In many business management areas, the current COVID-19 crisis is accelerating forays into digitization, and risk management is no exception. Yet, the pandemic, despite its enormity, should not be the primary reason to get savvy about risk analysis. Business risk management encompasses so many more facets and angles, each of which can improve or suppress performance to some degree.
Indeed, the myriad of risk strands faced by enterprises should itself be the most compelling reason to adopt a data analytics strategy. After all, there are just too many possible issues for manual assessment and analysis methods to cover in full.
So what should today’s executive take into consideration when contemplating the use of digital risk analytics tools? What benefits can this particular business intelligence technology offer to pay for its adoption and maintenance? Those are precisely the questions you'll find answered in this brief guide to the latest risk analytics developments.
Risk analytics is a form of business intelligence that serves as a component in a risk management environment. It does not have to be a digital solution, and indeed, businesses have been analyzing and assessing risk for years using manual or semi-automated methods.
Nevertheless, in this guide, we’ll focus on digital risk analytics, which is growing in popularity as a niche of BI development due to increased interest among risk-management professionals.
Digital risk analytics as a discipline has changed—and vastly improved—the way risk managers evaluate potential scenarios and predict risk-laden events. It minimizes the need for reliance on human intuition, allows enterprise-wide assessment of risk exposure, and enables management precision, which would have been unimaginable not so long ago.
Invaluable in any industry, risk analytics exploits internal and external structured and unstructured data to model scenarios and outcomes, providing insights into areas such as:
The above examples are but a few of the elements that companies and organizations can evaluate using risk analytics. Capturing, storing, and extracting data relating to all the risk strands in a particular business environment enables risk managers to amass targeted intelligence, visualize scenarios, and prepare for them. The resulting insights provide an organization with a plethora of benefits to security, operational continuity, and competitive advantage.
Machine learning technology is probably the biggest game-changer in digital risk analytics, primarily due to its ability to reduce the margins of error in predicting risk likelihoods and severities.
No longer do analysts need to set exclusively structured data against fixed rules, a form of catchall measure that can sometimes deliver inaccurate conclusions. For example, in conventional fraud risk analysis, a company might use rules to flag transactions exceeding a specific monetary value. In this case, close manual scrutiny of each flagged transaction is necessary to avoid false positives.
When a cognitive solution, powered by the latest in artificial intelligence, creates a risk alert, human verification requirements still exist. However, as risk specialists increasingly weed out false positives and update the algorithms, the application learns from the inputs and becomes more accurate in evaluating the risk of fraud. This has become one of the most potent applications of machine learning in banking, for example.
The same benefits exist not only in fraud detection but also in any risk-management scenario where machine learning is applied.
Additional accuracy comes with AI technology's ability to process unstructured data using natural language processing, text analysis, and image recognition. That makes the need for rule-based analysis far less prominent and enables near real-time risk identification—and ultimately, faster responses.
Of late, risk analytics applications are beginning to cross the boundaries of prediction into more actionable realms of prescriptive analysis. The most sophisticated solutions help risk managers to identify the best course of action to prevent, circumvent, or at least mitigate potential harm arising from disruptive events and criminal activity.
The immediate and longer-term advantages of combining historical risk-related data with predictive analytics based on machine learning algorithms and scenario modeling include the following:
As an organization builds its experience with risk analytics and the above benefits are realized, they combine to support improvements in capital and operating costs, efficiency, service, and profitability. Gains can also be achieved in other crucial elements of business performance, such as reputation, brand awareness, and trust among stakeholders and customers.
Recognition of these advantages is spreading. While a mere 6% of organizations currently claim to be using advanced risk analytics extensively, more are expected to follow. Indeed, experts predict healthy growth in the market over the next few years, as illustrated by figures shown later in this article.
During the summer of 2017, the Southern United States and parts of Central America were hit by no less than four catastrophic natural events. Three hurricanes in close succession, and also an earthquake, caused massive disruption to companies and their supply chains across the affected regions.
Hurricanes Harvey, Irma, and Maria pounded the US Gulf Coast and the Caribbean, while a powerful earthquake struck Central Mexico. These were regions in which pharmaceuticals company Biogen had suppliers of raw materials and vital components for its products, many of which were the only options for treating neurological disorders.
Fortunately, Biogen had recently entered an initiative in partnership with a supply chain resilience and risk analytics platform. That collaboration quickly proved its worth during the unusual convergence of natural catastrophic events, which hit its competitors’ supply chains hard.
