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ML for Commercial Property Insurers

For years, the preparation and management of data have exposed themselves as two costly and critical challenges for commercial property insurers. These challenges are hampering production and efficiency and inhibiting growth and profitability. The flow of submissions and the preparation of statement of values are laborious and time-consuming to agents, brokers, insurers and anyone else in between. Without a solution to meet the changing market needs to manage these complex data sets, commercial property insurers’ ability to quickly respond to markets and aggressively price business is hindered.

The inability to address these issues has obstructed the process, making it prone to error and hard to scale, especially in today’s market. In turn, this obstruction limits the speed and accuracy of commercial insurers’ decision making and debilitates businesses’ potential to grow. The gap between data preparation, screening, prioritization, analysis and pricing steepens, and companies find themselves stagnant and looking for answers. There is yet to be a commercially viable solution focused exclusively on automating the operational preparation and processing component of commercial property insurance data so companies can better meet the growing need of customers and markets and handle the substantial work that is required.

A company’s inability to respond quickly can affect the relationship with the producer, leading to a higher chance of being selected against. These types of companies are more likely to take on more complex characteristics, along with riskier business as the expectation of long processing times is already set.

But we’re starting to implement machine learning into problem-solving tools to address these challenges. These tools enable commercial re-insurers to take their raw data sources and harmonize them with next-gen technology that analyzes, reviews and writes business submissions to provide companies with the competitive edge that’s been sought after for years.

Making the most of your data 

On average, commercial property insurers can only process a portion of the submissions they receive. Typically, managing and preparing results in inconsistencies surrounding labeling, coding and more, which create downstream issues with pricing, modeling and aggregation. Critical amounts of data are lost through the process, and information is not consistently accessible, hindering the ability to make crucial decisions. The only way to solve this and manage business expectations is by hiring additional skilled labor, but this increases the acquisition costs, hurts profits and isolates information among the skilled experts.

Using machine learning, data integration and analysis offer the ability to make data mapping suggestions based on learning algorithms. Manual adjustments are then fed back into the decision-making model, transforming complex, big data into actionable insights that are accessible, in real time, to the entire organization. This allows teams to spend their time on business-generating activities and acting on insights from data, instead of the constant back and forth editing spreadsheets.

See also: How Machine Learning and AI Reduce Risk  

Potential opportunities to grow the business are lost today because of the acquisition costs for new business, but machine learning allows insurers to get from point A to point B by enabling them to screen and prioritize submissions. Today, submissions can be prepared one at a time, but, with machine learning, employees are able to triage multiple submissions at once, including new submissions, enabling the underwriter to focus on the key deals and negotiating terms.

A solution for the enterprise

Giving users the ability to gain access to all commercial property data gives them a wider, more detailed view of the market as well as an understanding of the risk profiles that producers are sending. By providing an automated process to ingest and prepare data, insurers are afforded a more efficient and flexible way of consolidation that essentially helps eliminate errors, cuts costs and promotes growth as companies can now allocate resources to address other areas of the business. Ultimately, automation and machine learning provide insurers with the ability to process submissions at a much higher rate of around 80%.

While giving data access to individuals within the company is beneficial, expanding that access in the form of outsourcing can create a number of different security concerns. Many insurers are operating and sharing data globally, making security and compliance with regulations like GDPR an absolute necessity. Outsourcing is nearly impossible under GDPR due to the heightened risks in sending and having external sources manage large amounts of customer data. Insurers need to show due diligence in not only securing their own data but their customers’, as well. In place of outsourcing, we are now seeing data management and storage platforms incorporating heightened security and data integrity into the design, ensuring these tools meet security standards such as ISAE 3402, SSAE 16, AES at rest and SSL/TLS in transit and ISO 27001. Meeting the standards not only helps to prove compliance with regulatory requirements, it also shows customers that insurers are taking their data privacy demands seriously.

Looking ahead

For the commercial property insurer, it is of the utmost importance to have the ability to prepare and manage complex data sets with an easy, quantifiable solution. Emerging solutions across the industry will enable insurers to make fast, appropriate decisions required to address the always-changing market and expand the business.

See also: How Machine Learning Transforms Insurance  

With the introduction of technology such as artificial intelligence and blockchain, combined with machine learning, the realm for new directions provides the insurance industry an unprecedented opportunity to collaborate. While these changes will continue to bring us new and improved methods to get things done faster and more efficiently, one thing is certain, ambitious commercial property insurers are already discovering collaborative initiatives to establish concept cases.

