As organisations continue to evolve their risk governance practises, focused and relevant information about emerging risks is at a premium. The objective of Protiviti’s PreView newsletters to provide an input for these efforts as companies focus on risks that are developing in the market. We discuss emergent issues and look back at topics we’ve covered to help organisations understand how these risks are evolving and anticipate their potential ramifications.
As you review the topics in this issue, we encourage you to think about your organisation and ask probing questions: How will these risks affect us? What should we do now to prepare? Is there an opportunity we should pursue?
Our framework for evaluation of risks is rooted in the global risk categories designed by the World Economic Forum. Throughout this series, we use these categories to classify macro-level topics and the challenges they present.
Inside this issue:
Emerging Risk Categories: Technological, Environmental, Societal, Economic
Industries Impacted: Government, Financial Services, Technology, Consumer Products & Services, Agriculture, Healthcare & Life Sciences, Industrial Products & Services
Nanotechnology involves the ability to see, control and manufacture materials and devices on the scale of individual atoms and molecules, and even molecular subunits (supramolecular level). Since everything on the planet consists of atoms, the application of nanotechnology is far-reaching. As scientists and engineers find ways to manipulate materials at the nanoscale to take advantage of enhanced properties, such as higher strength or lighter weight, many possible applications suggest themselves across multiple industries.
Nanoscience has been evolving since 1959, when physicist Richard Feynman introduced the concept of manipulating atoms on an infinitesimal scale of 10-9 metres (aka nanoscale). Since then, scientists in all fields — chemistry, biology, physics, material science, engineering, etc. — have been designing structures and systems to perform these manipulations. Configurations on a larger scale are constricted to certain properties of atoms, such as weight, strength, reactivity and response to light; however, at the nanoscale, these structural properties can be altered to increase a material’s usefulness, advancing societal needs. These extremely small structures can be used for anything, from increasing the strength of sporting equipment to delivering drugs to diseased cells in a person’s body.
Over the past several decades, nanotechnology has become increasingly relevant. The nanotechnology market is estimated to reach US$12.83 billion by 2021, demonstrating the promising nature of nanostructures. While the U.S. is currently the largest market for nanotechnology, the Asia-Pacific region, Brazil and Canada are all poised for considerable growth in the coming years due to favourable microeconomic conditions and investments.
Use of Nanomaterials by Industry
Source: Nanotechnology Products Database, StatNano 2016: product.statnano.com/
Key Considerations and Implications
As with any rapidly evolving field, new applications for nanotechnology are appearing constantly, along with considerations regarding the future of the technology. Among them:
- Regulation — Due to the variety of nanotech applications, supervision of nanotech research can be very complex and difficult to apply. Care must be taken not to strangle a burgeoning field with regulation, while providing a safe environment for beneficial research across many different fields.
- Transparency — Nanotechnology may be used in controversial applications, such as surveillance instruments, miniature guns and explosives, or weapons with the ability to attack physical structures or biological organisms at the molecular level. The technology is not visible to the naked eye, raising concerns of the ability to easily monitor the use of these new applications and provide an appropriate quality control framework. On the sensational end of the spectrum, some have expressed concern that self-replicating nanobots can wreak havoc on Earth if they are not properly controlled.
- Privacy and consent — In healthcare, applications of nanotechnology may represent forms of invasive medicine, raising issues such as appropriate disclosures, patient consent and privacy. Such issues need to be considered early on, before widespread application of the technology in the medical field.
- The environment — The energy industry could experience huge advantages from applications of nanotechnology that can produce fossil fuels more efficiently, as well as spur the production of a number of “green” fuels. In addition, nanotech applications in controlling water pollution could greatly alter environmental risk assessments, enabling approvals of oil extraction and other controversial projects. This, in turn, could shift the weight of environ- mental concerns from production methods and water pollution to other risks, such as carbon emissions.
Spotlight: Industry Applications of Nanotechnology
Nanotechnology is already being used by consumers in everyday products, such as stain-resistant clothing, stronger, thinner and lighter bottles and packaging, and stronger tennis balls and rackets. Below are examples of other applications across industries:
Emerging Risk Categories: Economic, Environmental, Geopolitical
Industries Impacted: Agriculture, Energy & Utilities, Manufacturing, Consumer Products & Services
While oil is often first to come to mind in discussions of natural resource scarcity and competition, fresh water is quickly overtaking oil as the world’s most precious commodity. The increasingly strained supply of this indispensable resource is approaching a level of criticality worldwide that cannot be ignored. At the present time, about 4 billion people, comprising approximately two-thirds of the world’s total population, experience severe water shortages during at least one month every year. Based on current trends, it is estimated that by 2030, water demand will exceed sustainable supply by 40 percent, a gap that presents significant humanitarian, economic and geopolitical concerns. As policymakers, environmental scientists and conservationists grapple with the best ways to address this crisis, the continuous decline in fresh water availability and quality warrants attention as one of the leading global risks we face today, and in the coming decades.
