Tag Archives: valen

Applied Analytics Are Key for Progress

While most carriers collectively understand that predictive analytics is necessary to remain competitive with other data-driven and VC-backed companies, progress isn’t as fast as it should be. Some are still not using analytics at all despite knowing its importance, while many others limit use to one area of the business. One main obstacle is a disconnect between the C-suite and analytics staff.

The C-suite will not invest in initiatives they cannot measure, manage and understand. Therefore, how a business handles the implementation of analytics is just as important as the predictive models themselves. This is why applied analytics programs are needed to push the insurance industry into the future.

Applied analytics recognizes the value of data in specific business processes and basing key decisions off the insights. Carriers must define their strategy and goals around the initiative, decide on an implementation approach and convey it successfully to the rest of the organization to ensure buy-in and adoption. Executing this can be difficult, often a result of balancing sound technical decisions against each individual organization’s company culture.

Strategy and Goals

The first step a carrier should take when applying predictive analytics is building the strategy and selecting the goals. Beginning with smaller projects is often an excellent way to build confidence for future implementations across different areas of the business. In this phase, carriers decide which specific challenges they hope to tackle using predictive analytics. For example, a carrier’s main goal may be to improve risk selection and pricing, and a secondary goal would be to achieve underwriting consistency as the carrier grows its existing business in new states. By defining what the organization hopes to gain from a particular initiative, it avoids misunderstanding and confusion during the implementation and duration of the project.

See also: 3 Key Steps for Predictive Analytics  

Next, the carrier must choose their success metrics. Using the same example, if the target for a predictive analytics initiative is improving risk selection and pricing, loss ratio improvement is often the best measurement. If business growth is the primary concern, profitability and aligning price-to-risk is an important metric to use. The C-suite is results oriented and this step sets the foundation for the implementation that follows.

Implementation Plan

Once a project has been selected, its goals determined and success metrics defined, an implementation plan should be created to strategize the role governance will play within the corporate culture. The way a carrier must address this step largely depends on the type of predictive analytics initiative in place. If a commercial auto carrier uses analytics to improve pricing with its long-haul trucking business, it’s key to find the balance between the model score and your underwriters’ expertise. Don’t leave it to chance, carriers should develop clear rules for how to use the predictive model that matches each company’s weaknesses and strengths.

By using the predictive model, policies are scored individually and assigned to a specific risk “bin”.

As one example, a carrier may decide that the best performing risks for smaller policies (bins 1-3) are approved for straight through processing, the average risks (bins 4-7) must always be reviewed by underwriters, and the poorer performing segments (bins 8-10) should be avoided altogether. A common and more granular form of implementation is determining how much credit or debit can be added to each policy depending on the model score, before needing management approval. By implementing rules of engagement that correctly fit a specific organization, it will not only boost the effect of the predictive analytics project but make it easier to manage and make necessary adjustments long-term.

Organization Adoption

Even if a carrier has an excellent strategy and model, it means nothing if those who will be using it on a daily basis fail to do so correctly. During the implementation process, all members of the organization must be aligned with project goals and comprehend its importance for the company. This means undergoing training and support by those involved closely with the initiative — whether the model is being built in-house, through a third party vendor or a consultant. There should be complete transparency throughout the process, and room for adjustment based on feedback from the staff. Predictive analytics is an imperative tool in its own right, but just like any tool, it requires a skilled individual to obtain the best results. In fact, Valen research shows the best results are found when combining human judgement and predictive analytics.

The graph shows the lift of a predictive model. The greater the lift, the more effective the model is for the carrier. The blue line represents the loss ratio improvement based on a combination of the underwriter and the model when making decisions on pricing policies. There is clearly a more significant lift here when compared to both the underwriter (green) and predictive model alone (red).

See also: An Opportunity in Resilience Analytics?  

In order to sustain long-term plans and goals, the predictive analytics strategy must converge with the overall corporate strategy. That can’t happen for any real length of time without executives confidently making important decisions using the insights that come from predictive modeling. Only when a carrier and all of its members fully understand those insights and trust in data are they able to become a data-driven organization.