Three Key Takeaways

  • The current processes are putting insurers behind their competitors in the commercial property market because they typically process 20% to 30% of the submissions received.
  • Machine learning is allowing insurers to triage, screen, prioritize and score submissions much faster and with a higher output rate.
  • The result is more completed submissions, which leads to the ability to be first to market.

Do We Need Thought Leaders, or Followers?

Credit the coronavirus with one thing. In an era when individuals and organizations strive to establish themselves as “thought leaders,” COVID-19 has vividly–or, should we say, morbidly–demonstrated the importance of people becoming sensible “thought followers.”

Whatever one’s assessment of the public sector’s response to the pandemic, many believe there has been too much comment from too many quarters on the crisis, exposing people to an incessant barrage of information and misinformation. If there was ever a time to refrain from talking unless you have something truly new and valuable to say, this is it.

So, it is certainly gracious for an organization devoted to thought leadership to give room to someone promoting the idea of “thought followership.” It’s also presumptuous for me to think that what I have to say is “truly new and valuable” at a time when “stay home and shut up” is a civic calling. But, here goes.

A sacrilege?

The premise of thought leadership is that there is value in being able to come up with new ideas, either to solve problems or shape how others see them. Being perceived as a thought leader is considered to have value in itself, beyond any immediate impact that one’s ideas might have.

With regard to insurance, an abiding message of commentary over the past 10 to 15 years has been that insurers face transformational changes that threaten to “disrupt” the industry. “Insurtech” upstarts threatened to replace incumbents in a manner similar to how Amazon displaced traditional retailers. In the view of many “thought leaders,” the insurance industry had to shed its hidebound legacy methods and get into the 21st century, or, or—what?

What, indeed, would happen if the insurance business as a whole remained operationally behind the times in the eyes of people not on the front lines of accepting and compensating risk?

It may seem sacrilegious to say so, but it is more important for insurers to follow than to lead. Innovation and disruption are givens in the modern economy; insurance is used to limit their potentially damaging effects. Insurance is there to preserve things as they were, to the extent possible.

Whose job is it?

To that end, it is not the job of the insurance business as a whole to figure out how to manage new and different ways of risk transfer. On the contrary, it’s up to those creating the risks to convince insurers that the risks can be adequately identified, managed, allocated and priced. 

There’s no right to be insured for property and liability losses. We lose sight of this basic fact because we’ve come to expect a competitive market for coverage to emerge almost automatically whenever a new form of enterprise emerges, almost as if it were a matter of right. 

The typical progression is for E&S markets to experiment with coverage of emerging risks until enough experience is acquired to write them in admitted markets. If professional insurers cannot come up with a way to sustainably insure certain risks, whoever has those risks will either retain them or create their own insurance entity to share the exposure (a mutual company, captive or risk retention group).

Of course, given the robust competition in U.S. insurance, no sensible person would deny that individual insurers must innovate in some way to remain competitive in the long run.

Still, prospective vendors and market entrants are often surprised to see how many insurance organizations remain viable despite having what the newcomers regard as outdated products, services and operations. “Incumbents” with “legacy” processes are likened to prehistoric fauna headed for extinction, yet they still manage to lumber along.

First, be dependable

How is it possible that an entire industry could appear to lag behind others and continue growing? There are a lot of reasons, and, yes, regulation is one of them, but it’s not the only one, nor even the most important. 

If insurance seems to be mired in inertia, it’s because stakeholders in commerce—consumers, organizations, lenders and public authorities—want insurance to be dependable more than they want it to be innovative. 

When it comes to core insurance products, these stakeholders value what’s old, established and expected. Sure, products have to address current exposures, but there’s been little desire and some resistance to having policies that are new and different for their own sake.

To explain, I’ll provide an anecdote from when I worked for an advisory organization that developed policy forms and manuals for standardized lines of P&C insurance. 

See also: Digital Darwinism: Time to Move Faster

As new employees went through orientation, I used to explain how we competed with the market leader to produce forms that had content almost entirely equivalent to that of the market leader. In other words, we competed to standardize, if you can grasp that. We had to be different enough to add value while adhering to long-established parameters and practices of coverage.