Projected Water Stress by Country, 2030
The above graphic was created based on the World Resources Institute’s (WRI) Water Stress Projections report, which depicts the ratio between water withdrawals and total availability by country (year 2030, business-as-usual [BAU] tab). Higher percentages correspond to greater competition among water users.
Key Considerations and Implications
- Urbanisation — Along with increases in overall global population, a key driver for the rising demand for fresh water is the rapid urbanisation of developing countries, especially in Latin America, Africa and Asia. As these areas experience greater economic activity, sources of nearby freshwater are being taxed to meet the demand for improved living standards, the production of goods and services, energy generation and the expansion of irrigated agriculture. By 2050, the number of people living in urban areas will almost double to about 6.3 billion people, according to United Nations (U.N.) projections. This concentration is significant given World Bank forecasts that water availability in cities could decline by as much as two-thirds by 2050, largely as a result of climate change and competition from energy generation and agriculture.
- Climate change — As freshwater withdrawals continue to increase each year, the replenishment of these sources is threatened by the impacts of climate change. Along with more frequent and extended droughts in certain regions, rising sea levels can drive saltwater into freshwater reservoirs, requiring expensive desalination efforts to return this water to a usable form. Important freshwater reservoirs, such as the Florida Everglades, which are in close proximity to coastlines, are the most susceptible to this risk.
- Human migration — Global competition for access to fresh water will likely continue to intensify in the coming decades as shortages worsen, leading to population migration and an increased risk of geopolitical conflict. Many regions of the world that are subject to water insecurity, such as North Africa and the Middle East, already experience heightened levels of tension.
- Water quality — While access to steady supplies of fresh water presents an ongoing challenge for many regions, ensuring that this water meets essential quality standards presents another. The World Bank estimates that at least 663 million people lack access to safe drinking water. This raises a variety of health concerns, highlighted by the fact that about 675,000 people die prematurely every year due to poor sanitation, water and hygiene. Even with consistent access to reliable water sources, the cost to transport, purify and store this water is often too steep for many regions of the world.
The scarcity of fresh water poses not only challenges but also opportunities for governments and the private sector to invest in fresh water projects (e.g., desalination, water purification, centralised networks, distributed water systems) as a means of capitalising on this emerging risk.
Spotlight: Water Shortage Impacts by Industry
Water is a fundamental input to nearly every industry of the global economy, and is present at virtually every stage of any given value chain, to the point that often it is taken for granted. As water scarcity increases, industry leaders will face a number of challenges with maintaining consistent access to quality water to meet business objectives and support operations. The following water-intensive industries highlight the importance of this precious natural resource:
As the global demand for safe, reliable water sources increases, current supplies will not be able to sustain future needs. Looking forward, country leaders will need to place an emphasis on and promote high-quality, renewable water practices and cooperation in solving this challenge, as water not only transcends borders but is also a basic human necessity regardless of country income or development status.
Emerging Risk Categories: Economic, Technological, Societal
Industries Impacted: Financial Services, Technology, Healthcare & Life Sciences
Machine learning, also known as Analytics 3.0, is the latest development in the field of data analytics. Machine learning allows computers to take in large amounts of data, process it, and teach themselves new skills using that input. It’s a way to achieve artificial intelligence, or AI, using a “learn by doing” process.
Machine learning enables computers to learn and act without being explicitly programmed. It evolves from the study of pattern recognition and the design and analysis of algorithms to enable learning from data and make possible data-driven predictions or decisions. It is so pervasive today that many of us likely use it several times a day without even knowing it.
In earlier stages of analytics development, the companies that most benefited from the new field were the information firms and online companies that saw and seized the opportunities of big data before others. The ability to provide much needed data and information represented a clear first mover’s advantage for these companies. While the first movers in big data were the big winners, their advantage won’t last much longer as productivity levels out. The evolution to Analytics 3.0 is a game changer because the range of business problems that intelligent automation — a mixture of AI and machine learning — can solve is increasing every day. At this stage, nearly every firm in any industry can profit from intelligent automation. Companies that invest immediately in machine learning have the potential to gain long-term benefits, profiting from the work of analytics pioneers. To gain these benefits, companies must rethink how the analysis of data can create value for them in the context of Analytics 3.0.
In PreView, Volume 2, Issue 2, we highlighted the challenges that investors in AI face, including high research and development costs and the difficulty of retaining people with the right skill sets. Still, we believe that the long- term benefits outweigh the costs. The biggest downside of not adopting AI, and specifically machine learning, early is that firms delay the opportunities to profit and risk displacement by the early movers.
