OF INTEREST

October 2017

Actuarial Association of Europe

The AAE announced that Thomas Béhar has been elected as the new AAE Chairperson for the year to September 2018. A French national, he was President of the Institut des Actuaires in France from 2006–2010 and 2012–2016. He said: “Representing the voice of 36 actuarial associations and almost 23,000 actuaries in Europe, I am very honoured to be able to lead on the continuation of the long tradition of selfless AAE contributions to the public interest of our European community. With a new strategic plan and a new governance, we are ready to address the fascinating challenges that Europe and our profession face today. We will work in close cooperation with our members, the European institutions and our European stakeholders. Our special focus will remain on the new European regulations and the review of the existing ones. We will also open our work to the wider fields, meaning notably the impact of the Fintech world and the new technology in our European practices.” During last week’s General Assembly Esko Kivisaari (Finland) was elected as Vice-chairperson for the coming year. The newest issue of The European Actuary (Number 15 – October 2017) is now available. Please visit its website of The European Actuary for prior issues or further details.

Becoming an Analytics-Based Insurer: A Road Map
By Stephen Lowe, FCAS, MAAA, CERA

An analytical arms race is disrupting the traditional insurance company business model and changing the imperatives for success. Predictive modeling is steadily expanding, reaching beyond merely being a tool for product strategy to becoming an integral function within a new data and analytics-based business model.

Transformations come with challenges. Insurers are currently at various stages in the evolution towards an analytics-based organization. Those well along the journey are building processes that focus on innovation along each element of the value chain displayed in Exhibit 1. Meanwhile, insurance executives are facing two concurrent implementation challenges. Namely, they must find the talent necessary to transition the traditional business model to the data and analytics driven one while also engaging in company-wide change management

Necessary Skills
Since introducing predictive models to personal auto insurance more than 20 years ago, insurers have gradually expanded their use of predictive models to other insurance lines and applications. The three most common predictive modeling applications are underwriting/risk selection, evaluating fraud potential, and deciding when to order reports (such as credit), according to Willis Towers Watson’s 2015 Predictive Modeling and Big Data Survey.

Additional applications in the ranking include: premium auditing, advertising strategy, claim triage, underwriting expense efficiency, determining litigation potential, agency management/compensation, loss control and agent placement/distribution management. Released in February, the report’s conclusions were based on the responses of 61 North American property/casualty insurers.

Capitalizing on the new technological landscape requires a team that possesses three primary skills sets. The first is data hacking, which in this context does not refer to criminal activity but describes the mindset to develop solution-yielding approaches. Hacking skills include data sourcing knowledge, capabilities in data assembly and management, and experience in scrubbing and extracting information from raw data.

Facility in contemporary analytics tools built on new era math and statistics is the second necessary skill. These include Generalized Linear Models (GLMs), Classification and Regression Tree Analysis (CART), machine learning, data visualization, etc., that permit deeper insights into relationships evidenced within the data. However, access to infinite data and statistical prowess is not enough to build a truly analytics-based insurance company. For that, the third skill is required: contextual knowledge, referred to by some as domain knowledge, which includes full appreciation of insurance risk.

Context is the deep knowledge of the critical nuances and complexity of insurance that assures a focus on relevant data rather than data for its own sake. No one can adequately and effectively analyze a set of data without fully understanding its context – the environment from which it emerged. Context, for example, is necessary for considering how the predictive models should be developed for appropriate decision-making and what will happen if the external environment or the internal incentives of the decision makers change.

Finding analytical professionals with the skills and knowledge required for becoming a truly analytics-based insurer differ from traditional business skills primarily because of three incredibly rapid technological advances. First, the cost of computation and data storage is no longer a significant part of the strategic calculus. Thanks to low-cost cloud servers, insurers can gather, retain and manage massive amounts of data.

Second, data sources are plentiful and growing exponentially as monitoring devices have become ubiquitous. Automobiles will allow insurers to capture location, acceleration and speed a dozen or more times a second and analyze how usage translates into accidents. By deploying drones, home insurers can capture roof condition before and after a storm to settle damage claims.

Third, the tools and applications to assemble, manipulate and analyze data are better than ever and continue to improve. Summarizing and segmenting data is no longer necessary to make analysis manageable. Transactional level data, even in volumes measured in terabytes, works with today’s predictive models.

Technological change has been significantly profound. It has even shifted the focus of statistics away from traditional sampling theory since an entire population can now easily be analyzed. State-of-the-art applications and contemporary programming languages such as R and Python allow insurers to handle very large and complex data sets, perform analytics, create meaningful data visualizations and build quite effective predictive models.

Further, analytic models are also changing, from merely descriptive to predictive and ultimately, to prescriptive. Claim triage applications, for example, are prescriptive because they analyze the attributes of a claim when it is reported and recommend the appropriate adjuster based on their experience and expertise.

