Risk is the one thing that everyone tries to avoid in life, or at least try to reduce its possibility. No matter the industry, the risks are important factors that must be dealt with complete sincerity and attention.

As businesses adapt to the pandemic and shift to new norms, risk mitigation strategies have become as usual and ubiquitous as having a fire escape in the office. Smarter, AI-driven learning and development initiatives will help mitigate risk in our rapidly evolving world.

According to Statista, risk management experts state that the 2021 pandemic outbreak was a more significant business risk to small companies than it was to mid-sized or larger companies.

Businesses must manage risks because they can frequently prevent or minimize their financial, political, social, and cultural ramifications. In addition, risk management is an effective tool to tackle uncertainties in the probability of an event’s occurrence that challenges decision-making.

Despite the difference in overall risk, all businesses and people suffered under the pandemic; how different-sized companies were impacted varied: Business interruption, including supply chain disruption, was the greatest threat to companies with annual revenues of over £180 million. Meanwhile, the pandemic outbreak was the biggest threat to companies that generated revenues below £180 million.

Infusing AI to minimize risks

When organizations infuse AI and data analytics into their risk management solutions, they unlock many advantages. These include centralized reports, faster access to data, and a direct connection to their database.

According to Anna, Learning Pool’s recent partnership with Sisense was instrumental in helping add ‘Insights’ to its portfolio. This partnership enables Learning Pool to create AI-powered, expertly-designed dashboards explicitly built for learning data analysis.

Risks management with data analytics

All human endeavors involve uncertainty and risk. For example, in the food production area, science has made great strides in genetic management. But there are concerns about some of the manipulations involved, with different views prevailing across the globe.

In the United States, genetic management is generally viewed as a way to obtain better and more abundant food sources more reliably. Nonetheless, there are strong objections to bioengineered food in Europe and Asia.

Risk-based decision-making

Employing computational intelligence for decision-making based on risk in information systems as supporting systems of decision-making has been studied since 1970. Some studies have taken advantage of business intelligence to provide another application for analyzing the loan risk in the financial modeling of the pulp and paper industry.

Many researchers have also addressed specifically the value of assessment and risk in IT investment. It is done by taking the resource-based view of the company and the perspective of the feasible option. They found that IT investments and their timing influence organizational downside risk.

Final thoughts

To manage financial risk management, numerous companies are struggling to become data-driven businesses. Business intelligence models have been applied in risk management contexts worldwide. They have proven effective for over half a century. We hope that this special issue provides a glimpse of how business intelligence can be applied by more readers faced with enterprise risk.