Tag Archives: data scientists

How Do Actuarial, Data Skills Converge?

Our survey of leading carriers shows that insurers are increasingly looking to integrate data scientists into their organizations. This is one of the most compelling and natural opportunities within the analytics function.

This document provides a summary of our observations on what insurers’ analytics function will look like in the future, the challenges carriers are currently facing to make this transition and how they can address them.

We base our observations on our experience serving a large portion of U.S. carriers. We supplemented our findings through conversations with executives at a representative sample of these carriers, including life, commercial P&C, health and specialty risk.

We also specifically address the issue of recruitment and retention of data scientists within the confines of the traditional insurance company structure.

The roles of actuaries and data scientists will be very different in 2030 than they are today

Actuaries have traditionally been responsible for defining risk classes and setting premiums. Recently, data scientists have started getting involved in building predictive analytics models for underwriting, in place of traditional intrusive procedures such as blood tests.

By 2030, automated underwriting will become the norm, and new sources of data may be incorporated into underwriting. Mortality prediction will become ever more accurate, leading to more granular (possibly at individual level) premium setting. Data scientists will likely be in charge of assessing mortality risks, while actuaries will be the ones setting premiums, or “putting a price tag on risk” – the very definition of what actuaries do.

Risk and capital management requires extensive knowledge of the insurance business and risks, and the ability to model the company’s products and balance sheet under various economic scenarios and policyholder assumptions. Actuaries’ deep understanding and skills in these areas will make them indispensable.

We do not expect this to change in the future, but by 2030, data scientists will likely play an increased role in setting assumptions underlying the risk and capital models. These assumptions will likely become more granular, based more on real-time data, and more plausible.

Actuaries have traditionally been responsible for performing experience studies and updating assumptions for in-force business. The data used for the experience studies are based on structured data in the admin system. Assumptions are typically set at a high level, varying by a few variables.

By 2030, we expect data scientists to play a leading role, and incorporate non-traditional data source such as call center or wearable devices to analyze and manage the business. Assumptions will be set at a more granular level – instead of a 2% overall lapse rate, new assumptions will identify which 2% of the policies are most likely to lapse.

See also: Wave of Change About to Hit Life Insurers

Actuaries are currently entirely responsible for development and certification of reserves per regulatory and accounting guidelines, and we expect signing off on reserves to remain the remit of actuaries.

Data scientists will likely have an increased role in certain aspects of the reserving process, such as assumptions setting. Some factor-based reserves such as IBNR may also increasingly be established based on data-driven and sophisticated techniques, which data scientists will likely play a role in.

Comparing actuarial and data science skills

Although actuaries and data scientists share many skills, there are distinct differences between their competencies and working approaches.

PwC sees three main ways to accelerate integration and improve combined value

1. Define and implement a combined operating model. Clearly defining where data scientists fit within your organizational structure and how they will interact with actuaries and other key functions will reduce friction with traditional roles, enhance change management and enable clearer delineation of duties. In our view, developing a combined analytics center of excellence is the most effective structure to maximize analytics’ value.

2. Develop a career path and hiring strategy for data scientists. The demand for advanced analytical capabilities currently far eclipses the supply of available data scientists. Having a clearly defined career path is the only way for carriers to attract and retain top data science (and actuarial) talent in an industry that is considered less cutting-edge than many others. Carriers should consider the potential structure of their future workforce, where to locate the analytics function to ensure adequate talent is locally available and how to establish remote working arrangements.

3. Encourage cross-training and cross-pollination of skills. As big data continues to drive change in the industry, actuaries and data scientists will need to step into each others’ shoes to keep pace with analytical demands. Enabling knowledge sharing will reduce dependency on certain key individuals and allow insurers to better pivot toward analytical needs. It is essential that senior leadership make appropriate training and knowledge-sharing resources available to the analytics function.

Options for integrating data scientists

Depending on the type of carrier, there are three main approaches for integrating data scientists into the operating model.

Talent acquisition: Growing data science acumen

Data science talent acquisition strategies are top of mind at the carriers with whom we spoke.

See also: Digital Playbooks for Insurers (Part 3)  

Data science career path challenges

The following can help carriers overcome common data science career path challenges.

Case study: Integration of data science and actuarial skills

PwC integrated data science skills into actuarial in-force analytics for a leading life insurer so the company could gain significant analytical value and generate meaningful insights.

Issue

This insurer had a relatively new variable annuity line without much long-term experience gauging its risk. Uncertainty about excess withdrawals and rise in future surrender rates had major implications for the company’s reserve requirements and strategic product decisions. Traditional actuarial modeling approaches were limited to six to 12 months of confidence at a high level, with only a few variables. They were not adequate for major changes in the economy or policyholder behavior at a more granular level.

Solution

After engaging PwC’s support, in-force analytics expanded to use data science skills such as statistical and simulation modeling to explore possible outcomes across a wide range of economic, strategic and behavioral scenarios at the individual household-level.

