Risk analytics: an executive’s guide

Risk analytics: an executive’s guide

July 28, 2020

Kate Prohorchik

Data Intelligence Researcher

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.

What is risk analytics, and what risks can it analyze?

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.

Risk is industry-agnostic, as is the solution

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:

  • Fraud risk
  • Market risk
  • Credit risk
  • Transportation and logistics risk
  • IT risk
  • Financial risk
  • Investment risk
  • Supply chain risk

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.

How machine learning is shaking up the risk analytics game

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 machine learning-based fraud detection but also in any risk-management scenario where this branch of AI 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.

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Business benefits of data-driven risk analytics

The immediate and longer-term advantages of combining historical risk-related data with predictive analytics software based on machine learning algorithms and scenario modeling include the following:

  • Near real-time visibility of anomalies and risk-predictors to support fast responses to potential hazards.
  • Fast identification of high-risk customers, suppliers, and partners.
  • Increased accuracy in risk quantification and prioritization.
  • Prevention of repetitive losses and liabilities.
  • Reduction in insurance premiums across enterprise functions.
  • Improved risk mitigation strategies powered by accurate insights and predictions.
  • Greater business resilience to uncontrollable events, both natural and those of human origin.

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.


An example of success with risk analytics

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.

How Biogen outpaced disaster

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:

  • Establishing that the first hurricane, Harvey, would have little or no impact on the supply chain, enabling the company to avoid wasting time on presumptive planning and actions.
  • Executing early shipping of inventory from Florida-based suppliers, which analytics highlighted as being at high risk from the second hurricane, Irma. By moving the inventory to its facility in Kentucky, Biogen avoided supply shortages that would otherwise have arisen. As the analysis and modeling had indicated, the suppliers’ plants were indeed struck by Hurricane Irma.
  • Diverting products and activities in Central Mexico to alternative locations unaffected by the earthquake. Redirecting the manufacture and supply of custom products, with large advance orders from suppliers not in the path of the third hurricane, Maria, which on arrival crippled two of its Puerto-Rican suppliers.
  • Creating cross-functional playbooks using learnings from the events of late-summer 2017 for reference during future natural disasters.

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.

What to look for in risk analytics solutions

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:

  • Real-time detection and reporting of risks
  • Automated alerts that can be routed to relevant stakeholders
  • Comprehensive dashboards with intuitive visualizations
  • The ability to display risks by severity to enable fast and accurate prioritization
  • Risk quantification/valuation
  • Internal audit and self-assessment tools
  • Prescriptive analytics engines to suggest risk mitigation actions
  • Flexible reporting capabilities
  • Real-time tracking of risk mitigation
  • Mobile BI apps or responsive web apps enabling access from all types of devices
  • Support for code-free collaboration between IT and business users

The shrinking architecture and growing value of risk analytics

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.

A market poised for growth

The growing ease with which organizations can benefit from digital risk analytics might be a key driver of spectacular market growth.

Risk analytics market, MarketAndMarkets

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.

How to get ready for risk analytics in your enterprise

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.

Prerequisite #1: a risk management model

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.

Prerequisite #2: the right culture and mindset

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.

Risk analytics team roles

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.

Prerequisite #3: data quality

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:

  1. Extract relevant data from your source systems.
  2. Use rules and logic to obtain an overall picture of data cleanliness and quality.
  3. Repair inaccurate or corrupted data and remove data that cannot be fixed.
  4. Ensure that you retain just one instance of your cleaned data.
  5. Standardize your data against a set of predefined rules to ensure quality.

After completing the process outlined above, it will make sense to deploy a data monitoring and cleaning application to ensure quality is continually maintained.

Hope for the best and analyze the rest

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.

Take a look at your options

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.