To describe the implications of this, I would ask new employees to consider what would happen if they showed up at a mortgage closing with a “new model,” “cutting edge,” “outside-the-box” homeowners policy. Even in the unlikely event the policy was approved by regulators, the transaction would come to a halt. The parties could not stop to read and interpret a new policy. To proceed, they would need coverage in place in a format they immediately recognize.


Now, my premise above is being challenged by Berkshire-Hathaway, whose three-page small business policy, called “THREE,” provides broad commercial property and liability coverage in a short policy designed to be read and comprehended by the policyholder. If THREE takes off and starts a trend, that would truly upend decades of insurance marketing practices—and probably lead to a new standard approach.

At this point, some readers will object that my analysis has overlooked insurance distribution and claims management, two transactional elements of insurance where buyers’ expectations are established by the experiences they have with banks, retailers, service providers and other organizations outside of insurance.

In that case, it is indeed wise to be a thought follower, or practice follower, in the wake of industries that specialize in transactions. There’s no need to be a leader here, because insurance is not a transaction-rich business, compared with others.

Think about it. How many transactions a month do you have with your financial institution, supermarket, gas station, utility companies and other providers? Compare that with how many transactions a year—or even in your life—you’ve had with your P&C insurer(s). There’s no comparison.

One of the biggest misconceptions about insurance is that the business has “fallen behind” other financial services in embracing and using technology. The fact is, insurers were among the first major adopters of computer technology as record keeping and manual calculations were moved onto electronic media, and insurers eagerly embraced online data and analytics as they emerged at the turn of the 21st century.

If insurers “fell behind,” in the eyes of some, it was principally in the 1980s and 1990s, when other industries implemented technology for massive volumes of user-generated transactions that insurers did not have or need.

Insurance transactions are better compared to one’s relationship with a lawyer than with other financial services. People buy insurance the way they buy legal services, with the hope they will never have to make contact, but with the expectation they will be fully and competently supported if they do.

Like legal advice, insurance is really a consultative service. Online portals will sell standardized coverage commodities, like low-cost auto coverage. “Insurance,” properly understood, will consult with households and business to help them select the right types and amounts of protection against a growing range of risks. That will certainly entail a good deal of innovation and thought leadership, but it will also entail a return to the founding principles and practices of modern insurance.

Fail fast? Not here

On their own, constraints on policy form development shouldn’t suppress innovation in insurance, but they do place boundaries on it, boundaries that extend to the corresponding loss information used to help price coverage. 

More than that, however, enduring expectations and practices regarding coverage shape the culture of insurance companies and the industry in general. Given the inherent limitations on insurance product innovation when compared with other industries, insurance is going to attract and retain individuals who value dependability over disruptive change.

Those who prefer the breakneck pace of disruptive change would be frustrated to learn that the current mantra of “fail fast” has little application in insurance. 

“Fail fast” refers to an organization’s toleration for experimental change whose results, good or bad, can be quickly demonstrated, acknowledged and, if necessary, abandoned. Fail-fast is a path to innovative breakthroughs in many industries and can be safely applied to internal agency and carrier operations with limited exposure to product performance or market conduct.

There is almost no room for failing fast in core insurance operations, however.

An underpriced exposure in a portfolio of property accounts can devastate the combined ratio of that book. An overlooked or unintended exposure in liability accounts can do the same. Errors like these can linger in a book of business for months or years before being detected, at which time it may be too late to compensate for the damage.

Again, beyond the immediate impact of the operating results, this limitation on insurance innovation shapes the culture of companies and the industry by self-selecting for people most comfortable operating under such constraints.

A.M. Best weighs in

This discussion might be academic if not for the fact that A.M. Best has just begun formally incorporating an insurance company’s ability to innovate into Best’s assessment of the overall strength of an insurer.

Innovation and thought leadership are not the same thing, as the former can be carried out quietly, and often is in the world of insurance, where even small and subtle adjustments can create competitive advantages that carriers are reluctant to share publicly.

Nonetheless, innovation and thought leadership share the same basic premise: The ability to generate and implement new ideas is seen to have value in itself, apart from their actual impact. The implicit presumption is that an innovative company culture will generate enough good ideas to more than compensate for any bad ideas that are tried and rejected.

