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Changing Nature of Definition of Risk

As the foothold of innovation across industries grows stronger by the day, insurers are witnessing the advent of tech-based economies, and with them a fundamental shift in the very definition of risk. Every advancement stands to revolutionize how property, businesses and employees will be insured. Consider automated cars and workplace automation tools, such as Amazon warehouse robots, or the emergence of shared ownership business models, like Lyft and AirBnB. Traditional risk calculation models need to evolve to keep up with rapid change.

How shall insurers prepare for this shift? According to Valen Analytics’ 2019 Outlook Report, a key part of the answer lies in the need to weave data and predictive analytics into the fabric of their business strategies. The report, which employs third-party and proprietary data to identify key trends, revealed:

Insurers Are Heavily Relying on Advanced Use of Data and Analytics to Fuel Growth

Valen’s Underwriting Analytics study found that 77% of insurers are incorporating predictive analytics into their underwriting strategy. This marked an increase compared with the steady 60% of insurers during the past three years, demonstrating a clear emphasis by the industry on data-driven decisions.

While many factors have fueled the demand for sophisticated data and analytics solutions, one stands out. Insurers have a growing desire to reap a share of the underserved small commercial market, which represents over $100 billion of direct written premiums. Data analytics tools enable insurers to reduce the number of application questions, verify necessary information and ascertain risk much more quickly and accurately. This is particularly important in creating effective business models that align with the needs of small business owners.

The rise in insurers looking to employ advanced data analytics techniques has also resulted in the growth of data aggregation services and consortiums. With new primary customer data sources emerging, insurers have access to better insights on consumer risk and behavior. This has contributed to insurers’ appreciation of the predictive horsepower that large pools of data offer. In fact, Valen’s proprietary research found that the synthetic variables appended with consortium data are as much as 13 times more predictive than policy-only data. Synthetic variables are built from computations of more than one variable, made possible by leveraging large and diverse datasets.

See also: Understanding New Generations of Data  

Regulation and Innovation Must Go Hand-in-Hand

With a rise in advanced predictive analytics and robotic process automation in insurance, regulators are paying close attention to the industry. To ensure this oversight doesn’t stifle innovation, it is important that insurers build and document their analytics initiatives so they can be explained and understood by regulators. Being collaborative and responsive will help ensure that regulators can discern the small percentage of use cases that need to be reviewed for consumer fairness protection. In doing so, insurers have the opportunity to take the industry to Insurance 2.0 — the next phase in technology adoption and innovation.

Talent and Infrastructure Challenges

While insurers are looking to integrate data and predictive analytics into their business strategies, what will truly determine their success is their ability to hire and nurture the right talent. Unfortunately, the industry continues to suffer from a lack of the talent needed to support fast-paced innovation. Seventy-three percent of insurers surveyed indicate moderate to extreme difficulty in finding data and analytics talent, and the reasons haven’t changed over the years. While geographic location of the job is the primary reason cited by the survey respondents, more and more prospects are either looking for better compensation packages, are simply not interested in an insurance career or opt for opportunities in tech startups or data-driven companies in other fields.

Another roadblock for insurers is their dated IT infrastructures, which cause massive backlogs. While most insurers suffer backlogs of two years or more, others cannot identify how long their IT backlogs are.

See also: Insurance and Fourth Industrial Revolution  

Both of these problems go hand in hand. Clearly, there is a need to foster an innovation mindset, and, to do so, the industry needs a mix of new thinking and engaging work culture. Insurers should follow the footsteps of leading tech companies and cultivate a culture that appeals to high-level talent. By making small changes, such as embracing diversity and a remote workforce, insurers can make themselves attractive to the talent they need. This will build a workforce capable of overcoming IT infrastructural issues.

In short, to maintain a competitive advantage, insurers must not only put data and analytics at the forefront of their businesses, but also make strategic decisions on how best to employ them to enhance all aspects of their businesses, from customer service and information handling to risk calculations and claims processing.

