The Actuary Magazine

In the Spring 2020 issue of The Actuary, data was the theme running through many of the articles. In his editorial, Martin Snow, FSA, MAAA, spoke of the new frontier of artificial intelligence (AI) and machine learning (ML), and the challenges of overcoming the inertia with existing processes occupying valuable time and resources. Over the years, the actuarial profession has adapted well to embrace technology and thrive. Our current environment is no different, and new techniques should not be viewed as a risk but as an opportunity. However, the question is: How do we move on this opportunity?

Outside of the actuarial domain, data is a common theme, too. The Economist published an article discussing the data economy with the bold title, “The World’s Most Valuable Resource Is No Longer Oil, but Data.”1 Unlike oil, data cannot be burned to produce momentum. It requires a considerable amount of science to go from raw material to concrete value. A Gartner article predicted that through 2020, 80 percent of AI projects “will remain alchemy, run by wizards whose talents will not scale in the organization.”2 This points to an opportunity for the actuarial profession to step in. Actuaries have established a rigorous scientific approach to their work, one that is proven to scale and is leveraged by the insurance industry.

Using data for AI and ML itself is not a strategy, though. The disruption created by companies like Amazon, Uber, Microsoft, Google, Facebook and Tesla was not brought about by setting out to do data science. Google’s mission is “to organize the world’s information and make it universally accessible and useful.” Tesla’s is “to accelerate the world’s transition to sustainable energy.” And Microsoft strives “to empower every person and every organization on the planet to achieve more.” These companies are experts at creating value from data; but, more important, they have a clear purpose and know how to use data to deliver demonstrable impact.

The predicted failure rate of big data projects is sobering: “Through 2022, only 20 percent of analytic insights will deliver business outcomes,” according to Gartner.3 Bill Gates is said to think that most people overestimate what they can achieve in the short term and underestimate what they can do in the long term. In data projects, the situation is almost reversed; there’s a short-term focus on gathering data to service a future need that is only vaguely conceptualized. What engineering practices have taught us is that you must iterate toward a goal. Getting to the moon was an audacious goal; many said it was impossible. But NASA solved the problem by following a rigorous scientific process based on experiments. They took an incremental approach to get to the moon, one launch at a time—they didn’t shoot for the moon in one go.

This preamble is to say: ML and AI are an opportunity, actuaries are well-positioned to help, and success comes from driving toward valuable business outcomes using a rigorous iterative process. What next?

Customer Focus

Lowering the cost of doing business is not exciting. Paying down the technical debt4,5 that is holding back innovation is a potential source of long-term competitive advantage, but it’s often overlooked. It is not an option to just stop doing the work we must do. It’s hard to start over, from a blank sheet of paper, especially in heavily regulated industries like insurance. In the United Kingdom, Starling Bank, a FinTech and “challenger bank” founded in 2016, is doing well starting from a blank sheet, with 1.4 million personal accounts and 155,000 business accounts.6 It is unencumbered by legacy systems, which is the basis of one of its competitive advantages: “Starling’s world-class tech reimagines banking for life today, putting the tools people need to feel good about money in the palm of their hand.”7 InsurTech has startups in health and property and casualty, but, looking at the top 15 companies in January 2020, there are none of note in the life insurance sector.8

Although regulation remains a barrier to innovation, it may not make for a level playing field. For example, the Volkswagen Group caught regulators’ attention for cheating emissions standards, and they are now going to be subject to increased scrutiny going forward, while a different automotive company might not be. Mistakes of the past are a legacy that cannot be easily undone, and new companies simply have not made big mistakes yet. Although the technology startup mindset of “move fast and break things” (the motto of Facebook’s Mark Zuckerberg) really can’t apply in insurance, the ability to take a risk needs to be there to bring about lasting change. Insurers need to lay the groundwork for regulatory approval of new products and new business practices that may emerge from the continued adoption of new technologies, and actuaries are key parts of this process.

To compete in a digital marketplace, incumbent insurers may create innovation hubs that look for ways to unlock the value of technological advances. For example, Aviva has a digital garage that “takes the IT department concept to the next level with a fast-paced agency atmosphere dedicated to producing mind-boggling digital customer experiences, quickly and often.” The focus of much of this innovation is on growing the top line. This has a material impact when the cost of customer acquisition is high, and it is also easier since it’s not deeply integrated into a complex value chain—instead, it’s at the start of one.

Lowering the cost of customer acquisition also can remove barriers to completing a sale. Amazon’s “one-click ordering” is a good example of removing steps in closing a sale, and it has set customer expectations. This is particularly relevant to us, as it’s often said millennials are impatient and they are the future buyers of insurance.