A New Frontier for Venture Capital

With sales of ping pong tables declining and with first-quarter IPO numbers the lowest since 2008, many are wondering whether we are in the midst of another tech bubble and, if so, when it will burst. Despite a record amount of money flowing into venture capital, funding for startups is drying up.

Rather than believing in every single unicorn, VCs need to believe in insurance. As insurance tech investment has skyrocketed ($2.65 billion in 2015) and is expected to increase in 2016, a shift in how money is invested in Silicon Valley is beginning to take place. Investors are jumping into the insurance tech ecosystem, and, as software is increasingly pervasive in the industry, insurance startups are attacking a wide range of different pain points. Given the rapid expansion in available information, artificial intelligence and IoT technologies, innovative carriers and data-rich competitors from outside the industry may be poised to spark transformations in a number of insurance markets. From commercial auto and health aggregators to providers and solutions, the space is rapidly growing.

Why Invest?

For starters, insurance has the two components any VC-backed startup longs for: 1) Poor customer experience issues across the board and 2) an industry that has been necessary since the 1800s. While venture capitalists and entrepreneurs have been investing and building new offerings, this seems odd to others because, from a consumer perspective, the insurance industry is highly regulated and mundane. But insurance is — and will always be — in demand because people and businesses are looking for ways to minimize risk. Let’s not forget that the industry contributes close to 8% GDP in the U.S. and employs more than 2 million people.

Insurance represents a significant part of the S&P 500 index and has seen a ton of M&A growth ($13 billion) in 2015 alone. Even though it is overlooked for a variety of reasons — regulations, low profit margins, lack of innovation, etc. — technology’s influence is providing the industry with changes that are necessary to stay afloat. Now, consumers can shop for better rates and compare prices easily, while agents and underwriters can better manage data and provide more precise quotes.

The workforce is also changing significantly, and the option for working outside of the traditional employer relationship is on the rise. Individuals need insurance and traditional models are ill-suited to accommodate, so it’s no wonder newer models are coming out of the woodwork to capitalize on this need for change. The workforce inside the industry is changing as well, with the average age of an agent at 59 and with one-fourth of the workforce expected to retire by 2018. Companies are targeting millennials in hopes of boosting numbers and more changes.

While VCs have typically avoided regulated industries, the revenue base in insurance is incredibly high, and investors are paying attention. Because of disruption, M&As, the widespread consumer dissatisfaction with dominant carriers and the Affordable Care Act’s new marketplace for individual plans, innovators are jump-starting a revolution.

Time-to-Invest Indicators

The commercial lines insurance market is starting to undergo the same kind data analytics revolution that first occurred in personal auto, which caused market consolidation over the past two decades. From 1970-92, Progressive averaged a 3% annual profit margin on underwriting insurance, whereas its competitors averaged a 7% annual loss. In 1992, Progressive had $1.45 billion in premium — it now holds $16.5 billion in premium. By implementing predictive analytics, Progressive’s pricing sophistication adversely selected competitors, and the company was able to gain considerable market share. Today, the top 10 personal auto carriers represent 71% of the market. Workers’ compensation, which is often considered the leading indicator for momentum across all of commercial lines, already has companies showing signs of a market share shift.


The above graph shows several companies’ growing market share from 2009-14, with notable gains from analytically-driven companies like Berkshire Hathaway and Travelers.

Something else to keep an eye out for is the important connection between the Internet of Things (IoT) and insurance. IoT is influencing just about every industry, and it’s been predicted that, by 2020, IoT will reach a billion dollars. From driverless cars to connected homes, IoT is already hitting insurance. Liberty Mutual just acquired IoT startup Notion to help reduce water leakage and burglaries, while State Farm is already offering discounted rates to homeowners who have a Nest thermostat or smoke detector. Insurance companies are interested in these technologies because they ultimately provide better benefits to the customer.