To become analytics-based, insurers are aggressively staffing predictive analytics teams and linking them into the business. However, as interest in predictive analytics has spread from a few carriers to the majority of them, the demand for talent is outstripping supply, making talent acquisition and management critical issues.

Talent Shortage
An insurer’s ultimate goal is to benefit from the intersection of the three skill sets as outlined in Exhibit 2, which is a variant of one suggested by Dave Conway, a prominent data scientist.

Since there is currently a shortage of analytical professionals, those responsible for building predictive analytics teams are faced with a Hobbesian choice. Should they hire newly minted data scientists straight out of universities and teach them insurance? Or, should they redeploy actuaries and ask them to round out their contemporary statistics and analytics skills? Both approaches mean that team members require training to overcome a steep learning curve, thus affecting immediate productivity.

Data scientists are one potential talent source. Those being hired by insurers generally are recent graduates, usually with an advanced degree, who have been trained in the first two skill sets. Their advantage is the currency of their education, giving them up-to-date technological knowledge.

However, in conversations with analytic team managers, newly-minted data scientists typically lack understanding – or context -- of how insurance and risk work. They require training in this third skill area before they can be effective and it takes awhile to be well versed in insurance.

As the original architects of the insurance industry’s predictive models, actuaries possess contextual knowledge as well as aptitude in hacking and analytics. From the first predictive model in 1880 to predict life expectancy to current applications, actuaries have been in the predictive modeling game since the profession was created.

To help fill the talent gap that exists today, the Casualty Actuarial Society (CAS), through its new wholly owned subsidiary, The CAS Institute, will offer both data scientists and actuaries a new data and analytics credential to provide an objective skills benchmark. The CAS Institute’s program will not only focus on the three skill areas, but will feature a capstone project requiring candidates to apply what they have learned to develop a solution to a real-world problem. (For more information, please visit TheCASInstitute.org.)

Challenges Beyond Team Building
As the analytics team expands predictive models to more decision areas and quantitative professionals master the three necessary skill sets, insurers pioneering the data driven and analytics approach face other challenges to overcome. Such insurers are finding that big data is important, but it isn’t enough. Despite a wealth of available data, decision makers can often still be starved of true insight. This should change once the appropriate analytical teams are put into place and appropriately trained.

Becoming a data driven and analytics based insurer requires preparation for necessary changes in culture. To create the most value, analytics must be deeply embedded in an organization’s operations so that information and insights are shared across business units and functions. Oftentimes, the hardest aspect of implementation is not generating and sending the signal insights, but assuring the appropriate decision makers can receive and accept them.

Resistance to change is natural. Managers can be surprisingly unreceptive or feel threatened by the insights predictive models can provide. This is especially true if the modeling team is not fully supported by the C-Suite (“tone from the top”) or is perceived as merely part of the “back-office.” The challenge is exacerbated when end-users do not understand the terminology or the predictive models are not well explained.

Part of the push towards being analytic-based is the clear benefits from better insights to support decisions. Research shows that analytical judgment outperforms what behavioral scientists call clinical judgment. The latter is the experience and instinct underwriters develop to determine individual risk selection and make pricing decisions. Claim adjusters also employ their own clinical judgment to make individual claim handling decisions. Analytical judgment is superior to clinical, however, because it is based on all available experiences rather than the experience of one person.

While the benefits are objectively demonstrative, insurers still sometimes encounter clashes between analytical results and clinical judgments. This is not an uncommon problem, as illustrated in the book and movie Moneyball, in which the baseball scouts are unable to accept even the idea that a statistician could make better recommendations on player selection. They correctly perceived the possibility as an existential threat. Underwriters and claim managers must not be put in the same position, lest they react the same way.

Of course it is best to seek a combination of analytical with clinical judgment, as this can be superior to either in isolation.

Conclusion
The availability of big data coupled with technological innovation are disrupting the traditional insurance company business model, moving it to one driven by analytics. Since data scientists and actuaries generally bring different skill sets to an analytics team, they will need to cross-pollinate until individual professionals can offer all three skills necessary for successful analytics: data hacking, modern statistical prowess and intimate insurance knowledge.

While insurers are building analytical teams to complete the three necessary skill sets for the new data and analytics insurance company model, it is just as critical to address barriers to integrating analytics into the company. Effective change management that helps employees embrace the benefits of the analytics model is another necessary element for effective transition from a traditional business model.

Bio:
Stephen Lowe, FCAS, MAAA, CERA is currently serving as president of the Casualty Actuarial Society. He recently stepped down from his role as managing director at Willis Towers Watson to consult with clients and pursue research. He has been advising insurance companies for more than 40 years.

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