Early on, some observers questioned the need for Best to assess innovation separately. If the ability to innovate makes a company stronger, won’t the existing measures of company strength reflect that?

For its part, Best argues that, to the extent insurers can innovate, they can “better respond to external challenges such as evolving consumer preferences, growing business complexity, shifting market dynamics and ever-expanding technological advancements.”

Best adds that “insurers that successfully incorporate innovation will likely strengthen their organizations, increase their customer base and improve their efficiency, supporting their financial strength.”

It’s hard to argue with that, but the issue becomes a little murky when one considers the actual criteria for rating innovation. In one key section, the new methodology scores a company’s “level of transformation” due to innovation according to four statements, labeled 1-4, with one being the lowest and four the highest (best) score.

  1. The company’s innovation output is primarily the result of replication of well-used or mature processes or technology.
    Why is this a weakness? If you can do something with existing tools and methods, why change?
  2. The company’s innovation output is not industry-leading. The company has adopted some emerging technologies.
    Shouldn’t insurers be selective in their technology investments?
  3. The company’s output indicates that it is an industry leader in innovation. Peers often replicate the output results. The company is viewed as a leader in the industry.
    Peers often replicate the output results.” Where do you want to be in the chain of innovation investment?
  4. The company effectively uses cutting-edge processes and technology throughout the enterprise. The company’s innovation is at levels comparable to leaders even outside the insurance industry.
    If your company really needs and can use “cutting-edge processes and technologies,” go for it. If you want to be “comparable to leaders outside the insurance industry,” knock yourself out. But if you want to insure risks on a sustainably profitable basis, why not do so with the minimum investment on your end, and with the greatest possible commitment and contribution by those whose risks you are assuming?

Now, no one should let off-the-cuff comments of an industry observer diminish the importance of what A.M. Best is trying to accomplish. Following extensive review by and input from the industry, the new criteria are thorough and carefully explained, and compose only a fraction of a company assessment.

See also: Will COVID-19 Disrupt Insurtech?  

But insurance professionals should not be distracted by this important initiative from recognizing and embracing the historic role of this business in preserving value so that innovators can create value. One might say that insurance is the protective yin to the dynamic yang of a modern economy, an essential complement that responds to different imperatives.

Thought-following essentials

What, then, is “thought followership?” I would describe it as a series of commitments and understandings to guide decisions within an insurance organization and in its dealings with customers:

  • A commitment to minimize disruption until it can add real value;
  • A commitment to use established business practices and methods of communication until others are shown to better add or preserve value; and
  • An understanding that, all else being equal, an established idea or practice is actually better than a new one, simply because of its track record and common understanding.

For the most part, insurance professionals already demonstrate these commitments and understandings, even as they improve the way coverage is delivered. That approach simply hasn’t been fashionable of late, and the industry and the people who work in it are continually told they have to change the way they think. Well, they don’t, fundamentally. What they have to do is follow where other sectors are leading, and provide old-fashioned assurances to leading-edge enterprises.

How AI Can Stop Workers’ Comp Fraud

Wondering how AI can help detect medical provider scams? Wonder no more.

Artificial intelligence (AI) is redefining work in nearly every industry thanks to the increase in accuracy, efficiency and cost-effectiveness that AI-based applications offer. One of the latest industries to benefit is insurance, where applications are now being deployed to help detect and reduce provider fraud through advanced predictive tools. Claims payers identify fraudulent providers early in the life of a claim and root out bad actors while saving organizations millions of dollars.

The Fraud Problem

Fraud involves deliberately presenting false information to extract a benefit. The most common examples of provider fraud include “phantom billing” (billing for services not rendered), submitting bills for more services than are possible in a provider’s day, providing services unrelated to the injury, using unlicensed or non-credentialed individuals to provide medical services, getting paid kickbacks in exchange for sending patients to third parties and referring patients to entities (such as laboratories or testing facilities) in which the provider has an ownership interest.

While most providers do not engage in fraud, those that do are extremely costly. According to the National Insurance Crime Bureau (NICB), workers’ compensation medical fraud costs approximately $30 billion per year in the U.S. alone.

Fraudulent provider behavior is hard to detect and prove, particularly in workers’ compensation data systems. Advanced data analytics based on AI, however, offers opportunities to overcome the inherent weaknesses in these systems while developing methods to identify and curb provider fraud. Let’s take a look.

