AI-Driven Predictive Project Risk Management in Large-Scale Financial Software Programs

Abstract

The study examines the value of AI-based predictive project risk management to enhance the yield of large-scale financial software programmes, where complexity, regulatory pressure and intensity of data play to the detriment of success. Through an explanatory secondary mixed-methods process, the study combines the evidence provided by recent scholarly literature, industry reports, graphical performance data and an actual case study of Microsoft and Deloitte. The most important findings are summarised as follows: the AI-based predictive analytics are much more efficient in comparison with traditional risk management techniques. This allows identifying the risk earlier, detecting it more often, and monitoring the risk in real-time: financial, operational, and scheduling risks. Empirically, it has been shown that there are high gains in scheduling effectiveness (a maximum of 85%), resource use (a maximum of 80%), as well as fraud and financial risks detection accuracy (more than 25% improved). The findings of case studies also make it clear that AI applications minimise the delay of projects, cost overruns, and project failures by proactively mitigating and supporting decisions based on data. Nevertheless, the analysis also reveals such essential challenges as limitations in data quality, challenges in explaining the algorithms, the need to integrate with the legacy systems, and skills deficits. In general, the study finds that AI-driven predictive risk management is a scalable, adaptable and better variant of the traditional methods and has a great prospect of improving the success of big-scale financial software projects.