In life insurance, automated underwriting removes the need for intrusive medical tests, which take weeks. If you can move faster than your competition, you’re more likely to win the client, especially today when people expect immediate results. Speeding up claims processing by using images uploaded from mobile phones provides a much better customer experience. It reduces the time it takes to settle a claim, which also lowers cost and fraud. Devices that track customer behavior—for example, driving style or number of steps per day—can result in lower premiums for the customer, because the risks the insurer and individuals are exposed to are lower. Each of these examples is customer centric and data dependent—and infused with AI/ML.

Nudge theory, from behavioral economics, proposes that positive reinforcement and indirect suggestions may influence the behavior and decision-making of groups of individuals. Thinking of customer behavior as something that can be modeled and influenced is a powerful concept, which creates an opportunity to use predictive modeling to identify and better serve profitable customers. This should enhance long-term profitability through the improved marketing and retention that results from better placement of distribution centers and alignment of agent incentives.

These approaches provide concrete value quickly and have solid short-term goals where success is easily measured. However, they do not bring about organizationwide transformation.

The advantage a startup has over an incumbent is that it is a digital native—technological innovation is infused throughout the organization and not limited to pockets of excellence, partners or InsurTech acquisitions. Tesla does not have a data center of excellence; it is one. Tesla was founded in 2003, and in 2020 became the world’s most valuable car company with a $208 billion valuation.9 The company has taken an engineering first view to all areas of its business, from building better batteries (and open sourcing the specifications), to supply chain, automated driving assistance and vehicle safety. Tesla is entering the automotive insurance market to offer lower premiums than traditional insurers.10 Tesla can offer these lower premiums thanks to the huge amounts of driver data it collects and analyzes. Additionally, Tesla benefits from breaking down traditional silos between businesses. It can optimize repair costs to reduce the liability and embed driver safety aids to minimize the risk of an accident. This kind of disruption breaks traditional business models and creates a differentiated advantage that cannot be easily replicated. There is a great deal of benefit to the consumer, as the customer experience is streamlined, both in sales and in claims. And, more important, the aligned incentives mean car journeys are safer.

In times of economic turmoil, as we’re experiencing today with COVID-19 and might experience in the following recession, there will be a knock-on impact. Fewer customers with surplus income could lead to a decline in new business and increased lapses. Are there other areas of focus that could help bring about a broader digital transformation? Could those benefits be more impactful during a downturn?

Back-end Focus

With back-office transformation, it is possible to go beyond the focus on customer experience. Since expenses incurred are significant in the back office, driving down the cost of policy administration is a valuable goal. Robotic process automation (RPA) reduces the time it takes to execute complex processes and improves quality and agility from early insight, which is especially valuable in times of market turmoil. The actuarial engine room is a good place to innovate if you want to unlock intellectual capital, drive out technical debt and reduce expenses. Regulatory burden is increasing, as are the complexity of products, and it’s not an insignificant challenge or investment to reduce the total cost of ownership of an insurance policy. It is also likely that the burden of regulatory change will be felt more severely due to technical debt and accidental complexity.11,12 Having systems that are hard to adapt inhibits agility. This increases the cost of change or likelihood of missed opportunities, especially in an environment of economic or regulatory change, and is yet another example of how a startup could have a significant advantage over an incumbent.

Looking at a life insurer’s modeling function reveals some interesting parallels to the world of the modern startup’s data strategy. Leaning into that to organically build muscle for data science, ML and AI bring another business function into the new frontier. Marrying the digital culture of the frontend of the business with the back office could lead to a revolutionary change after which everyone thinks as a digital native and speaks the same language—and that synergy may provide unexpected business outcomes.

Actuarial Modeling and Data Science

How much does the actuarial modeling team have in common with a modern data science team? What can they learn from each other, and how could commonality create synergy? How might that be exploited?

Software engineering has had a concept of DevOps (combining software development and IT operations to shorten systems development life cycle and increase quality) for several years. MLOps, ModelOps or modern data science is similar and brings data scientists and operations professionals together to manage the production ML life cycle. Figure 1 is an example of the process from Microsoft.

Figure 1: Continuous Integration and Continuous Delivery Pipeline for ML/AI
Figure 1

Source: MLOps with Azure ML. Microsoft (accessed June 23, 2020).

This process is data centric and sits within the context of a data platform that delivers curated input to models. SAS defines a “centralized repository” where “collaborative, cross-functional teams capture and store model information and related metadata in a single location.”13 The actuarial modeling process is similarly data centric, and data is often cited as a major cause of concern for the actuarial modeling teams.