Beneficial Applications Versus Risks of Machine
Key Considerations and Implications
- The moral component — The level of intelligence and “morality” that a machine exerts is a direct result of the data it receives. One consequence is that, based on the data input, machines may train themselves to work against the interest of some humans or be biased. Failure to erase bias from a machine algorithm may produce results that are not in line with the moral standards of society. Yet not all researchers, scientists and experts believe that AI will be hurtful to society. Some believe that AI can be developed to mirror the human brain and obtain human moralistic psychology to enhance society.
- Accuracy of risk assessments — Risk assessments are used in many areas of society to evaluate and measure the potential risks that may be involved in specific scenarios. The increasing popularity of using AI risk assessments to make important decisions on behalf of people is a direct result of the growing trust between humans and machines. However, there are serious implications to note when using a machine learning system to make risk assessments. A quantitative analyst estimates that some machine learning strategies may fail up to 90 percent when tested in a real-life setting. The reason is that while algorithms used in machine learning are based on an almost infinite amount of items, much of this data is very similar. For these machines, finding a pattern would be easy, but finding a pattern that will fit every real-life scenario would be difficult.
- Transparency of algorithms — Supporters of creating transparency in AI advocate for the creation of a shared and regulated database that is not in possession of any one entity that has the power to manipulate the data; however, there are many reasons why corporations are not encouraging this. While transparency may be the solution to creating trust between users and machines, not all users of machine learning see a benefit there.
Next, we highlight some of the ways these implications play out in several industries.
Today, artificial intelligence makes it possible to predict the likelihood of a heart attack with much better accuracy than before. While manual systems are able to make correct predictions with around 30 percent accuracy, a machine learning algorithm created at Carnegie Mellon University was able to raise the prediction accuracy to 80 percent. In a hospital, an 80 percent prediction theoretically would give a physician four hours to intervene before the occurrence of the life-threatening event.
However, the accuracy of risk assessments in the medical field may vary depending on the level of bias in the research used to train the machine learning algorithm. For instance, most heart disease research is conducted on men, even though heart attack symptoms between men and women differ in some important ways. If the system is trained to recognize heart attack symptoms found in men, the accuracy of predicting a heart attack in women diminishes and may result in a fatality. For that reason, people who are affected by decisions based on AI risk assessments will want to know how these decisions are systematically made.
Hedge funds, which have always relied heavily on computers to find trends in financial data, are increasingly moving toward machine learning. Their goal is to be able to automatically recognize changes in the market and react quickly in ways quant models cannot. Most of these algorithms are proprietary, for a reason. The risk of having transparency in this case is that as one fund becomes successful using a certain algorithm, others will want to mimic that company’s machine learning method, diminishing everyone’s success and creating an artificial market environment. For this reason, any regulation that attempts to control the transparency of AI must be suitable and appropriate to the various scenarios where AI is used.
The U.S. National Highway Traffic Safety Administration recently released guidelines for autonomous vehicles, requiring auto manufacturers to voluntarily submit their design, development, testing and deployment plans before going to market with their vehicles. Despite these efforts to increase the transparency around “the brains” deployed in autonomous vehicles, car manufacturers, tech companies and auto parts makers are in a tight competition to develop the software behind self-driving cars, and their need to keep development efforts under wraps to gain market advantage may end up hurting the future of autonomy.
In addition, the nature of machine learning itself makes it very difficult to prove that autonomous vehicles will operate safely. Traditional computer coding is written to meet safety requirements and then tested to verify if it was successful; however, machine learning allows a computer to learn and perform at its own pace and level of complexity. The more automakers are willing to be transparent about the data they input into the learning algorithms, the easier it will be for lawmakers and auto safety regulators to create laws that will ensure the safety of consumers.
The Effects of Populism on Trade and Regulation
Populism, defined as a belief in the power of regular people and their right to have control over their government as opposed to being controlled by a small group of political insiders or a wealthy elite, has started to influence major events on the global economic and political stage — but its effects have not yet manifested themselves fully, or they are not yet fully understood. While populism is certainly not new, it has revealed itself recently in the form of rallies against both foreign trade and regulation, stemming from the notion that these practices prevent domestic job growth and redirect power and wealth from domestic citizens and corporations to parties abroad. Due to the speed at which citizens can now organize and communicate, these movements are more impactful, less predictable, and more difficult to address than in the past. Recent events such as Brexit, the U.S. presidential election and Italian citizens’ response to proposed constitutional overhauls evidence their magnitude.
The potential implications of such movements create uncertainty. Globalization has facilitated the interchange of worldviews, products, ideas and culture for a long time and has accelerated over the last three decades. Global trading partners and supply chains can’t be cut off through the proverbial turn of the spigot without repercussions. Although more restrictive trade policies theoretically create domestic jobs, history shows these actions can also result in job loss, trade wars and an increase in the price of imports. If restrictive trade policies give rise to protectionism, that could precipitate a dangerous downward spiral.