Another reason to invest in insurance is simply that carriers themselves are investing. Insurers, who arguably have the best view of their own business and the complexities that come with it, are increasingly recognizing technology’s potential to offer real-time monitoring of vehicles, homes and all other areas of risk exposure. The main ways carriers are doing it now is by investing in emerging startups and tech giants — and even creating innovation and VC organizations internally.

From emerging tech trends to market share consolidation, insurance investment is rapidly growing. According to data from CB Insights, the first quarter of the year was the second largest ever for investment in insurance technology with more than 45 deals raising $650 million.

The industry is growing fast — with deals, acquisitions and innovations — and we can expect to see more traction in the next few years.

Are Market Cycles Finally Ending?

The property/casualty industry has been characterized by its market cycles since… well, forever. These cycles are multi-year affairs, where loss ratios rise and fall in step with rising and falling prices. In a hard market, as prices are rising, carriers are opportunistic and try to “make hay while the sun shines” – increasing prices wherever the market will let them. In a soft market, as prices are declining, carriers often face the opposite choice – how low will they let prices go before throwing in the towel and letting a lower-priced competitor take a good account?

Many assume that the market cycles are a result of prices moving in reaction to changes in loss ratio. For example, losses start trending up, so the market reacts with higher prices. But the market overreacts, increasing price too much, which results in very low loss ratios, increased competition and price decreases into a softening market. Lather, rinse, repeat.

But is that what’s really happening?

What’s Driving the Cycles?

Raj Bohra at Willis Re does great work every year looking at market cycles by line of business. In one of his recent studies, a graph of past workers’ compensation market cycles was particularly intriguing.


This is an aggregate view of the work comp industry results. The blue line is accident year loss ratio, 1987 to present. See the volatility? Loss ratio is bouncing up and down between 60% and 100%.

Now look at the red line. This is the price line. We see volatility in price, as well, and this makes sense. But what’s the driver here? Is price reacting to loss ratio, or are movements in loss ratio a result of changes in price?

To find the answer, look at the green line. This is the historic loss rate per dollar of payroll. Surprisingly, this line is totally flat from 1995 to the present. In other words, on an aggregate basis, there has been no fundamental change in loss rate for the past 20 years. All of the cycles in the market are the result of just one thing: price movement.

Unfortunately, it appears we have done this to ourselves.

Breaking the Cycle

As carriers move to more sophisticated pricing using predictive analytics, can we hope for an end to market cycles? Robert Hartwig, economist and president of the Insurance Information Institute, thinks so. “You’re not going to see the vast swings you did 10 or 15 years ago, where one year it’s up 30% and two years later it’s down 20%,” he says. The reason is that “pricing is basically stable…the industry has gotten just more educated about the risk that they’re pricing.”

In other words, Hartwig is telling us that more sophisticated pricing is putting an end to extreme market cycles.

The “what goes up must come down” mentality of market cycles is becoming obsolete. We see now that market cycles are fed by pricing inefficiency, and more carriers are making pricing decisions based on individual risks, rather than reacting to broader market trends. Of course, when we use the terms “sophisticated pricing” and “individual risk,” what we’re really talking about is the effective use of predictive analytics in risk selection and pricing.

Predictive Analytics – Opportunity and Vulnerability in the Cycle

Market cycles aren’t going to ever truly die. There will still be shock industry events, or changes in trends that will drive price changes. In “the old days,” these were the catalysts that got the pendulum to start swinging.

With the move to increased usage of predictive analytics, these events will expose the winners and losers when it comes to pricing sophistication. When carriers know what they insure, they can make the rational pricing decisions at the account level, regardless of the price direction in the larger market. In a hard market, when prices are rising, they accumulate the best new business by (correctly) offering them quotes below the market. In a soft market, when prices are declining, they will shed the worst renewal business to their naïve competitors, which are unwittingly offering up unprofitable quotes.


Surprisingly, for carriers using predictive analytics, market cycles present an opportunity to increase profitability, regardless of cycle direction. For the unfortunate carriers not using predictive analytics, the onset of each new cycle phase presents a new threat to portfolio profitability.

