Embrace Disruption Through Comprehensive Risk Assessment
When is risk not involved in today’s global macroeconomic setting? It’s inherent in much of what today’s decision-makers are challenged to accomplish. As organizations continue to develop their digital ecosystems with complexity and speed, the data powering those systems becomes transformative and disruptive -- ultimately changing the way we understand, value, and react to doing business. To me, that makes risk even riskier.
However, if you’re anything like me, you believe that with risk comes opportunity. As finance leaders’ tools evolve, there’s a huge opportunity to build internal cultures around innovation and deliver value through technology, including artificial intelligence, blockchain, and machine learning. The business world is hungry for a new breed of finance leader to help navigate these new complexities. Indeed, the leaders that embrace risk will be the differentiators and thus more likely to succeed. Using modern technology and select tools can help leaders predict and monitor financial risk in the customer base. Which is something you can capitalize upon, while your competition may struggle to navigate.
What is a Risk Assessment? The Best Strategy is to Have One.
Ok, let’s talk about risk strategy, or as I also refer to it, the big “what if?” What if your biggest supplier turns out to be a bad player? What if your biggest customer suddenly walks? What if the government of the country where your manufacturing center is based is overthrown?
If you haven’t played the “what if” game, chances are you probably don’t have a comprehensive risk management framework in place for your business. In 2018, to better understand how finance leaders prepare for disruption, Dun & Bradstreet conducted a risk management study. Not surprisingly, finance teams said managing risk is a struggle and most believe their actions pose an increased level of business risk.
Despite that, the key to embracing potential disruption is to develop a comprehensive risk framework. While risk is unpredictable, a detailed and comprehensive risk assessment can offer peace of mind and bolster confidence in employees, suppliers, partners, and customers.
There are several components to consider when creating a successful risk strategy. First, appoint an owner to oversee the plan and ensure that employees understand their role in managing risk. Next, document all critical business processes and identify potential risk such as natural disasters, financial difficulties, and leading competitors. Ideally, prioritize risks based on the likelihood of occurrence and its impact on your business. Finally, review your plan regularly to identify new risks and monitor the plan’s effectiveness. With a strategy in place, should a supplier turn out to be a bad player or your biggest customer cancels its contract, you’ll be ready to help your business recover quickly.
Preparing for Risk with Predictive Analytics
Organizations can get ahead of business risks through the use of machine learning to engage in predictive analytics, avoiding the downsides associated with risk and manipulating its disruption. In one example, banks are realizing the value that machine learning and predictive risk analytics can deliver. As McKinsey notes in the article “Risk analytics enters its prime,” “Risk-analytics leaders are creating analytic algorithms to support rapid and more accurate decision-making to power risk transformations throughout the bank. The results have been impressive. An improvement in the Gini coefficient [measure of variation] of one percentage point in a default prediction model can save a typical bank $10 million annually for every $1 billion in underwritten loans.”
While the practice of predictive analytics isn’t new, many organizations still aren’t sure how to use it. Moreover, sometimes there’s a misconception. Remember, predictive analytics reveals if something is likely to happen, not that something is going to happen.
Predictive risk analytics allow for a wide variety of techniques, including data mining, statistics, modeling, machine learning, and artificial intelligence, to analyze current data and make predictions about the likelihood of unknown future occurrences. It allows us to use the past and present to inform the future. Using that data can help inform how an organization responds to variables and ultimately help determine the probability of a business’s growth or decline, as well as assess change in a company’s risk profile. Thanks to machine learning algorithms, we can better and more rapidly predict what is more likely to happen and prepare ourselves in the most cost-effective manner.
Using risk analytics helps organizations evolve and become more agile, leading to faster execution and lasting organizational change. First, focus on a small, select number of critical metrics that affect the business. Second, adopt analytics technologies and expertise across the organization. Next, focus on adapting decision-making processes. Fourth and last is activation. Activating predictive analytic engagement allows the organization to learn and adapt.
Building Data-Connected Enterprises to Combat Business Risk
Developing a business risk strategy that leverages predictive analytics can help companies when monitoring and predicting customer risk. Equally important is accurately connecting the organization to the data within it to reduce costly missteps from instances of subpar risk management. Our study found that nearly half of finance leaders collect and manage data in silos, and only 20 percent report having the ability to share data in an integrated fashion to manage enterprise risk.
Of course, there’s more to collecting and aggregating accurate and clean-sourced data. Finance leaders must also manage, distill, and share data correctly to eliminate risk and reduce inefficiencies, evolve needs, and drive growth within the customer base. Organizations can be caught off-guard due to events and trends beyond predictability, leading to costly missteps. That’s why businesses seek to eliminate potential risk chaos through a standardized connected-data structure. With a connected-data structure, it is easier to access critical information and empower employees to make the smartest decisions necessary to avoid risk.