Taking a step back from the technology view and thinking about the theoretical process, you see a scientific process akin to what many actuaries do today: Domain experts run a series of experiments to validate a theoretical model built using ML and pre-existing data.14

Actuarial processes can benefit a great deal from the contemporary concepts of DevOps and ModelOps, with only minimal change. A modern end-to-end architecture, similar to what is illustrated in Figure 2, automates complex processes and also establishes rigor in the development life cycle. This reduces cost of ownership, increases accuracy and removes manual controls that are costly to demonstrate during an audit.

Figure 2: A Modern End-to-End Architecture
Figure 2

Source: Milliman

An opportunity associated with transforming toward engineered processes, common in software engineering and productized ML models, is synergy in investments across the business. One such example is observed from the widespread adoption of agile methods beyond the software engineering teams. It is now quite common to find Scrum-based agile processes in several areas of change in an organization. The foundational view of a process like Scrum is one of scientific rigor—an empirical process model. This model provides control through frequent inspection and adaption of processes that are imperfectly defined and initially generate unpredictable or unrepeatable results. Said differently, the model asserts that knowledge comes from experience.15 Agile methods, like Scrum, have provided a framework for companies to deliver incrementally and realize a return on investment (ROI) sooner. It’s like NASA getting to the moon: one experiment at a time, using data to take a better step forward.

The standardization of tools across an entire organization from the top down is expensive and onerous to roll out. It’s also counterproductive, as everyone has different requirements. Indeed, the actuarial modeling core is very specialized. Thought leaders in modern data warehousing, such as Netflix and Uber, are choosing to leverage open-source technologies and not the existing enterprise solutions. These open-source solutions are available from each of the major cloud providers in the form of a platform as a service. Google gave away huge amounts of value in the form of open-sourced frameworks, like TensorFlow, and pretrained models for common tasks like image and voice recognition. Companies can connect and combine them with their own intellectual property to deliver their unique services to the market very quickly. It’s also interesting to observe the partnerships of cloud providers, such as the partnership between Microsoft and SAS,16 which will bring well-established tools into this open-source cloud era.

As each cloud vendor provides a broad range of technologies to innovate with on a single connected platform, there are very few barriers to getting started. The emerging cloud first strategy of many insurers should mean that technology decisions are democratized, and business units can more easily invest in tools connected to their cloud data lake. As those new use cases contribute more data back to the lake, there is organic growth and increased opportunity to leverage the data across organizational silos.

Bringing It All Together

There is indeed a new frontier. More companies are undertaking scientifically rigorous data processes, which is in the wheelhouse of actuarial teams. There are a lot of benefits to increasing the top line, but there are just as many in improving the bottom line. The challenging times we’re in provide an opportunity to optimize the back office by leveraging the very same approaches that drive impact to the top line. The long-term value is a unified, digitally transformed organization that is less encumbered by technical debt and more agile, multidisciplinary and collaborative.

There is an additional competitive advantage to be gained from building sophisticated models for policyholder behavior or for other actuarial assumptions. This advantage might be leveraged by reinsurers or potential merger and acquisition (M&A) deals as companies look to identify attractive blocks of business for acquisition or to divest unattractive blocks. These same models may be applicable to attracting and retaining customers at the front end. Before you know it, you may have marketing leaning on lapse studies conducted by the actuarial team, or customer retention teams leveraging the actuarial model to assess customer value when making decisions about reducing premiums to retain profitable customers. In short, democratization of both data and models could have a far-reaching organizational benefit.

It is critical to focus on delivering business outcomes and not fixate on the technology or the promise of data. This is also a perfect opportunity to build a “digital native” culture to facilitate transformation across the organization, bringing in actuarial talent instead of keeping it in a silo. Peter Drucker said that “culture eats strategy for breakfast,” and McKinsey & Company research found that “digital transformations require cultural and behavioral changes.”17 The McKinsey & Company research suggests that the key factors for successful digital transformation are leadership, building for the future, empowering people to work differently using collaborative methods and having the right tools. This is a lot for any company to manage, but those companies that address all of these factors in a coherent way are the companies that will shape the future of insurance.

Tom Peplow, MSc, is a principal at Milliman, where he is the director of Product Development for Life Technology Solutions.
Corey Grigg, FSA, CERA, MAAA, is a consultant at Milliman and technical lead overseeing implementation of Milliman’s Life Technology Solutions products.