Examples of data science solutions include:

  • Applying various machine learning algorithms to 10 years of policyholder data to better identify most predictive
    variables.
  • Using statistical matching techniques to enrich the client data with various external datasets and thereby create an
    accurate household-level view.
  • Developing a simulation model to simulate policyholder behavior in a competitive environment as a sandbox to run scenario analysis over a 30-year period.

Benefit

The enriched data factored in non-traditional information, such as household employment status, expenses, health status and assets. The integrated model that simulated policyholder behavior allowed for more informed estimates of withdrawals, surrenders and annuitizations. Modeling “what if” scenarios helped in reducing the liquidity risk stemming from uncertainty regarding excess withdrawals and increase in surrender rates.

All of these allowed the client to better manage its in-force, reserve requirements and strategic product decisions.

This report was written by Anand Rao, Pia Ramchandani, Shaio-Tien Pan, Rich de Haan, Mark Jones and Graham Hall. You can download the full report here.

The Science (and Art) of Data, Part 2

Given the high need and growing demand for data scientists, there are definitely not enough of them. Accordingly, it is important to consider how an insurer might develop a core talent pool of data scientists. As it is often the case when talent is in short supply, acquiring (i.e., buying) data scientist talent is an expensive but fairly quick option. It may make sense to consider hiring one or two key individuals who could provide the center of gravity for building out a data science group. A number of universities have started offering specialist undergraduate and graduate curricula that are focused on data science, which should help address growing demand in relatively soon. Another interim alternative is to “rent” data scientists through a variety of different means – crowdsourcing (e.g., Kaggle), hiring freelancers, using new technology vendors and their specialists or consulting groups to solve problems and engaging consulting firms that are creating these groups in-house.

The longer term and more enduring solution to the shortage of data scientists is to “build” them from within the organization, starting with individuals who possess at least some of the necessary competencies and who can be trained in the other areas. For example, a business architect who has a computational background and acts as a liaison between business and technology groups can learn at least some of the analytical and visualization techniques that typify data scientists. Similarly, a business intelligence specialist who has sufficient understanding of the company’s business and data environment can learn the analytical techniques that characterize data scientists. However, considering the extensive mathematical and computational skills necessary for analytics work, it arguably would be easier to train an analytics specialist in a particular business domain than to teach statistics and programming to someone who does not have the necessary foundation in these areas.

Another alternative for creating a data science office is to build a team of individuals who have complementary skills and collectively possess the core competencies. These “insight teams” would address high-value business issues within tight time schedules. They initially would form something like a skunk works and rapidly experiment with new techniques and new applications to create practical insights for the organization. Once the team is fully functional and proving its worth to the rest of the organization, then the organization can attempt to replicate it in different parts of the business.

However, the truth is there is no silver bullet to addressing the current shortage of data scientists. For most insurers, the most effective near-term solution realistically lies in optimizing skills and in team-based approaches to start tackling business challenges.  

Designing a data science operating model: Customizing the structure to the organization’s needs

To develop a data science function that operates in close tandem with the business, it is important that its purpose be to help the company achieve specific market goals and objectives. When designing the function, ask yourself these four key strategic questions:

  • Value proposition: How does the company define its competitive edge?  Local customer insight? Innovative product offerings? Distribution mastery? Speed?
  • Firm structure: How diverse are local country/divisional offerings and go-to-market structures, and what shared services are appropriate? Should they be provided centrally or regionally?
  • Capabilities, processes and skills: What capabilities, processes and skills do each region require? What are the company’s inherent strengths in these areas? Where does the company want to be best-in-class, and where does it want to be best-in-cost?
  • Technology platform: What are the company’s technology assets and constraints?

There are three key considerations when designing an enterprisewide data science structure: (a) degree of control necessary for effectively supporting business strategy; (b) prioritization of costs to align them with strategic imperatives; and (c) degree of information maturity of the various markets or divisions in scope.

Determining trade-offs: Cost, decision control and maturity

Every significant process and decision should be evaluated along four parameters: (a) need for central governance, (b) need for standardization, (c) need for creating a center of excellence and (d) need for adopting local practices. The figure below illustrates how to optimize these parameters in the context of cost management, decision control and information maturity.

This model will encourage the creation of a flexible and responsive hub-and-spoke model that centralizes in the hubs key decision science functions that need greater governance and control, and harnesses unique local market strengths in centers of excellence. The model localizes in regional or country-specific spokes functions or outputs that require local market data inputs, but adheres to central models and structures.

Designing a model in a systematic way that considers these enterprise-wide business goals has several tangible benefits. First, it will help to achieve an enterprisewide strategy in a cost-effective, timely and meaningful way. Second, it will maximize the impact of scarce resources and skill sets. Third, it will encourage a well-governed information environment that is consistent and responsive throughout the enterprise. Fourth, it will promote agile decision-making at the local market level, while providing the strength of heavy-duty analytics from the center. Lastly, it will mitigate the expensive risks of duplication and redundancy, inconsistency and inefficiency that can result from disaggregation, delayed decision making and lack of availability of appropriate skill sets and insights.