3 Ways to Optimize Predictive Analytics

A few years ago, simply applying predictive analytics to insurers’ underwriting practice was enough to gain a competitive edge against the large portion of the market that was still operating with traditional methods. That ship has sailed with increased adoption of analytics, raising the stakes for companies that once enjoyed a first mover advantage. Currently, 60% of insurers have welcomed predictive analytics into decision-making and underwriting processes, and research continues to show correlation between predictive analytics integration in the property & casualty industry and improvement to top and bottom lines. Companies that view analytics as a necessary commodity for modern underwriting instead of the centerpiece to their decision making will find themselves falling short of their competition. The biggest differences between the winners and losers in analytics today is equal parts ideological and technical.

In its recently published ROI study, Valen Analytics observed 20 insurance companies, representing $1.8 billion in premium, and compared their loss ratios and premium growth against the industry. The study showed that data-driven insurers consistently outperformed the market on both metrics.

  • Between 2012 and 2017, the industry saw its loss ratios improve by 18 points, whereas these 20 carriers averaged improvements that were nearly twice that (loss ratios improved by 35 points).
  • Between 2012 and 2017, industry-wide premium grew 18% on average, while the carriers studied grew by 53%.

For the first time since its inception, the ROI study isolated the impact of applied analytics on insurers with concerning loss ratios: those whose loss ratio were greater than 60%. This group of insurers saw loss ratios improve to market average within 12 months, and then outperform the market with each subsequent year. These results underscore the value of predictive analytics in insurance.

See also: 3-Step Approach to Big Data Analytics  

Below are three best practices that the insurers studied have implemented to draw the most value from their predictive analytics programs:

Empower underwriters

The considerably positive findings of Valen’s study do not imply that predictive analytics should replace traditional underwriters. Instead, research suggests that predictive analytics tools should aid traditional insurance writers. This year’s study found that underwriter performance improves 3x when they combine predictive analytics with expertise. A well-implemented analytics solution helps underwriters leverage powerful data that they wouldn’t be able to otherwise, and underwriters provide the expertise to make the final decision. In other words, an insurance underwriter’s wealth of knowledge and contextual expertise is a largely irreplaceable asset. Underwriters know the critical variances between the price suggested by the analytics model and the historical habits of a policyholder and can incorporate this information into their decisions. Thus, predictive analytics usage augments an underwriter’s decision-making process rather than supplements it.

Streamline the workflow

Predictive analytics enable insurers to accurately align price to risk exposure, helping underwriters price policies within the context of an insurer’s risk appetite, and oftentimes allowing insurers to implement straight-through-processing (STP) for specific types of risk. In doing so, insurers can eliminate the need for underwriters to be heavily involved in certain decisions and allow them to focus on the decisions that will have the greatest impact to a book of business. This, again, leverages the expertise of an underwriter.

Incorporate the right data

Insurers that have incorporated a consortium of anonymized data into their model-building initiatives tend to be better-positioned for growth. This additional information can be crucial to initiatives like expansion across states or business classes, often by identifying risks that might fall in a blind spot of institutional knowledge. In other cases, the incorporation of consortium data will eliminate sample bias in an existing book of business. For instance, an insurer that’s relied heavily on its expertise in knowing how to underwrite low-risk construction accounts in one state to build a data set that determines good risks in a new state will risk overfitting the model, essentially giving it too high a standard. This will leave an insurer vulnerable to underpricing risky accounts without third party data to balance the scales. Consortium data increases the predictive power of models and helped the group in our ROI study of analytically inclined insurers grow premium last year, even as the market declined.

See also: Global Trend Map No. 5: Analytics and AI  

For the third consecutive year, Valen’s ROI study has identified just how much value applied analytics can add to insurers. The carriers that have leveraged analytics and consortium data and empowered their underwriters have realized significant advantages over competitors to improve both profitability and growth.

