In business, the goal of risk management is to accurately identify and then perhaps even control the various threats that might endanger the company’s profits, capital, and even its very existence. The list of possible threats to a business is quite long, as these include competition, changing consumer demands, economical uncertainties, potential legal liabilities, the emergence of new technologies, strategic management mistakes, and even accidents and natural disasters. 

The task is in no way easy, and the recent Covid disaster is a prime example of this point. Such disasters can cause widescale disruptions, especially when such factors as political tensions and the climate crisis are taken into consideration as well. 

Future of Risk Management: Out with the Old, In with the New

Businesses need to be more prescient when it comes to identifying and mitigating risks, and that is best done scientifically, using modern tools such as automation, artificial intelligence, and data science. 

The Covid era proved once and for all that the old ways of risk management will no longer suffice. One case in point was the overreliance on point-in-time assessments for monitoring risk. These assessments were usually done only once a year, or perhaps after a huge risk event. 

But in today’s world where risks evolve quickly, that kind of assessment can no longer suffice. The data collected in those annual assessments tend to become useless too quickly, and this results in notable blind spots. To correct that problem, it has to be replaced with constant monitoring and continuous risk data gathering. 

Plenty of companies did recognize this issue, but the implementation of continuous monitoring just resulted in increasing the workload for the already-beleaguered risk management team. In other words, their work just got more difficult.

Ultimately, this is the challenge. How does a company manage the huge volume of continuous risk data, without spending too much, using up too many resources, and taking too much time? And how does a company use this data to keep the business resilient and avoid disruption? 

Automation is the answer to all these questions. 

How Automation Helps with Risk Management 

How can companies use automation along with big data to boost their risk management efficiency? It starts with enabling companies to monitor the risks efficiently and cost-effectively. And in an increasing number of companies, the journey toward risk management automation is already underway. 

Today, more companies are already using RPA (robotic process automation) for data collection. They’re using AI (artificial intelligence) and machine learning tools to perform sophisticated tasks such as performing sentiment analysis to conclude whether the current data is good or bad news for the company. These tools can also do impact analysis to discover the potential impact on the organization. The data may signify that there’s a low or medium risk over the horizon, or it might conclude that the risk requires the immediate attention of the powers that be. 

These technologies can not just statistical models, but also continuously improve the accuracy of detection, confirmation, and prediction. This lets the company truly see the real scope of the risk exposure, and react properly to the threat. 

3 Major Components of Automated Risk Management 

At the very least, automation replaces valuable human resources from the tedious task of gathering and validating just a small portion of the data that AI can handle. But for a truly fully-functional risk management system, you need more. 

Your automated risk management program should have the following components so that it will work effectively: 

The Monitor

This works like a listening post, though it’s much more sophisticated than that. This component uses automation and AI to collect and validate the risk data. Afterward, it can also perform sentiment analysis and impact prediction. It can also flag and automatically forward the risk events that meet or exceed certain set criteria to the risk intelligence system. 

The Risk Intelligence System

This is a company-wide workflow tool that factors in the particular risk tolerances and thresholds of the company, to determine the correct risk mitigation reactions for each risk event sent by the listening post. Some of these reactions can be automated and can be handled by the risk intelligence system itself. 

But some reactions may need human participation, and these will be assigned to the risk response center. 

The Risk Response Center 

This is the hybrid human and system team that deals with the most crucial risk mitigation tasks. Due to the use of automation before even reaching this step, the humans involved won’t have to use up their precious time on more mundane tasks that automation can already handle. Humans, along with their automation tools, can then use their innate skills in a way that’s more focused, more effective, and more efficient. 

The Bottom Line

Before a company can even engage in risk management automation, the powers that be in that company has sit down and ready their decisions regarding risk tolerances, thresholds, and appetites. Humans still have to decide what makes constitutes a risk for their company, and how they should react to those risks. Only then can automation work—in the end, humans still make the rules and guidelines.