Incorporating Key Risk Indicators (KRIs) into the Risk Management Framework

Risk intelligence generation will be crucial for banks because it provides critical information about how economies are rebounding from the epidemic and which sectors are recovering more swiftly than others. Each industry has easy access to intelligence about the national economy.

Trade magazines, financial news sources, government agencies, and analysts place a premium on national indicators and their implications for the domestic and global economies. Similar intelligence is not readily available at the state level, as each state’s economy is subjected to a fraction of the examination and study that the national economy receives. By selecting the appropriate criteria, banks in any state can readily generate risk forecasts and insights.

The most critical KRI data must be made available to employees and decision-makers to ensure that bankers consider key risk indicators in their decisions. Banks can opt to track KRIs manually or using an automated method.

KRI Tracking Manually

Many banks manage KRIs manually, using spreadsheets to maintain and disseminate the most recent risk data; nevertheless, choosing a manual method has limits. The most obvious reason is that it is contingent upon the dedication and accountability of the workers charged with maintaining an updated inventory of critical risk indicators. A lapse in judgment can result in inaccurate data that cascades into the bank making an incorrect decision due to their risk management system inaccurately assessing the severity or probability of developing risks.

Visibility and intuitiveness are other vital factors to consider when using a manual technique. Creating a spreadsheet with risk measurements and emailing it to certain employees may result in the emails being overlooked or missing vital information. While these issues may appear minor on the surface, it is critical to realize that such oversights are precisely what modern risk management tools are designed to minimize. Implementing a risk mitigation approach that is also a risk has the potential to generate negative externalities.

Risk prediction systems provide KRI tracking tools and visual representation of risk metrics, which can be accessed by stakeholders in real-time. Click To Tweet

The primary disadvantage of manual KRI management in anticipating future risks and opportunities is that it precludes risk management automation. All analysis must be performed manually, including data collection from other sources such as leading indicators.

Technologies Devoted to Risk Prediction

Risk technology’s emergence has resulted in numerous advancements in risk automation, reporting, and accountability. Banks can acquire risk prediction technologies to forecast developing issues by tracking critical key risk indicators and cross-referencing them with other data sources. These tools enable risk executives to select the essential metrics and combine them with data from peers and other sources to obtain a complete picture of their risk profiles. The metrics can then be imported automatically from reliable sources or parsed directly from official reports and papers. The primary benefit of these technologies is their simplicity. Vendors can readily integrate them into banks’ risk management frameworks.

Risk prediction systems provide KRI tracking tools and visual representation of risk metrics. Instead of depending on emails or spreadsheets, bank stakeholders access real-time updates on selected risk indicators via a cloud-based tool on any device with an internet connection.

These tools can be integrated into the bank’s current risk management system. If a bank does not currently utilize risk management technology, the application can be used to directly offer evaluations and tracking of the most critical risk activities and issues. Additionally, banks can set their risk tolerance and restrict the ranges of risk measurements that are permitted on the platform. If a KRI violates the bank-defined constraints, the assigned stakeholders are automatically notified.

If a bank already has risk technology in place, the data from the tool can be directly incorporated. While integrating modern tools with historical solutions may require a specific integration solution, many current solutions provide built-in APIs and other data sharing techniques.

Platforms for Risk Management

While implementing a risk management platform with an integrated risk prediction module delivers the most in-depth insights and accurate risk forecasts, it demands a higher investment on the bank’s part. By implementing a platform, a bank gains all of the advantages of a risk prediction tool while also strengthening its risk prediction capabilities. Such a platform may combine risk forecasting easily with all other aspects of risk management. Additionally, banks can mix internal and external data to improve risk assessments and forecasting. KRI data can be shown alongside other risk data, which helps to normalize incorporating KRIs into decision-making across the business. Additionally, risk management platforms provide risk mapping, a critical tool for forecasting and assessing risks.