Strategies to Combat Barriers to Insurtech

The insurance industry may be in the middle of a technological revolution, but Valen’s recent 2018 Outlook Report: An Industry Divided suggests that barriers to insurtech adoption and engagement remain.

79% of surveyed insurers believe new functionality and features will make their teams more efficient in the long run, aligning with much of the optimism around insurtech acceleration. EY reinforced this notion in its Fintech Adoption Index 2017, explaining that the surge of adoption is positioned to hit the mainstream, driven largely by consumer demand. In fact, customer expectations for technologically advanced solutions for insurance providers have surpassed those of financial planning or investment platform providers.

This reflects positively on the industry for fostering innovation and making smart, data-driven decisions. It should also encourage more insurtech investment, but Valen’s study finds there are many hurdles to overcome.

See also: Insurtech Is Ignoring 2/3 of Opportunity  

Here are four steps insurers should consider when they’ve made the decision to modernize a workflow or process through insurtech to get beyond these barriers to create a replicable approach to successful rollouts:

Identifying the goal

One of the biggest reasons why insurtech solutions fail is the wrong approach to thinking about technology. Insurers should begin with the end-goal in mind, identifying the actual business needs they are looking to address before considering which technologies are available to them. This step will help to secure buy-in from employees and streamline the rest of the process for putting new technologies in place. For leaders at insurance companies, examples might be “lowering claims,” “identifying/limiting the number of policies with high risks in a book or business” or “improving client retention.”

Getting the team on board

Once a business need is identified, it’s important to secure buy-in from the teams that will be most affected by new technology. For example, when data analytics first began to permeate the insurance landscape, many underwriters resisted the change. The sentiment of “machines taking people’s jobs” was prevalent, but the reality is the opposite. Predictive analytics enables underwriters to have a larger impact on an insurer’s overall business.

The challenge doesn’t end with underwriters. 55% of front-line employees are observed to be somewhat or highly resistant to new technologies. Major barriers can include a lack of proof-of-value, understanding of the new functionality, leadership in rolling out the innovation, time for training sessions and user acceptance. These can culminate in resistance to adoption from employees and, ultimately, derail deployments.

By taking each of these barriers into consideration, insurers can ease on-boarding and create strategies to simplify the learning curve and ensure adoption of the new functionality.


Once a new technology’s value is clearly established and the need is confirmed by key stakeholders, the implementation process must be carefully planned. Insurers should choose a leader to oversee the roll-out process, as well as the point of contact for all questions and concerns.

See also: 10 Trends at Heart of Insurtech Revolution  

With regard to training, this leader should create and execute a roll-out plan to foster user acceptance by having the training sessions be fun and interactive. This can be achieved through a carefully crafted presentation, having a charismatic training leader or incentives such as gift cards, free lunch and other perks.

Addressing objections

Even once implementation is complete, there may still be objections and employees who are hesitant throughout the organization. Overcoming this is a result of communication, highlighting the successes of a technology implementation.

Naturally, the ability to communicate success goes back to identifying a specific business goal. With each success, it becomes easier to implement other new technologies. This is where insurers can really thrive from an insurtech perspective, creating an continuing cycle of new technological additions that increase efficiency and drive profitability.

3 Ways to Measure Models’ Effectiveness

Most insurers are using some form of predictive modeling, but it can be difficult to know if it will remain effective over time. Evaluating a predictive model can be tricky because, while there are many ways data can be measured, there is no accepted standard. With the considerable investment that’s involved in predictive analytics, the C-suite understandably wants to hold certain yardsticks to the models and see if they are performing well, and to make sure every stakeholder is using it correctly. Having a forward-looking evaluation can make all the difference when making key decisions, especially if there is trust in the measuring mechanism.

Below are three new ways that insurers can evaluate the impact of predictive models, based on a model currently in production for a regional workers’ compensation insurer. The graphs below provide real-time insights that can help predictive modeling avoid becoming a black box, meaning that you can only see the output of the predictive model, not the input or how that output came to exist. The first two graphs separate out 10 equal portions of either premium or policy count, with each portion referred to as a “bin.”