If output declines, deflation occurs and/or unemployment increases, populist governments may be pressured to take pre-emptive action to invigorate their economies, even if that means limiting imports.
Further, repealing major regulatory frameworks, such as the one created by the Dodd-Frank Act, may reduce corporations’ costs of compliance and thus create more jobs — but this can also leave critical risks unmitigated. As citizens across the globe witness the relative ease and power of modern populist movements, it’s likely that more movements will arise, making institutions of global trade and regulatory infrastructures subject to continuous, and often unpredictable, change. This trend can have implications of historic proportions for virtually all nations and industries.
Cybersecurity Risk Revisited
In the Volume 2, Issue 1 of PreView, published in 2015, we highlighted major data breaches and their financial and compliance-related implications to select industries. Recently, federal and state governments have emerged prominently as targets of cybersecurity concerns. These government hacks extend from data breaches to suspicions of tampering with the election process. To combat the increasing threat of cybersecurity, the federal government introduced the Cybersecurity National Action Plan and also appointed the first Federal Chief Information Security Officer. Cybersecurity will continue to be an evolving and persistent risk to individuals, companies and governments, and thus a topic for future issues.
“Nanorobots: Where We Are Today and Why Their Future Has Amazing Potential,” by Peter Diamandis, Singularity Hub, May 16, 2016: http://singularityhub. com/2016/05/16/nanorobots-where-we-are-today-and- why-their-future-has-amazing-potential/.
“From Super Pills to Second Skin: Meet the Willy Wonka Revolutionizing Medicine,” by Amy Fleming, The Guardian, October 17, 2016: www.theguardian.com/ lifeandstyle/2016/oct/17/robert-langer-nanotechnology- pioneer-willy-wonka-revolutionising-medicine.
“Nanomedicine To Combat Infections From Antimicrobial- Resistant Bacteria.” Article published by Nicolas Gouze, ETPN Secretariat in the publication ‘International Innovation’ (Issue 140): www.etp-nanomedicine.eu/public/ about-nanomedicine/nanomedicine-applications/amr.
“Nanotechnology & You,” Nano.gov: www.nano.gov/you/nanotechnology-benefits.
The Global Water Crisis
“Water Shortage May Cripple Global Power Supply,” by Pola Lem, Scientific American, March 18, 2016: www.scientificamerican.com/article/water-shortage-may-cripple-global-power-supply/.
“What Is Hydrology and What Do Hydrologists Do?” by Howard Perlman, USGS, December 2, 2016: http://water.usgs.gov/edu/hydrology.html.
“2016 UN World Water Development Report, Water and Jobs,” United Nations Educational, Scientific and Cultural Organization, 2016: www.unesco.org/new/en/natural-sciences/environment/water/wwap/wwdr/2016-water-and-jobs/.
“Analytics 3.0 and Data-Driven Transformation,” by Chandramohan Kannusamy, Data Informed, March 7, 2016: http://data-informed.com/analytics-3-0-and-data-driven-transformation/.
“Machine Learning: Of Prediction and Policy,” The Economist, August 20, 2016: www.economist.com/news/finance-and-economics/21705329-governments-have-much-gain-applying-algorithms-public-policy.
“The Rise of the Artificially Intelligent Hedge Fund,” by Cade Metz, Wired.com, January 25, 2016: www.wired.com/2016/01/the-rise-of-the-artificially-intelligent-hedge-fund/.
On the Radar
“2017 Markets Likely to Soar Higher Under Trump,” by Katina Stefanova, Forbes, January 1, 2017: www.forbes.com/sites/katinastefanova/2017/01/01/markets-in-2017-reagan-style-republicanism-meets-neo-populism/.
“Italy’s Populists Claim Victory in Referendum, But Chaos Looms,” by Silvia Marchetti, TIME, December 5, 2016: http://time.com/4590204/italy-referendum-matteo-renzi-populists/.
“Presidential Proclamation — National Cybersecurity Awareness Month, 2016,” The White House, Office of the Press Secretary, September 30, 2016: www.whitehouse.gov/the-press-office/2016/09/30/presidential-proclamation-national-cybersecurity-awareness-month-2016.
“Fact Sheet: Cybersecurity National Action Plan,” The White House, Office of the Press Secretary, February 9, 2016: www.whitehouse.gov/the-press-office/2016/02/09/fact-sheet-cybersecurity-national-action-plan.
The risk areas summarised above will continue to evolve, and there is no question that new risks will emerge and affect organisations globally. We are continuing the discussion we’ve started in this newsletter on our blog, The Protiviti View (blog.protiviti.com). Our blog features commentary, insights and points of view from Protiviti leaders and subject-matter experts on key challenges and risks companies are facing today, along with new and emerging developments in the market. We invite you to subscribe and participate in our dialogue on today’s emerging risks. You can also find additional information on our microsite: protiviti.com/emergingrisks.
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