Simply accepting that profitability will wax and wane with market cycles isn’t keeping up with the times. Though the length and intensity may change, markets will continue to cycle. Sophisticated carriers know that these cycles present not a threat to profits, but new opportunities for differentiation. Modern approaches to policy acquisition and retention are much more focused on individual risk pricing and selection that incorporate data analytics. The good news is that these data-driven carriers are much more in control of their own destiny, and less subject to market fluctuations as a result.

Can We Disrupt Ourselves?

Brian Duperreault, CEO of Hamilton Insurance Group, delivered these remarks to the recent Global Insurance Forum, held by the International Insurance Society (IIS) in New York City.

It’s a real pleasure to be with you at what is arguably one of the most important annual events in our industry.

I was just 18 years old when the International Insurance Society had its first global meeting in Austin, Texas. I entered the industry in my 20s and joined the IIS in my 30s.

Since then, I’ve benefitted professionally and personally from the knowledge I’ve gained and the friends I’ve made at these annual meetings.

Today, I’m going to talk about an issue that represents a distinct threat to our industry. I might even go so far as to call it an existential threat.

But, like all threats, it also represents a great opportunity.

In it could lie the seeds of a legacy of meaningful change for each of us charged with leading our industry.

So I’m going to address the question: Can we disrupt ourselves?

I’m going to start by saying a few words about Twitter.

Bear with me. I do have a point to make that’s relevant to insurance. Twitter has one billion registered users so far… about one human out of every seven on Earth.

Only 6% of Twitter users are over the age of 45. More than 300 million active users—most of them under 45—join Twitter each month.

Twitter started as a platform for sharing personal moments. It’s morphed into an information delivery system that plays a major role in distributing news, marketing products and affecting the outcome of political and social developments.

And this instant, real-time communication comes with the restriction that you can only use 140 characters to get your message across.

Twitter’s simple idea completely disrupted the way we communicate. I used Twitter as an example of disruption last week when I spoke at the Young Professionals Global Forum in London. I called that speech “Risk in 140 Characters.”

Since then, the CEO of Twitter has stepped down amid charges that the platform isn’t evolving as quickly as it should, and there’s been a lot of soul searching about how this disruptive form of social media can keep current in this ever-changing, ever-evolving age of disruption.

In spite of Twitter’s challenges, I believe the metaphor is a good one. It’s time to select, analyze and price risk, faster and more efficiently – the equivalent of risk in 140 characters.

The young professionals I spoke to last week are all digital natives. As Don Tapscott, who studies the digital economy, says: They’ve been bathed in bits since they were born.

They embrace technology and use it to navigate their world, their relationships and their work swiftly and creatively.

These digital natives are mobile, wireless and connected with their peers all over the globe.

Meanwhile, in the other corner, I—and most of my friends here in this room—are digital immigrants. We’ve had to make a deliberate and conscious choice to adapt to digital ways of doing what we used to do on paper, over the telephone, or through other physical or, at best, analog, means.

Even though it was our generation who invented the Internet, many of us have the feeling of being strangers in a strange land. Using search engines and apps to navigate life and work doesn’t come naturally to us.

We digital immigrants tend to shun social media or dabble around the edges, still thinking Facebook, Twitter, SnapChat and Instagram are trendy chat rooms where younger people tell everybody what they’re up to a thousand times a day.

But the truth is that social media, which erupted onto the scene as a means of personal contact, has quickly morphed into a powerful engine of collaboration with profound ramifications for business development.

Digital natives know that. And because they know it, and use that knowledge to great effect, they are leaping ahead of the digital immigrants in our generation.

There’s a term for this: digital lapping. And this lapping of one generation by another is the basis for the disruption that’s blowing apart traditional business models. For digital natives, disruption is the new normal.

You know what I’m talking about. How many music stores saw iTunes coming? How many taxi dispatchers saw Uber coming? How many hotel chains saw Airbnb coming?

How many Blackberry execs even saw the iPhone coming? Well, maybe they saw the iPhone coming, but it’s an understatement to say their reaction was too little, too late.

