In our last blog in this series we looked at the important role data plays in a modern Integrated Risk Management (IRM) framework. While data is indeed the foundation for IRM, tools and innovation are indispensable elements that help to organize processes, facilitate the ability to share and analyze data, and create a bespoke customer experience that is critical for successful IRM programs.

For example, within the tools and innovation category, workflow accelerators and process mining tools leverage artificial intelligence (AI) and machine learning ML which can help to decrease costs and provide meaningful, real-time insights. Other tools apply innovative technologies such as natural language processing (NLP), natural language generation (NLG), data analytics and visualization, and mobile capabilities. These tools are used to implement a variety of useful capabilities for IRM:

  • Early warnings and predictive analytics. Exception-based risk management, which is focused on putting concentrated resources towards identifying and mitigating the largest, most challenging risks, is not a new concept, but has been difficult to implement without the use of AI and ML. The use of these technologies can empower analytics, dashboards, and reporting on key indicators that give earlier and better focused insights into when a risk event or operational incident may occur.
  • Continuous control monitoring. User interfaces, reports and dashboards that are augmented with virtual reality, AI and ML capabilities can provide control functions with clear visibility of an organization’s risk and control profile in real-time and help identify trends, risk concentrations and control effectiveness issues.
  • Customizable dashboards and reporting. Similar to what we described for early warning and predictive analytics, modern IRM capabilities often include the ability to customize look and feel, along with the data, information, and metrics in dashboards and reports so that they are most meaningful and useful to their users. Sometimes this is achieved through a single tool, but more often than not it is realized by integrating multiple tools (e.g. GRC tool + interactive data visualization tool).
  • Intelligent workflows and customer decisioning. AI and ML technology applied to workflow capabilities and business rules within an IRM tool can add intelligence and automate processes to increase consistency of decisioning and actions. Examples of this include automatic creation of issues, providing recommendations on classification of issues, risks, and controls, and suggested actions based on data that represents similar prior events.
  • Self-service data and insights. Organizations that are implementing modern IRM are making tools and technology increasingly available to frontline risk managers for on-demand access to data and information, including self-service reporting and analytics tools that assist with analysis, decision making and other activities. In doing so, organizations are empowering their risk management teams to take more responsibility and ownership for managing the firm’s risk while still providing an enjoyable experience that makes them want to do their job better.

These are just a few examples of how AI, ML, and other innovative technologies are being used by organizations to implement IRM and create consistent, transparent, and proactive risk management functions. The broader adoption of these tools can enhance the end-user IRM experience and increase the firm’s risk posture.

In the next blog in this series, we will examine the role of streamlined, integrable processes in the creation of modern Integrated Risk Management.

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