Systematic framework for software risk management in banking IT project planning

Posted: Aug 23, 2009

• Abigail Hernandez
• Abigail Rodriguez
• Aiden Brown

Abstract

The digital transformation of banking institutions has accelerated at an unprecedented pace, driven by evolving customer expectations, regulatory requirements, and competitive pressures. Banking IT projects represent some of the most complex and high-stakes software implementations, where failures can result in significant financial losses, regulatory penalties, and erosion of customer trust. Traditional risk management frameworks, while valuable in many contexts, often fall short in addressing the unique characteristics of banking software projects. These frameworks typically treat risks as discrete, independent events and fail to capture the complex interdependencies and emergent behaviors that characterize modern financial technology ecosystems. This research addresses the critical gap in existing risk management methodologies by developing a systematic framework specifically tailored to the banking IT project environment. Our approach represents a fundamental departure from conventional practices by integrating principles from complex systems theory, behavioral economics, and quantum-inspired uncertainty modeling. The framework acknowledges that banking software risks are not merely technical challenges but rather manifestations of complex socio-technical systems where human behavior, regulatory constraints, and technological capabilities interact in unpredictable ways. We propose that effective risk management in banking IT projects requires a paradigm shift from viewing risks as problems to be solved to understanding them as inherent characteristics of complex adaptive systems. This perspective enables project managers to move beyond reactive risk mitigation and toward proactive risk intelligence, where potential challenges are anticipated and addressed before they manifest as project failures. The framework incorporates novel risk classification dimensions that capture the temporal evolution of risks, their systemic interconnectedness, and their potential for cascading effects across the project ecosystem. Our research demonstrates that this integrated approach significantly enhances risk identification accuracy, improves risk prioritization effectiveness, and enables more resilient project planning.