Fragmentation of Payers

One of the biggest issues in provider fraud is that no one organization has more than 5% of workers’ compensation market share, so none can see the entire picture of a provider’s claims. This can cause a whole host of issues. For example, if one company has identified a fraudulent provider, other companies may not have this information and continue payments. In states where fraud information is publicly available, providers simply begin practicing in other states, avoiding the state that sanctioned them.

Using AI tools, however, organizations can tap into multipayer pools of aggregate information to spot fraudulent patterns quickly and reliably without compromising payer, employer and employee information. It also makes it easier to flag and curb behavior across a multipayer database.

See also: Untapped Potential of Artificial Intelligence

Inaccurate Provider Identification

The constantly changing complexity of provider identification is another major challenge. Data is often tied to names. Fraudulent providers know this system weakness and frequently change their organization names and addresses as well as other identifiers.

Using AI, data scientists can now reliably link multiple bills from the same provider using a National Provider Identifier (NPI) developed by the Centers for Medicare and Medicaid Services (CMS). Almost all providers have an NPI, and some have more than one. When supplemented with taxpayer identification (FEIN) numbers and license numbers, NPIs can reliably identify 95% of medical providers. As a result, machines can overcome the name game, detecting the long-term, multiyear activities of almost all providers and provider organizations.

Long Lag Times

The interval between when an instance of fraud occurs and when it is detected is often several years. For example, a provider may submit a bill on day one for services unrelated to the injury; the bill will be submitted for review 30 days later and will likely be paid in another 30 days. This practice will be repeated dozens of times by the same provider on the same patient over the course of months. If fraud is detected, the provider will have already been paid, and financial recovery is difficult.

To combat this problem, AI can detect the entire course of treatment on the same claim from the first through subsequent billings over multiple years. Software tracks the diagnoses and the number of procedure codes billed by the same provider on the same claim — per day, per month and per year. As a result, claims staff receive real- time alerts and can intervene when a fraudulent provider initiates treatment on a claim.

Complex Provider Supply Chains

The entire fraud supply chain often includes attorneys, medical providers, outpatient and inpatient facilities, interpreters, testing facilities, medical device suppliers, pharmacies, copy services and transportation services. Unless data sets capture all or most of these moving parts, the chance of detecting fraudulent patterns is very difficult.

With AI, it’s getting a lot easier. Data scientists can use aggregated data to track sequences of out-referral and in-referral, exposing links between fraudulent individuals and entities. Sophisticated techniques isolate consistent and repeatable patterns of relationships between multiple providers and third parties. Data scientists then can graphically display suspicious network clustering patterns inherent in fraud networks.

And these are just a few examples of how AI tools can greatly increase the detection of fraud.

See also: Impact of COVID-19 on Workers’ Comp

Defining the Future of Claims

AI differs from more traditional research approaches because it can generate its own rules to detect fraud and look across large data sets nearly instantly. Via machine learning, databases are continually refreshed, becoming smarter and more effective all the time. By incorporating AI-based solutions, insurance payers can defeat fraud at a systemic level and realize significant financial benefits in return.

As first published in The CLM.

Cloud Computing Wins in COVID-19 World

Through the countless discussions that have occurred these past two weeks with many insurers, there have been winning strategies that have shone through as insurers have been executing their business continuity plans. And there have been certain challenges on the other side. Were you a company that needed the support of people to be in the office to “load the tapes,” making sure all those batch jobs on the mainframe computer kept running? Did you have challenges making applications available to your now-remote workforce? Were your call centers still able to fully support agents and policyholders?

One of the greatest successes in this market has been the performance of cloud computing. I remember, back in 2012, discussing the advantages of cloud computing. As an industry, there was just experimental acceptance of this capability – usually relegated to sandbox environments and testing. Jump forward to 2020 – and we see that 84% of all core system buying transactions were cloud-based. Not only have we leapt forward in our use of cloud, but we are now in mainstream acceptance that core systems – some of the most critical systems in the enterprise – are being commonly deployed in the cloud.

Let’s consider for a moment some of the advantages of systems that are deployed in the cloud – just to name a few that have been experienced over the past two weeks:

  • Cloud provides a virtual computing environment that also enables virtualized managed services.
  • New environments can be created to dynamically test changes.  
  • Access is available – for all that need to use the applications regardless of physical location.
  • Cloud decreases the need for “onsite” resources – elimination of tape loads, etc.

