The use of cognitive risk sensing for the early detection of emerging risks
- Published: Thursday, 05 December 2019 10:00
Of more than 1,590 C-suite and other executives polled by Deloitte, 39.4 percent report that the greatest benefit of using cognitive risk sensing is the early detection of emerging risks and potential threats. However, just 5.3 percent of respondents said that their organization uses enterprise-wide cognitive risk sensing to manage such risks.
The poll was conducted during a Deloitte Dbriefs webcast titled ‘Cognitive risk sensing: Enabling internal audit to anticipate risks’.
According to Deloitte cognitive risk sensing employs human insights with advanced analytics and machine learning to aggregate, analyze and synthesize the world's available data to interpret signals — both threats and opportunities — more effectively. The intelligence generated can reveal emerging issues and disruptions to an organization's brand, products, services, and third-party ecosystem, as well as anticipate changes in the external environment. Equally significant, cognitive risk sensing provides an ability to ingest internal company data to receive an integrated and forward-looking perspective that can help improve risk prioritization.
To implement a true cognitive risk sensing capability across the enterprise, Deloitte says that organizations should seek to address the following key considerations:
- Cast a wide net: rather than monitoring a limited-set of well-known risks, your risk sensing analytics should look at a broad view of critical risks, both unknown and known, and leverage copious amounts of data to detect issues early on and drive business growth.
- Build context and maintain situational awareness: before dismissing outliers as insignificant, consider each new event or piece of information as providing an opportunity to refine the organizational vision and recalibrate the context. If an occurrence is strategically relevant, its rarity does not in itself diminish its potential significance and impact on the organization. Linking anomaly detection to the organization's strategy and business context keeps it rooted in risk management rather than reducing it to forecasting for its own sake.
- Take an outside-in approach: an external, integrated, view can provide greater context to internal data and analysis and thereby help in evaluating assumptions and potentially erroneous data and conclusions. Additionally, external data points can be presented to management, facilitate internal discussions, and be used to test scenarios designed to gauge likelihood of outcomes and their potential impacts.