Pick any industry, and you can see the pattern emerging.

The automotive industry is a telling example. Sergio Marchionne, CEO of Fiat Chrysler, recently said he’s “more determined than ever to pursue industry consolidation lest technology disrupters beat the auto industry at its own game.” Marchionne’s warning came after a meeting at Google and Tesla, and after spending almost an hour in a driverless car.

“The agenda needs to be moved,” he said, “or all these technology disrupters will come in and make our life incredibly uncomfortable.”

Clearly, all industries are facing massive disruptions because of technology. With new models of service delivery, new categories of products and restructured value chains, society and the customer expect far more than traditional businesses can offer.

These expectations represent a potentially bleak scenario for the insurance industry, because in many respects we are way behind the curve as far as technology is concerned.

And we are groping in the dark for an effective solution to attract digital natives to the industry.

Digital natives are the much-discussed, much-researched Millennials.

Born in the eighties and nineties, they’re the offspring of the Baby Boomers. They’re sometimes known as Echo Boomers or the App Generation.

Millennials are the most diverse generation we’ve ever had. In the US, 35% are non-white, and researchers who study generational differences say they are the most tolerant generation yet, believing everyone should be part of the community.

We’ve been studying Millennials for quite a while, so we know a lot about them:

  • They want to be team players.
  • They want their careers to have purpose.
  • They want to build new things that matter.
  • They use social media to collaborate. They crowd-source everything from fundraising to business capital.
  • They fight for worthy causes by alerting each other to things that distress them.
  • They don’t see much difference between work and leisure, and don’t see the point of rigid work schedules and being tied to an office.
  • They see hierarchy as an obsolete impediment to team progress. They need to get things done, and waiting for permission doesn’t strike them as sensible.

Now, does that list describe how the typical insurance company operates? I don’t think so.That’s a red flag that we need to pay attention to. Consider this:

  • Almost half of insurance professionals in the U.S. are over the age of 45.
  • 25% of all the people working in our industry will be eligible to retire in just three years.
  • That means that, in just five years, there will be 400,000 open positions in the U.S. alone.

Five years ago, Accenture warned that it’s hard to attract Millennials to a career in insurance. Accenture noted that “the industry’s apprentice structure—with its long learning curve and slow promotions—in no way suits a Millennial’s expectation of getting rapid feedback, or working in a flat organization that offers dynamic career development.” Since then, more alarm bells have been rung.

Recently, a report found that only 5% of high school and college graduates thought a career in insurance was worth looking at. When asked why, they said they thought the industry was dull and conservative and doesn’t offer much of a chance to make a difference.

For someone whose whole career has been dedicated to an industry that promises to protect, that really hurts. At the very least, we’ve done a terrible job in helping people to understand the value in what we do.

With hundreds of thousands approaching retirement in an industry that’s dismissed as boring and static, and with disruption looming on the horizon, I believe we’re staring into the jaws of a crisis.

Millennials are not only our future workforce, they’re our future customer base. And our industry, quite simply, is not prepared to attract the numbers we need, with the skills we need, to take charge of the disruption we know is coming.

The men and women in this room have presided over some of the great developments in our industry: Catastrophe modeling, deregulation and globalization all happened on our watch.

We’re not strangers to bold moves. Innovation isn’t a foreign concept.

But collectively we don’t seem to know how to crack this nut: How do we attract hyper-connected, entrepreneurial digital natives into the generally old-school world that so desperately needs them?

I know there are pockets of energy devoted to finding a solution to this problem.

MyPath has been established by the Institutes and affiliates as an industry-led effort to raise awareness of insurance as a career, and to provide information about the industry as well as job opportunities. Hamilton USA, the US operations of Hamilton Insurance Group, is one of the industry partners participating in MyPath.

And there’s Tomorrow’s Talent Challenge, an awareness campaign established by Valen, which provides predictive analytic and modeling capabilities to the industry.

Valen is so concerned about the lack of interest the digital generation is showing in insurance that it created Tomorrow’s Talent Challenge “as a rallying cry for the insurance industry to band together to sell exciting, innovative careers in insurance to Millennials.”