Access to the platform’s risk monitoring and analysis capabilities enabled Biogen to successfully avoid significant disruption by providing intelligence leading to the following proactive mitigation measures:
These achievements were realized by extrapolating and analyzing a large quantity of risk-related data sourced internally and from external agencies such as weather-monitoring centers.
What-if scenario modeling provided Biogen with pictures of the most likely areas of impact and probable levels of disruption. As a result, the company could redirect supplies and execute alternative arrangements to protect its supply chain from disruption.
For outcomes like those in the Biogen case study to be realistic, your organization will need to combine data science expertise with advanced analytics, utilizing machine learning and cross-platform data collection.
With an ever-growing array of solutions entering the market, from generic risk analytics to industry-specific applications, it’s not necessarily easy to know what constitutes a suitable product. An in-depth appraisal of tools is beyond the scope of this article, but as a general guide, the best solutions today should offer the following features:
No business or organization is immune to risk, and even the smallest entities face a range of threats that would challenge any manager to keep on top of them all. However, until recently, only the largest corporations could afford the luxury of digital analytics solutions.
All that is changing now, though, with modular platforms becoming available, allowing medium-sized enterprises to start with a limited setup and gradually scale up to a comprehensive business-wide program.
Even small businesses can take advantage of the latest risk analytics capabilities, as more vendors launch cloud-based solutions. It’s no longer necessary to invest in complex hardware architecture, because available SaaS platforms are affordable and need next to no infrastructure.
The growing ease with which organizations can benefit from digital risk analytics might be a key driver of spectacular market growth.
Of course, the emergence of the COVID-19 pandemic has only helped raise this particular technology niche's profile. Organizations everywhere have turned their attention to the risks presented by the crisis, such as threats to healthcare data security, and the probability of similar catastrophes in the future. If such events should transpire, they want to meet them with far more readiness than was the case with the novel coronavirus.
Like any significant IT deployment, several prerequisites must be fulfilled before integrating risk analytics into your technology environment. To close out this brief executive’s guide, let’s highlight some of these and look at a few critical steps to take when preparing for implementation.
The need to have a risk management model in place might seem like a no-brainer to larger organizations, but it bears emphasizing as a reminder to less risk-savvy businesses. No analytics solution will help you if you don’t have a risk management structure, process, and protocols established.
After all, digital hardware and software is only one element of a complete system. Even the best prescriptive software needs programming with the various response options that are practical for your company to exercise. Similarly, without the appropriate resources and protocols to act on digitally prescribed remedies for risk, your software solution will add little to your risk management strategy.
Before you think too deeply about deploying your application, it will pay to think about your people. More specifically, you should consider if your risk management team is ready for a brand new approach using data as a primary decision driver.
Many organizations have worked for years using manual or relatively simple digital tools to identify, assess, and analyze risk. Risk managers might not adapt readily to the more scientific thought processes and practices necessary for working with modern risk analytics. To counter any issues with adoption, extensive change management and training initiatives might be required to prepare your team to use new tools.
Although not unique to risk analysis, the perennial issue of quality requires significant attention before attempting to exploit data as a risk-reduction aid.
If your business is already focused on keeping data clean for use in existing systems, preparing it for risk analytics may not be so onerous. On the other hand, if data cleaning has not been a part of your data-management cycle, it should be a primary task in your project’s preparation phase.
Typical steps in data preparation, which you should not neglect to take before deploying risk analytics, are as follows:
After completing the process outlined above, it will make sense to deploy a data monitoring and cleaning application to ensure quality is continually maintained.
In a perfect world, enterprises and organizations would never need to add risk analysis to an already vast range of activities that divert time, money, and attention from core competencies.
While that might be a stretch too far toward wishful thinking, digital analytics can add effectiveness to risk management efforts and make them less burdensome on your organization’s resources.
Perhaps you already have an established risk management strategy in place and have been considering how to improve it with technology. Maybe COVID-19 has shocked you into raising the priority of risk and resilience. In any case, the tools to help you are becoming more sophisticated and less costly.
It’s not so difficult to make a start with a ready-built analytics solution from a vendor, especially one that serves your team from the cloud. Alternatively, you might opt for the more expensive but potentially more effective custom software development.
Whatever your preferences, now might be a good time to graduate to digital risk analytics. At least it’s worth looking at the possibilities with an eye to the future. When doing so, try asking not what you’ll gain now from AI-powered risk analytics, but—in the wake of the coronavirus crisis—what future problems such a move might prevent.