Predictive Risk Intelligence (PRI)

The concept of Predictive Risk Intelligence (PRI) is to help organizations apply analytics to develop a forward-looking lens into its potential risks. The risk management lifecycle can be organized into three categories:

• Reactive risk monitoring is the ability to respond to a post-event with a remediation plan as well as prevention of similar events in the future.
• Integrated risk monitoring is incorporated throughout business processes and be able to timely report on risk based of identified criteria and thresholds.
• Predictive risk monitoring applies analytics to current and historical information to identify potential or emerging risks.

Predictive Risk Intelligence Process
PRI can help turn risk, controls, and performance information into preventative and actionable insights, preparing organizations for a refined understanding of emerging risks. The PRI process is explained below:
1. Define PRi scope Management and risk governance teams identify prioritized risk events to better track and monitor on a continual basis.
2. Identify precursors of risk events Each risk identified within scope is analyzed to identify indicators or incidents that precede risk events and provide reliable indication of an event occurrence. For example, product quality failures may result from an internal process failure or a supplier failure.
3. Identify data sources Each risk event precursor is prioritized and mapped to internal and external data sources which can supply the baseline data required for analysis and predictive modeling—see Figure 2 for example data sources.
4. Develop static and self-learning predictive algorithms Through combined analysis of internal and external precursor information, a predictive analytics algorithm (a data-driven statistical model) is selected for fit and applied to predict or detect the heightened occurrence and likelihood of a risk event. Data mining and machine learning capabilities allow these models to be carefully maintained and/or evolve with ongoing improvements to accuracy.
5. Initiate PRI generation Risk governance functions start collecting the baseline data for each risk category and apply risk predictive algorithms to generate emerging risk alerts and notifications. Results are reported and continuously evaluated against actual results to determine the success rate of the models and enhance the accuracy of insights and outcomes. Formal reports are generated to describe the emerging risk environment for C-suite and board decisioning.