These are laudable efforts – driven by the same sense of urgency that I’m outlining here.

But they’re not enough.

We need a focused, coordinated strategy embraced by some of the major players in our industry.

We need a collaborative commitment like the one announced a few months ago.

In January, as many of you know, a consortium of eight companies from our sector announced a far-reaching initiative to provide insurance to the underserved. My company is proud to be one of the partner companies.

We referred to the new entity as the Microinsurance Venture Incubator – or MVI. Quite a mouthful.

This morning, we announced that the venture has a much better name.

After inviting more than 100,000 employees in our partner companies to help us name the MVI, we chose Blue Marble Microinsurance. This is a great name. It really captures the spirit of our venture. It reminds us of how connected we all are – ever more so in this digital age.

Blue Marble Microinsurance takes a holistic view of our world, planning to extend protection to a broader portion of the population by providing insurance in a socially responsible and sustainable way.

It offers people on the wrong side of the digital divide the stability and potential for growth that insurance makes possible.

Blue Marble Microinsurance’s company partners know that the ability to manage and finance risk is critical to the development of society – any society, but most urgently to those struggling to gain a stable toehold in their pursuit of education, jobs and a prosperous future.

Research and development enabled by Blue Marble Microinsurance will bring affordable insurance products to the developing world.

Technology is at the base of this global project, using innovative apps to connect consumers and products on a micro level – but what drives it is our industry’s collaboration, our sense of purpose and our focus on the future.

What we learn from Blue Marble Microinsurance could truly shift the insurance paradigm.

Yes, it has the potential to reduce the cost of risk analysis and product distribution and delivery. And, through reverse innovation, the application of that knowledge in the developed world could be one of the most enduring legacies of this project.

I have to admit to a huge sense of satisfaction at watching this concept unfold. It was three years ago – almost to the day – that I addressed the annual IIS meeting in Rio and outlined a plan for a coordinated industry effort focused on microinsurance.

At the time, I said that this wasn’t the sort of project that could be tackled by one company. Many had tried, but none had succeeded.

I’m delighted that Joan Lamm-Tennant is now leading the development of Blue Marble Microinsurance.

Joan poured her heart and soul into taking an idea outlined in Rio in 2012 and making it a reality three years later.

This initiative is a shining, innovative example of what happens when we work together to find creative risk solutions.

So if we can find a way to offer coverage to literally billions in developing markets around the world, I know we can figure out how to redefine our work environments, our human resources policies and our recruiting programs in such a way that digital natives will be beating down the doors to join us.

Last week, I challenged the leaders of tomorrow to take charge of their destiny and find ways to attract Millennials into the insurance industry.

Today, I’m inviting you, as today’s leaders, to work together to develop a strategy for our disruption, leveraging the talent and skills of the digital generation.

As I said last week, insurance should be catnip to a Millennial looking for a purpose-driven career.

Let’s invite these digital natives in, make them feel welcome and give them the benefit of our considerable experience and expertise.

Then, let’s step aside and let them lead the way.

We have one of those rare opportunities to leave a lasting, collective legacy – one that ensures the insurance industry stays relevant and innovative and becomes the No. 1 career choice for any young person who wants to make a difference, be part of a team, keep the world working – for generations and generations to come.

Blue Marble Microinsurance is proof that, when we collaborate, exciting things happen. Let’s take a disruptive step to the future – together.

3 Key Steps for Predictive Analytics

The steady drumbeat about the dire need for data and predictive analytics integration has been there for several years now. Slowly, many carriers have started to wake up to the fact that predictive analytics for underwriting is here to stay. According to Valen Analytics’ 2015 Summit Survey, 45% of insurers who use analytics have started within the past two years, and, of those that don’t currently implement analytics, 56% recognize the urgency and plan to do so within a year. Although it used to be a competitive advantage in the sense that few were using predictive analytics, it can now be viewed as table stakes to protect your business from competitors.

