Robust risk reporting is driven by a well-designed set of risk metrics that indicate to management when risk levels are changing and when management should take action. Escalation to management as risks increase should be an integral part of reporting, and data systems should support ad hoc root cause analyses to support management decisions in mitigating the level of risk.
The foundation behind this is a reliable and complete data ecosystem. Reliability is ensured by strong data governance that includes automated accuracy and integrity testing and error reporting to ensure that issues are identified and addressed quickly. A complete data ecosystem would be characterized by the risk attributes and outcomes that can be used to develop a robust set of metrics, including early warning indicators, key performance and key risk indicators.
Early warning indicators of increased risk include increases in loan delinquencies, fraud events or losses, or technology system outages that can alert management to potential problems so that they can be corrected before they become material.
Key performance indicators provide a mechanism for evaluating acceptable levels of performance including revenue, loss prevention, or system performance that can be used to support strategic plans that require accurate revenue and expense estimates.
Key risk indicators ensure that losses remain within risk appetite/ tolerance, where most organizations balance a risk-reward trade-off, and examples include the tolerance for credit, fraud, or operational losses to ensure that losses do not reduce net revenue below expectations.
This reporting should be implemented across risk types and business areas. The risk and business areas that drive strategy and profitability in financial institutions receive the greatest focus and investment, such as reporting on profitability, credit risk, market risk, interest rate risk and other financial reporting. With other risks increasing, such as consumer compliance, money laundering, fraud, and cyber risks, the data systems, monitoring metrics and reporting are also being built out at many organizations.
A complete data ecosystem should capture the data for robust reporting within each business area by risk type, in addition to having the necessary risk attributes and outcomes. For example, within a digital bank platform data would be gathered on credit performance of loans, deposit volumes and retention rates, funding sources and costs, interest rate and fee income, operating costs, customer complaints, anti-money laundering and fraud alerts, platform performance and outages, site clicks and inquiries for marketing, etc. Such reporting provides a comprehensive view of the revenue generation, costs, risks and opportunities that can support strategic decision making by management is presented in a comprehensive business profile.
For innovative and responsible decision making this comprehensive reporting is necessary to respond quickly to new opportunities in designing product innovation, retaining customer loyalty, expanding the customer base ordeveloping new business models. Additionally, it supports rapid response to and remediation of emerging risks that can come from multiple sources, including credit losses, deposit/funding runoff, fraud losses and/or cyber-attacks. It is paramount that management have complete information on both opportunities and threats/risks for strategic decision making. However, many organizations have systems that are not integrated and do not have the necessary data attributes. An investment in these necessary data systems and data attributes will provide competitive advantage and determine who is able to survive and grow their business.