Chief risk officer of Venerable Holdings Inc., Charles Schwartz, says: “The insurance risk profession is adept at capturing and assessing the risk of ‘known unknowns,’ less so the ‘unknown unknowns.’” To help us think about risks that may be perceived as less common or less likely—but may very well be the next major risk—here are four pieces on key emerging topics across InsurTech, FinTech, risk interdependence and strategic risk management.
Let’s start with InsurTech. As recently as three years ago, few had heard of it. Yet by the end of 2016, it was top-of-mind for many, and ITA PRO magazine named it “word of the year.” Martin Snow, FSA, MAAA, and Theresa Rollin, PMP, describe artificial intelligence (AI) and the risks the InsurTech industry faces. The broader category of FinTech, which applies to all financial services and is more developed than InsurTech, has its own unique risks. Michael Leibrock discusses the balancing of FinTech opportunities with the risks.
The interdependence of risk surrounds us and is easily forgettable when things are going well. April Shen, FSA, CFA, CERA, MAAA, encourages us to consider these risks even when all seems to be in order, as that is precisely the time to prepare. The final piece by Tricia Matson, FSA, MAAA, focuses on employing strategic risk management—how can risk management be done consistently and in advance to improve corporate decision-making?
We hope this content inspires you to think about other risks around the industry that may not always be top of mind—the “unknown unknowns.”
The Predictive Analytics Revolution: What You Need to Do
By Martin Snow and Theresa Rollin
One of the biggest risks facing the insurance industry today is that companies do not fully embrace how data and the tools to analyze it will completely change the insurance paradigm. Data enabled Netflix to make Blockbuster obsolete, and the mobile phone is starting to make bank tellers obsolete with the mobile depositing of checks via the phone’s camera and internet connection. Likewise, we believe predictive analytics, artificial intelligence (AI) and machine learning will create a massive revolution in the insurance industry—equal in scale and scope to digital cameras overtaking film and mobile phones making all of your data portable. The determinant of future success for established insurers will be how well they embrace new technologies.
We see some insurers making investments in predictive analytics, such as automated underwriting and data science teams. But this is taking place on a smaller scale than is necessary to reap the full rewards of the technology. For example, most companies are not using existing capabilities to:
- Identify and reach out to the millions of people who do not have sufficient insurance.
- Proactively manage health insurance claims to benefit both the customer and the insurer.
- Assess and manage significant risks with more precision and reduced variability.
As Clayton Christensen and Scott Anthony point out in their book, Seeing What’s Next: Using the Theories of Innovation to Predict Industry Change, established firms successfully tackle opportunities based on the sources of today’s revenue, where they have resources to succeed, what their existing processes facilitate, and how their values suggest prioritization in the context of all other resource demands. Although predictive analytics can help a company’s most valued customers today, it requires new resources, processes and values. Current processes are not enabling companies to consider what tomorrow’s values will be, from where revenue will be derived in the future and how new entrants enter the market.
Innovators succeed by finding niche areas to enter the market and continue to improve their processes while established players continue to prioritize investments in their existing processes that generate more revenue in the near term. By the time the established players realize a new technology is a threat, the innovators are well ahead of the previously established players. Consider what Amazon and its partners are doing with Haven. Companies risk a new entrant such as Amazon or Google building products and processes designed around the new technology, as well as others embracing these technologies to better manage balance sheet risk and variability.
We recommend that insurers consider if they are prepared to allow this to happen while they continue to consider the best approach. Insurers will want to look closely at what predictive analytics can offer and update their resources, processes and values (culture)—including decision-making, organization and strategic planning—to get ahead of the emerging predictive analytics revolution. These are not easy changes to make, and they may require augmenting, training or adding personnel; leveraging third parties; and extensive cultural change management.
Core changes are required for insurers to succeed and reap the major strategic benefits that will accrue for early adopters. How are you going to change the thinking at your company? Will you continue to invest in products like Kodak film and Blockbuster videos that will soon become obsolete? Or will you move to the future?
The Future of FinTech
By Michael Leibrock
The rapid emergence of new FinTech applications is one of the most promising developments affecting the financial services industry today, yet it has the potential to drive increased risk as adoption increases. As a result, it is critical for firms to ensure implementation decisions balance business value with the potential risk of adopting emerging FinTech.
Some key factors firms should evaluate when considering FinTech adoption are:1
- The provision of core banking functions by FinTech firms. FinTech companies that provide core banking functions could enhance financial stability through diversification of credit and liquidity risk. However, given the short track record of these companies, this could also create systemic vulnerabilities.
- The level of FinTech-related fragmentation. The unbundling of financial services associated with the rise of FinTech has the potential to fragment the creation and delivery of financial services across additional providers and platforms.
- The impact of FinTech on concentration risk. The rise of FinTech could reduce concentration risk by allowing nontraditional service providers to compete with incumbent firms. Conversely, it also could create new pockets of risk should a small cluster of FinTech companies become dominant in any given area.
