscape of project financing, exposing the limitations of conventional risk governance frameworks in keeping pace with contemporary investment dynamics. This study investigates how FinTech instruments and data-driven analytical tools are redefining the identification and mitigation of risks throughout the entire project lifecycle. The analysis focuses on four categories of exposure: operational risks, budgetary risks, financial-sustainability risks of the project vehicle, and structural risks. The evidence gathered demonstrates that AI-enabled solutions, large-scale data analytics, and distributed-ledger technologies meaningfully enhance the precision of risk identification and the reliability of adverse-event forecasting.

The goal The study seeks to establish a coherent theoretical-methodological and applied foundation for FinTech-enabled risk governance in project finance, culminating in a structured integration algorithm suitable for deployment within the risk management frameworks of innovation-driven investment initiatives.

Materials and methods. The study employs a systems-structural approach for the classification of project financing risks; comparative analysis for the synthesis of existing risk governance frameworks; inductive and synthetic reasoning for the development of the authors’ FinTech integration model; and content analysis of peer-reviewed scholarly publications alongside the PMBOK PMI and ISO 31000 standards to provide theoretical grounding for the propositions advanced.

Results. The study classifies project-financing exposures into four functional domains and maps the capabilities of NLP, Big Data platforms, BI dashboards, blockchain and RegTech solutions onto each phase of the risk-governance cycle. The authors propose a five-stage integration architecture comprising: digital risk discovery, quantitative-analytical assessment, smart contractual risk allocation, real-time continuous surveillance, and evidence-based adaptive response.

Prospects. Subsequent inquiry should prioritise the development of performance-measurement methodologies for integrated FinTech risk platforms, the refinement of machine-learning-based predictive models for adverse-event anticipation, and a thorough examination of the regulatory enablers and constraints governing FinTech adoption within the project-finance markets of transition economies.