The Science (and Art) of Data, Part 1

Most insurers are inundated with data and have difficulty figuring out what to do with all of it. The key is not just having more data, more number-crunching analysts and more theoretical models, but instead identifying the right data. The best way to do this is via business-savvy analysts who can ask the right strategic questions and develop smart models that combine insights from raw data, behavioral science and unstructured data (from the web, emails, call center recordings, video footage, social media sites, economic reports and so on). In essence, business intelligence needs to transcend data, structure and process and be not just a precise science but also a well-integrated art.

The practitioners of this art are an emerging (and rare) breed: data scientists. A data scientist has extensive and well-integrated insights into human behavior, finance, economics, technology and, of course, sophisticated analytics. As if finding this combination of skills wasn’t difficult enough, a data scientist also needs to have strong communication skills. First and foremost, he must ask the right questions of people and about things to extract the insights that provide leads for where to dig, and then present the resulting insights in a manner that makes sense to a variety of key business audiences. Accordingly, if an organization can find a good data scientist, then it can gain insights that positively shape its strategy and tactics – and gain them more quickly than less-well-prepared competitors.

What it takes to be an effective data scientist

The following table highlights the five key competencies and related skills of a qualified data scientist.

Competencies

Key Skills

Business Impact

1. Business or Domain Expertise

   Deep understanding of:

  • Industry domain, including macro-economic effects and cycles, and key drivers;
  • All aspects of the business (marketing, sales, distribution, operations, pricing, products, finance, risk, etc.).
  • Help determine which questions need answering to make the most appropriate decisions;
  • Effectively articulate insights to help business leadership answer relevant questions in a timely manner.

2. Statistics

  • Expertise in statistical techniques (e.g., regression analysis, cluster analysis and optimization) and the tools and languages used to run the analysis (e.g., SAS or R);
  • Identification and application of relevant statistical techniques for addressing different problems;
  • Mathematical and strategic interpretation of results.
  • Generate insights in such a way that the businesses can clearly understand the quantifiable value;
  • Enable the business to make clear trade-offs between and among choices, with a reasonable view into the most likely outcomes of each.

3. Programming

  • Background in computer science and comfortable in programming in a variety of languages, including Java, Python, C++ or C#;
  • Ability to determine the appropriate software packages or modules to run, and how easily they can be modified.
  • Build a forward-looking perspective on trends, using constantly evolving new computational techniques to solve increasingly complex business problems (e.g., machine learning, natural language processing, graph/social network analysis, neural nets, and simulation modelling);
  • Ability to discern what can be built, bought or obtained free from open source and determine business implications of each.

4. Database Technology Expertise

  Thorough understanding of:

  • External and internal data sources;
  • Data gathering, storing and retrieval methods (Extract-Transform-Load);
  • Accessing data from external sources (through screen scraping and data transfer protocols);
  • Manipulating large big data stores (like Hadoop, Hive, Mahoot and a wide range of emerging big data technologies).
  • Combine the disparate data sources to generate very unique market, industry and customer insights;
  • Understand emerging latent customer needs and provide inputs for high-impact offerings and services;
  • Develop insightful, meaningful connections with customers based on a deep understanding of their needs and wants.

5. Visualization and Communications Expertise

Comfort with visual art and design to:

  • Turn statistical and computational analysis into user-friendly graphs, charts and animation;
  • Create insightful data visualizations (e.g., motion charts, word maps) that highlight trends that may otherwise go unnoticed;
  • Use visual media to deliver key message (e.g., reports, screens – from mobile screens to laptop/desktop screens to HD large visualization walls, interactive programs and, perhaps soon, augmented reality glasses).
  • Enable those who aren’t professional data analysts to effectively interpret data;
  • Engage with senior management by speaking their language and translating data-driven insights into decisions and actions;
  • Develop powerful, convincing messages for key stakeholders that positively influence their course of action.

While it may seem unrealistic to find a single individual with all the skills we've listed, there are some data scientists who do, in fact, fit the profile. They may not be equally skilled in all areas but often have the ability to round out their skills over time. They typically tend to be in high-tech sectors, where they have had the opportunities to develop these abilities as a matter of necessity.

However, because of the increasing demand for data scientists and their scarcity, insurers (and companies in other industries) should consider if they want to build, rent or buy them. Although buying or renting capabilities can be viable options – and do offer the promise of immediate benefits – we believe that building a data science function is the best long-term approach. Moreover, and as we will address in our next post, in light of the shortage of data scientists, a viable approach is creating a data science office of individuals who collectively possess the core competencies of the ideal data scientist.