Risk Management in IT Projects for Digital Banking: A Case Study of an Indonesian State-Owned Bank - Wibowo, Aji Prastio; Raharjo, Teguh; Trisnawaty, Ni Wayan; Muhamad, Gilang Aulia; Faridy, Azka (Applied Information System and Management (AISM), 2025)
The increasing use of information technology in the banking industry has made it more difficult to manage risks in the digital projects of state-owned banks. This study aims to examine the risk management processes of a state-owned mortgage bank in Indonesia and how it manages the information technology risks in the digital banking project lifecycle. This qualitative research is based on content analysis of forty-three risk assessment documents, with thematic coding using ATLAS.ti. This research was further enriched through expert interviews and a quantitative survey conducted among 38 project stakeholders. Risks are defined in a hierarchical classification and mapped to project phases using the PMBOK. Identifying operational, compliance, and third-party risks is most pertinent in the execution and post-implementation phases. Additionally, there are pressing concerns, such as the potential for cyber threats, non-compliance with applicable laws and regulatory frameworks, integration issues, over-reliance on service vendors, and systemic dependence on external vendors. In this case, the study integrates PMBOK, ISO 31000:2018, and the insights of seasoned practitioners to create a singular holistic mitigation strategy. It comprises a risk prioritization matrix, phased actionable treatment plans for each defined stage, and robust governance and responsiveness enhancement mechanisms for high-risk reactive IT environments. The guidance is triangulated with sector-specific intelligence, thereby underscoring proactive risk governance through communication, vendor due diligence, dynamic control, and real-time accountability across boundaries scaffolding. Further single-initiative case studies, multi-institutional case studies, evolving longitudinal risk studies, and the application of AI and blockchain for predictive and autonomous risk steering in digital finance could enhance and refine this work.