The real competitive advantage, however, now comes from how you implement predictive analytics within your underwriting team and focus its potential on strategic business issues. New competitors and disruptors like Google won’t politely wait around for insurers to innovate. The window to play catch-up with the rest of tech-driven businesses is getting narrower every day, and it’s either do or die for the traditional insurance carrier.

All of this buzz about data and predictive analytics and its importance can be deafening in many ways. The most important starting point continues to center on where to get started. The most pertinent question is: What exactly are you trying to solve?

Using analytics because everyone is doing it will get you nowhere fast. You need to solve important, tangible business problems with data-driven and analytic strategies. Which analytic approach is best, and how is it possible to evaluate the effectiveness? Many insurers grapple with these questions, and it’s high time the issue is addressed head-on with tangible steps that apply to any insurer with any business problem. There are three key steps to follow.

First Step: You need senior-level commitment.

You consume data to gain insights that will solve particular problems and achieve specific objectives. Once you define the problem to solve, make sure that all the relevant stakeholders understand the business goals from the beginning and that you have secured executive commitment/sponsorship.

Next, get agreement up front on the metrics to measure success. Valen’s recent survey showed that loss ratio was the No. 1 one issue for underwriting analytics. Whether it’s loss ratio, pricing competitiveness, premium growth or something else, create a baseline so you can show before and after results with your analytics project.

Remember to start small and build on early wins; don’t boil the ocean right out of the gate. Pick a portion of your policies or a test group of underwriters and run a limited pilot project. That’s the best way to get something started sooner than later, prove you have the right process in place and scale as you see success.

Finally, consider your risk appetite for any particular initiative. What are the assumptions and sensitivities in your predictive model, and how will those affect projected results? Don’t forget to think through how to integrate the model within your existing workflow.

Second Step: Gain organizational buy-in.

It’s important to ask yourself: If you lead, will they follow? Data analytics can only be successful if developed and deployed in the right environment. You have to retool your people so that underwriters don’t feel that data analytics are a threat to their expertise, or actuaries to their tried-and-true pricing models.

Given the choice between leading a large-scale change management initiative and getting a root canal, you may be picking up the phone to call the dentist right now. However, it doesn’t have to be that way. Following a thoughtful and straightforward process that involves all stakeholders early goes a long way. Make sure to prepare the following:

  • A solid business case
  • Plan for cultural adoption
  • Clear, straightforward processes
  • A way to be transparent and share results (both good and bad)
  • Training and tech support
  • Ways to adjust – be open to feedback, evaluate it objectively and make necessary changes.

Third Step: Assess your organization’s capabilities and resources.

A predictive analytics engagement is done in-house or by a consultant or built and hosted by a modeling firm. Regardless of whether the data analytics project will be internally or externally developed, your assessment should be equally rigorous.

Data considerations. Do you have adequate data in-house to build a robust predictive model? If not, which external data sources will help you fill in the gaps?

Modeling best practices. Whether internal or external, do you have a solid approach to data custody, data partitioning, model validation and choosing the right type of model for your specific application?

IT resources. Ensure that scope is accurately defined and know when you will be able to implement the model. If you are swamped by an IT backlog of 18-24-plus months, you will lose competitive ground.

Reporting. If it can be measured, it can be managed. Reporting should include success metrics easily available to all stakeholders, along with real-time insights so that your underwriters can make changes to improve risk selection and pricing decisions.

Boiling this down, what’s critical is that you align a data analytics initiative to a strategic business priority. Once you do that, it will be far easier to garner the time and attention required across the organization. Remember, incorporating predictive analytics isn’t just about technology. Success is heavily dependent on people and process.

Make sure your first steps are doable and measurable; you can’t change an entire organization or even one department overnight. Define a small pilot project, test and learn and create early wins to gain momentum by involving all the relevant stakeholders along the way and find internal champions to share your progress.

Recognize that whether you are building a data analytics solution internally, hiring a solution provider or doing some of both, there are substantial costs involved. Having objective criteria to evaluate your options will help you make the right decisions and arm you with the necessary data to justify the investment down the road.