1. Monitoring that a model is still current and accurate

You need to be able to regularly check if the model you have in production is still up-to-date and providing accurate scores. This graph illustrates the overall model lift on the book for a regional workers’ comp insurer in 2015 and 2016. The insurer’s model is generating a low score on business that’s running very profitably — the lower-risk bins 1, 2, 3 are approximately 30% better than average. Policies getting a score in the higher-risk bins 8, 9, 10 are all running at twice the average loss ratio. This provides a clear indication of what to target and what to avoid.

Bottom line: This model is still current and accurate.

See also: Top 6 Myths About Predictive Modeling  

2. Tracking the impact of a model on decision-making

To realize the benefits of analytics, your staff needs to leverage the insights to make more informed decisions that create improved results. This is a graph of “decision data” from Valen’s InsureRight Manage application. Orange represents policies that were declined, red is quoted and lost, green is quoted and bound and yellow represents non-renewals. It’s evident that declinations are low on the good business — less than 10% — and high on the other end, approaching 50% for bin 10. The insurer is not renewing policies in bins 9 and 10 and, most importantly, retaining more than 50% of business in bins 1, 2, 3.

Bottom line: Underwriters at this insurer are using the model to make more profitable risk selection and pricing decisions.

3. Measuring if the overall risk quality of a portfolio is improving with a model in production.

If you’ve established that your model is accurate and your people are using it, the next question is what kind of impact it’s making to the quality of your portfolio. Are we lowering the risk of our book of business? This view shows the insurer’s risk-selection trends, with an overview of how risk-selection decisions have been influenced by a model and the resulting change to the portfolio. The blue bars represent premium volume by month, and the orange line represents average risk score (i.e., loss ratio prediction) by month. Though there is some variability from month to month, the overall downward trend indicates improvement over the course of the year. There is a small uptick in December 2016, which provides an indication that further analysis is needed.

Bottom line: The risk quality of this portfolio is improving, though still requires careful monitoring.

See also: Survey: Predictive Modeling Lifts Profits  

Not only is it crucial to measure before an implementation takes place, it’s vital to do so both during and after, as well. Predictive modeling only works well if it is aligned with stated business goals, and knowing how to measure that is key to an insurer’s bottom line. With these three new ways to measure, insurers now will have different yardsticks to see whether it is successful and if they are using the actionable insights.

3 Reasons Millennials Should Join Industry

With more than 70,000 expected U.S. retirees in 2017, the insurance industry faces an imminent talent crisis. Industry leaders have been eagerly searching for ways to recruit and attract young talent to replace the outgoing staff, but, due to poor industry perception, it remains an uphill battle.

The Insurance Careers Movement began as a grassroots, industry-wide initiative to combat the coming talent shortage and the ill-fated perception of the industry. We endeavor to empower young professionals who already work in insurance to share their feedback and experiences, educating their peers and students about the vast career opportunities available to them. As a part of the annual Insurance Careers Month each February, we conducted interviews with more than 30 millennials from a wide range of insurance carriers and agencies about their thoughts on the industry.

Contrary to the general perception outsiders have of insurance, findings from the interviews revealed that many younger workers view insurance as a dynamic field with significant opportunities for growth and development of personal relationships with customers and coworkers. In fact, their responses largely resemble the theme of the movement, referring to insurance as, “the career trifecta,” to emphasize the idea that pursuing a profession in insurance is stable, rewarding and limitless.

Here are the three recurring themes mentioned across all the interviews:

1.It’s Stable

In many of the interviews, one of the distinct benefits of working in insurance is the extensive career options, and the flexibility to try different sections of companies. A recent graduate can begin with underwriting, then branch into marketing, risk management or any other career path she wishes to pursue.