Investments that insurers continue to make to transform their organizations are bearing fruit today (even though we do not want to have to go through a pandemic to realize this truth). The digitally enabled experiences that insurers are providing to their customers and distribution partners are critical. Never before has it been more important to provide full transparency to the customer. For some, you are experiencing the world as it was before COVID-19 – a world of transformation that was moving forward, understanding the importance of the digital experience, and looking at ways to provide these capabilities. Today we are in a state where these digital experiences are a reality.

See also: Will COVID-19 Disrupt Insurtech?  

If the events of the past few weeks find you considering cloud deployment for your applications moving forward, refer to our recent research report, Cloud and Core Systems: Top 10 Strategic Considerations, for insights on buying cloud-deployed software solutions. Cloud will be one of the levers that insurers can use to meet the digital mandate that is no longer for the future – but is here today.

Claims Industry Set for Telecommuting

Insurance carriers and claim professionals deal with various catastrophes each year, so it was only fitting that when COVID-19 struck they were some of the first prepared to revert to “emergency work from home mode.”

With many other industries trying to determine how (and if) their workers can take on remote duties, the insurance world led the way with flexibility and (for the most part) ordered adjusters to telecommute so they would not miss a beat of their daily workload. 

This trillion-dollar industry is fairly secure in most crises (the popular mantra being that “everyone needs insurance.”) Understood are the IT capabilities needed in advance and the supervisor’s faith in employees, as they’ve been doing this sort of thing for years. It was as easy as a keystroke from insurance upper management to keep workers from driving to their respective claim centers with their laptops and instead plugging in at home and being ready to go. 

Some insurance companies were first to respond in the U.S. by canceling all in-person (unnecessary) meetings and going virtual. They left some of their fellow businesses they share office space with in the dust as those other companies continued to mull over their options (or until the point of being bound by local government orders). Carriers have, for the most part, been eager to protect their workers from risk (isn’t that what insurance is known for?) A possible hazard to staff meant a swift and immediate decision to work from home. 

Many carriers have realized the benefits of this arrangement, and even that many employees may put in more hours when working at home, saving themselves a tiresome commute (the average worker in the U.S. commutes over four hours a week, and some high-traffic areas require much more than that). 

See also: Moral Imperative for the Insurance Industry  

Most carriers also subscribe to the notion of in-office safety, encouraging those who are sick to work remotely, whereas some organizations may suggest workers come in or otherwise use a paid time off (PTO) day (few employees are pleased with that option as the average PTO days per year that Americans receive are quite low compared with other countries – another conversation, however!).

Many articles have been recently published with “work from home” tips; below are some of the more applicable to insurance industry professionals:

IF Insurance has penned a column called “How to work from home safely and efficiently?” It discusses an important topic in claims as it suggests that “Remote work provides several benefits, such as the possibility to focus deeply on specific tasks that require uninterrupted concentration.” For that large litigation claim file with extensive injuries, this makes much sense; fewer interruptions makes it easier to focus on complex claims. Some other useful tips of the article include letting family members know you need to work in peace and keeping an eye on ergonomics and the setup at home (is that monitor at the correct level?). Planning your breaks with a clear start and end time is also key. Remember to keep in touch with colleagues, and don’t isolate yourself completely!

Working From Home Can Mean You Never Stop Working” is a recent piece from Philadelphia Magazine that reminds us all to keep a better work-life balance while doing so and setting rituals for logging on and off while not falling victim to some of the various pitfalls. Remember to move around so as not sit in one spot all day. Have a list of your priorities for the day and use noise canceling headphones if needed to minimize distractions.

See also: Claims: Beyond the ‘Moment of Truth’  

Arch Daily’s website discusses tips for architects adjusting to the new experience of working from home. Largely, these professionals are used to collaborating with others in an office setting and now need to learn how to use digital technology to replicate those interactions. The article offers very useful information for experts in this field (and all others) to adapt to the times we face.

Will other industries learn from insurance and be well-equipped in the future? 

Or will the world change and move drastically to remote working after realizing some of its benefits? And is it really any surprise that insurance carriers are setting the example? 

After all, insurance and risk management by definition set out to identify, evaluate and prioritize risks and apply the use of resources to minimize the impact of unfortunate events (like right now).