English Version 2019年9月5日, 北美精算师协会(SOA)在西安举办“风险管理研讨会”。与会嘉宾针对未来中国保险风险管理各抒己见,进行了精彩的分享。以下为现场各位嘉宾的发言要点整理,所有内容仅代表嘉宾个人观点。 主持人: • 林红(友邦保险中国区首席风险官) 嘉宾: • 王晴(农银人寿保险股份有限公司总精算师) • 赵宇平(阳光人寿保险股份有限公司总精算师及首席风险官) • 吴可麒(安盛天平财产保险股份有限公司总裁助理、首席风险官) • 陈妙仪(瑞士再保险亚洲再保险业务首席风险官兼集团寿险及健康险再保险业务风险管理负责人,执行总裁)) 主持人林红: 近年来,中国保险业在风险管理方面取得了长足的进步,各位嘉宾们都是风险管理方面的资深人士,见证或参与了中国保险业风险管理的发展,今天请大家多多分享宝贵经验。 王总是行业里资深的总精算师,也曾任首席风险官,请您谈谈对中国保险业风险管理的总体看法? 嘉宾王晴: 中国保险行业的风险管理虽然启动较晚,但监管部门和各公司管理层已经非常重视风险管理工作。 监管部门在偿付能力、资产负债管理等监管制度上日益规范和完善,在制度健全性和遵循有效性方面提出了详细的要求,建立了完备的报告制度,这些都对行业风险管理起到了有力推动作用。 但我觉得整个行业在风险管理方面还有提升空间,需要不断学习和积累经验。 目前,大部分公司只是计算风险敞口与久期缺口,但是由于国内缺少相关金融工具等原因,目前大部分保险公司还没有采取风险对冲等行动。 同时,因为中国的长久期资产较少,行业的资产久期小于负债久期,再投资风险较高。 从近期的国债利率走势来看,呈现了一定的下降趋势。 我建议保险公司多做敏感性测试,测试利率下降对偿付能力以及资产负债各方面的影响。 主持人林红: 谢谢王总。 刚才王总提到寿险公司面临的利率风险问题,防范利率风险需要从资产、负债两端努力,下面有一个负债端的问题想请教赵总:从产品设计和销售管理角度,我们怎么去理解新出现的精细化的保险消费需求,未来销售队伍的变化趋势,以及新技术、新平台的影响等。 嘉宾赵宇平: 不同渠道、不同特性的客户,他的偏好或者需求是很多样的。 现在的客户变得越来越专业化与年轻化,他们的保险需求、消费习惯和过往几年前是有很大变化的;同时,客户也越来越关注疾病预防、健康管理、养老等方面的广义保险产品。 保险公司需要做出改变去适应客户的这些变化。 从渠道模式来看,未来不同销售渠道之间的割裂是需要被打破的,然后立体地去接触客户,去销售我们的寿险产品。 从组织方式看,将来组织结构会更趋于扁平化,代理人更专业化。 同时,保险公司可以通过更好地和客户进行互动以及采取有特色的差异化的策略去吸引客户,增强粘性,然后通过大数据进一步了解客户需求。 目前,互联网平台对传统保险已经有了比较大的冲击,互联网平台不仅仅能定位成一个销售平台,也可以把数据整合之后做出更定制化的产品,并且更准确地识别不同人的风险。 主持人林红: 谢谢赵总。 正如赵总谈到的,互联网保险的发展会给传统寿险业带来很多影响。 请吴总和我们分享一下传统保险公司在互联网保险发展下有什么样的挑战和机遇,以及财产险公司在健康险领域可以怎样与寿险公司及健康险公司进行差异化竞争。 嘉宾吴可麒: 在挑战方面,互联网保险市场现在已经进入了一个白热化的竞争阶段,几乎是一片红海。 互联网保险公司对技术的要求比较高,只有业务规模较大、长期持续经营的互联网保险公司才可能分摊固定成本、将持续的技术投入转化成获利,转亏为盈。 信息和网络安全以及如何保护客户的个人资料也是互联网保险面对的挑战。 在机遇方面,互联网保险可以降低保险销售的交易成本。 数字科技使得互联网保险公司可以更多地了解客户的信息,提升了风险管控能力,实现更精准定价,也使得销售快速便捷,加深了消费者购买保险的意愿。 针对财产险公司,因为车险市场竞争非常激烈,财产险公司为了生存需要去销售短期健康险,这对其是一个很大的挑战,但同时也给财产险公司带来了机遇。 销售短期健康保险可以优化财产险公司的业务结构,并且可以提供给客户综合全面的产品,推广短期健康保险产品的时候还可以利用其在销售财产险产品时累积的大量客户资源,财产险公司还可以去细化市场,细化风险,针对特定的客户提供专属的定制化产品。 主持人林红: 谢谢吴总。 除了互联网保险的发展,国际性监管标准的变化对于保险行业也有深刻的影响,请陈总以国际化的视角给大家进行分享对于全球监管变化、不同市场特征以及利率走向的看法。 嘉宾陈妙仪: 当欧洲开始受到利率下降的冲击时,那些管理投资组合较好的公司会开始降低它们的利率保证,从高保证利率转向低保证利率,从传统人身保险转向投连险,来让消费者承担更多的投资风险。…

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