- The degree of reliance on automated decision-making processes. Overreliance on purely data-driven algorithms could lead to errors that might not have occurred in an environment that requires additional human judgment. In addition, due to the inherent complexity of decision-making algorithms, their opaque nature also could hide biases that may be hard to identify.
- The sustained growth and adoption of FinTech services. The impact of FinTech depends on the extent to which it becomes a mainstream component of the financial ecosystem and how it will ultimately be used for delivering critical financial services.
By gaining a better understanding of systemic risks, firms can ensure the continued safety of the industry. Given the rapid pace of FinTech adoption, it is imperative that firms begin to establish an appropriate internal assessment framework now, before potential risks emerge and create real financial losses or operational incidents.
1 Gray, Andrew, and Michael Leibrock. FinTech and Financial Stability: Exploring How Technological Innovations Could Impact the Safety & Security of Global Markets. Depository Trust & Clearing Corporation, October 2017, (accessed April 5, 2019).
Bread and Butter: The Interdependence of Risks
By April Shen
Actuaries are experts in quantifying and managing risks, including analyzing risks on a stand-alone basis as well as integrating and stressing risks simultaneously. The interdependence of different types of risks is relevant to an actuary’s risk management strategy.
The interdependence of risks could be triggered by the causality between risk A and risk B, which is relatively more predominant. However, there’s more subtle interdependence when risk A and risk B are merely associated or caused by a mutual risk C, which may not be analyzed directly. For example, the International Swaps and Derivatives Association (ISDA) defines “wrong-way risk” as the risk that occurs when “exposure to a counterparty is adversely correlated with the credit quality of that counterparty.” Essentially, this risk emerges when default risk and credit exposure increase simultaneously, which may be caused by macroeconomic factors.
Wrong-way risk exemplifies interdependence of risks that actuaries can incorporate in their models by making mitigating assumptions. It might lead to changes in the overall risk management strategy or risk budget in stress scenarios when the credit risk is high—for example, the incentive to purchase a credit default swap (CDS) instrument from an independent counterparty to break the dependence. Another example of interdependence is the interaction of modeling risk and market risk. While companies use complicated simulations to model market risks, the complexity of the models poses additional risk, such as the boundaries and limitations associated with the model itself.
When “Murphy’s Law” is invoked, it means everything that can go wrong will go wrong. This is symbolic of the correlation and interdependencies of risks. Looking back at the economic crisis and the mortgage market in 2007–2008, Murphy’s Law rang true. One of the reasons so few people predicted anything like the financial crisis was the common knowledge that housing markets in different parts of the United States moved independently of one another, and there was very little interdependence between the change of housing prices in one region with that in other regions.1 However, as Andy Laperriere of ISI Group said in November 2007, “As an old pro in the mortgage business told us yesterday, the developments in the housing and mortgage finance market have been consistent with the most pessimistic scenario one could have imagined.”2
There are many ways to model interdependent risks, such as simulation, copula, integrated testing and stress testing. Actuaries should contemplate the impacts of interdependence in their risk management decision-making process and overall organizational strategy. The bread can fall with the buttered side down when it matters most.
Improving Performance Through Strategic Risk Management
By Tricia Matson
As the U.S. insurance industry continues to develop more mature enterprise risk management (ERM) programs, there is increased focus on strategic risk management. In other words, the industry is using these ERM processes and supporting analyses to make better strategic decisions across the enterprise. Historically, many insurers have made decisions with a focus on accounting metrics and within individual business lines or functional areas. For example, new product pricing is often focused on internal rates of return. Enterprise hedging or reinsurance decisions may be focused on reducing U.S. generally accepted accounting principles (GAAP) earnings volatility or statutory capital management. While some have looked at this through more of an economic lens (for example, for purposes of asset liability management), it has not typically been on an enterprisewide basis. Diversification across risks may have been considered in a qualitative way, but historically it has not been part of what drives the metrics considered in strategic decision-making.
This is changing for a variety of reasons, including:
- Better and faster enterprisewide risk and return metrics, often developed by the ERM function
- Continued focus on strategic risk management by rating agencies, most notably S&P
- Increased competition and reduced margins in insurance products
Strategic risk management involves evaluating decisions through a consistent and robust risk-and-reward lens before they are undertaken. Exactly how a company defines risk and reward will and should depend on its individual facts and circumstances. For example, a company may be very focused on GAAP earnings and therefore uses that as the reward metric. Critical to the process, however, is the inclusion of a risk metric that consistently captures all material risks across the enterprise, including diversification benefits. Since many organizations haven’t historically had such a metric, strategic risk management in its complete sense has been very challenging. But now many organizations have an internal risk capital metric (such as economic capital) that can be used.
Companies that have a robust and consistent way to evaluate risk and reward—and use that information regularly as part of the strategic decision process—will, over time, outperform their peers that do not use strategic risk management (assuming all else is equal). While many organizations may focus on strategic risk management due to a push from external stakeholders (such as rating agencies), its use is potentially the best way to unlock the value of a mature ERM function.
Copyright © 2019 by the Society of Actuaries, Schaumburg, Illinois.