Machine learning and data-driven models for financial risk and liquidity management in large-scale projects: a systematic review
Zeinab Sazvar, Majid Damavandi, Masoud RabbaniPurpose
This paper systematically reviews the use of machine learning (ML) and data-driven models in financial risk and liquidity management within large-scale projects, targeting the multifaceted challenges arising from economic, contractual and contextual complexities. This study aims to explore how data-centric approaches can strengthen forecasting, control cash flow volatility and improve resilience across infrastructure, energy and public-sector projects.
Design/methodology/approach
Following the PRISMA protocol, 142 peer reviewed studies published between 2012 and 2025 were comprehensively screened and analyzed. The sample includes 40 % quantitative, 32 % mixed methods, 16 % literature review and 11 % qualitative designs, demonstrating a balanced methodological landscape. The studies were synthesized across three interrelated domains, i.e. (i) data, (ii) model and (iii) application to capture technical, analytical and contextual dimensions of financial risk management.
Findings
The reviewed literature reveals clear research concentration patterns, with ML-based models, particularly ensemble methods, neural networks and decision tree algorithms, emerging as the dominant analytical tools, especially in credit risk assessment and related financial decision contexts. Credit risk-related research (30%) dominates the literature, while project-level liquidity remains underexplored (≈ 4%), marking a significant conceptual gap. Persistent limitations include lack of accurate historical data (28%), model transparency issues (19%) and market-specific bias (18%). These constraints highlight the need to improve data quality, enable interpretability and adapt models across heterogeneous project contexts and economic systems.
Research limitations/implications
This review is limited by potential selection bias due to language restrictions and the exclusion of unpublished or gray literature. The heterogeneity in methodological quality and reporting among included studies complicates direct comparison and synthesis of findings. The reliance on secondary data and simulation models also underscores the need for more empirical, context-specific studies. Future research should aim to address these gaps, explore broader regional contexts and validate ML models under real-world project conditions.
Practical implications
The findings highlight the superior predictive power of ML models in managing financial risks and liquidity in large-scale projects, facilitating more informed and proactive decision-making. Practitioners can use hybrid and interpretable models to optimize risk assessment processes and adapt financial strategies to dynamic project environments. However, effective implementation requires addressing data quality issues, ensuring model adaptability to local conditions and enhancing stakeholder collaboration across project teams. Integrating advanced analytics into existing project finance frameworks can drive more resilient, data-driven risk management and improve overall project outcomes.
Social implications
Adopting advanced ML and data-driven risk management methods can enhance transparency and accountability in project finance, supporting socioeconomic development and stability. Improved risk mitigation reduces the likelihood of project failures, cost overruns and financial crises that impact broader communities. Better liquidity management enables timely delivery of essential infrastructure, public services and employment, contributing to sustainable urban growth and improved living standards. Bridging the digital divide and addressing ethical considerations in AI usage will ensure equitable benefits for diverse stakeholders and promote public trust in technological advancements.
Originality/value
By integrating findings within the three-domain (data–model–application) framework, this review offers a structured synthesis of ML’s impact on financial risk and liquidity management. It delineates empirical performance, contextual adaptability and methodological challenges providing actionable directions for bridging theoretical design and real-world decision systems. The study delivers a clear roadmap for researchers and policymakers to strengthen transparency, data governance and cross-disciplinary collaboration in project finance analytics. Rather than promoting specific algorithms, this review maps the structure, focus and blind spots of existing research, providing a consolidated evidence base for future methodological and application-oriented studies.