Best Practices to Present Key Risk Indicators for Banking: A case study at Bank of Portugal

Abstract

Businesses face numerous operational risks, which can be mitigated through a well-crafted action plan that measures and manages these risks. Key Risk Indicators analysis, a vital tool for devising both reactive and preventive strategies, is widely used across industries due to varying risk tolerances and response capabilities. In today’s business world, the combination of this analysis with effective information sharing and the generation of clear, and concise, reports and dashboards, is essential. Significant research has been conducted in the field of business intelligence and some within the banking sector. However, a notable gap remains, particularly in the study of conduct supervision topics. This gap in the literature underscores the novelty and importance of the current study. The focus is on addressing the question of: How can Key risk indicators analysis be presented to assist top management as an assess and trigger tool? By addressing this question, the study intends to contribute to the existing body of knowledge and provide new insights to this underexplored area. The following study, derived from insights gained during an internship at the Market Conduct Supervision department of the Bank of Portugal, explores techniques and best practices for presenting a Key Risk Indicators (KRI) analysis, with a focus on creating a decision-making approach, using a storytelling methodology. To achieve the study’s objectives, an exploratory and empirical methodology, following the four phases of the Design Science Research Methodology, namely conceptualization, construction, production, and evaluation, was adopted. This project, aimed at constructing a comprehensive analysis and methodology for implementing KRIs via a custom real-time analytical tool that monitors and evaluates the risk conduct of Portuguese banking institutions, was developed to addresses specific market conduct supervision needs by retrieving and analysing data across all supervised areas, providing actionable insights to stakeholders, and aiding in decision-making processes.