See also: 10 Commandments for Young Professionals  

The insurance industry holds a long, rich history and is in nearly every part of the world. Therefore, there is a vast number of opportunities available in many areas of the field, adding to the stability factor. Ashley Jenkins, controller at Pioneer State Mutual Insurance, said, “Insurance companies are very stable compared with many other industries. As an example, my current insurance employer and prior insurance employer did not have to lay off any employees during the major financial crises in 2008 and 2011.” Additionally, according to the National Insurance Brokers Association, the median salaries in insurance are all well above the national average at around $30,000 a year. With more than 75,000 jobs opening up due to retirement, members of younger generations are being afforded regular growth opportunities, promoting a stable career path that doesn’t exist in many fields; today’s young employee tends to change jobs four times before they’re age 32.

2.It’s Rewarding

Although many jobs require employees to sit in cubicles, a career in insurance allows people to interact and cultivate relationships with other customers and coworkers. Koory Esquibel, TRAC risk analyst at Marsh, said, “One of my favorite parts about this industry, and the reason why it is so easy to recommend to students and new graduates, is the ability to form so many strong relationships with colleagues and business partners.” This is a pivotal time in insurance where improved employee and customer interaction is happening at all points of the workflow. Between mobile technologies to better interact with customers and analytics to improve speeds of underwriting and claims processes, the industry has never prioritized innovation so aggressively.

According to the survey conducted as a part of the Insurance Careers Movement, more than 92% of millennials working in insurance said that they are proud to work within the industry and want to promote the benefits and opportunities it provides. Their answers also revealed that millennials are already putting efforts into recruiting their peers, as 73% of respondents said that they have tried to convince at least one of their friends to choose a career in the risk management and insurance industry.

3.It’s Limitless

With the wave of digital innovation looming and new regulations and product offerings being created daily, the insurance industry is more dynamic than ever before. Employees at all levels, regardless of their areas of focus, are challenged to come up with creative solutions to tackle emerging problems. Yasmin Ahmed at Marsh said that she was “drawn to work in insurance because of the career mobility and succession planning.  Seasoned insurance professionals plan to retire within the next five years, providing more career advancement for young people.”

In fact, according to the Jacobson Group and Ward Group Insurance Labor Outlook Study, the insurance industry has added 100,000 new jobs in the past five years, and 66% of insurers expect to increase staff this year. The number of opportunities and intellectual challenge are perfect for millennials as, according to the My Path survey, new graduates are more interested in career advancement possibilities (25%) and learning opportunities (20%) when considering a job than older generations. Therefore, young professionals consider development within their careers more important than salary or benefits.

While the industry remained largely stagnant for years, the technological disruption is closing the experience gap and opening important roles for those interested in data science careers combined with creativity. In fact, Accenture’s fintech report found that investments in insurtech more than tripled from $800 million in 2014 to more than $2.6 billion in 2015. In addition to the heavy focus and investments in IT, startups like Lemonade are joining the industry with new technology-based business models. The entrance of startups has already brought recent changes as it motivated insurance giants to expand their focus. Some of the world’s largest insurers, such as Aviva, Axa and XL Catlin, announced their efforts to establish in-house venture capital funds and stated that they will be dedicating more than $1 billion investments in startups to spearhead digital transformation. This focus on new technology also creates more opportunities for the younger generation, as they can make contributions to their team regardless of the job titles.

See also: Can We Disrupt Ourselves?  

Most say that millennials are known for job-hopping. However, according to a recent Census study, once they’re satisfied, most will stay at the place of employment for three to six years. The bottom line is that carriers must not blame the generation for their lack of interest in insurance and instead work on raising awareness about the value the industry offers. Right now, a lack of talent is one of the biggest challenges for innovation growth. Insurers will have to make concerted efforts to follow through the recruitment process and provide robust training program to attract and retain young professionals. Through recognizing the underlying cause of the crisis and making an industry-wide endeavor, the insurance industry will be able to grow as a whole and successfully combat the talent shortage.