Tag Archives: automation

New Analytics for Small Commercial

Analytics can be a great equalizer in every industry. It’s why 90% of respondents to a McKinsey survey call their analytics investment “medium to high” and another 30% referred to the investment as “very significant” proof that the surveyed understand the value that analytics possesses.

Those investors—especially the commercial insurers—understand the value of analytics and get their money’s worth. In addition to improving sales targets and reducing churn, analytics can increase profitability when it comes to underwriting and selecting risk.

Still, the full potential of analytics goes beyond the insights it provides insurers. When merged with modern technology, data and analytics can fuel efficiency, accuracy and productivity. When used within the decision engine to drive automation, for example, data and analytics can help insurers expedite processes and improve customer experiences, even without human intervention.

Automated reports and actions provide insurers new ways to optimize their day-to-day operations. However, the marriage of automation and analytics is especially vital for the small commercial market as they contend with higher volumes of policy quoting and writing. Using predictive models, automation can reduce the amount of human effort it takes to sell and service policies for small businesses.

Analytics and automation present opportunities to optimize every facet of growing market share for small commercial insurers if properly applied. The sooner that insurers embrace the two, the better off they—and their customers—will be.

Analytics and Automation Can Deliver

When it comes to risk assessment for small businesses, insurers are usually hampered with limited or even misleading information. Unfortunately, this can result in a gap between a risk-appropriate rate and the quoted premium. Thanks to automation and analytics, however, that sort of disparity can be a thing of the past.

See also: What Predictive Analytics Is Reshaping  

While there are many ways analytics and automation can be used to improve the small commercial insurance industry, there are three particular areas where major improvements have been demonstrated. For insurers that are on the fence about committing to analytics and automation, here’s where their influences will likely be most visible:

1. Simplified Applications

By automating customer quoting and underwriting, insurers can phase out the process of collecting troves of information on an application. With reliable third-party data sources, automation can fill in many of the blanks present on typical applications. Insurers will then only need to ask for what’s relevant for the predictive model to assess the risk and provide direction on pricing.

In the same vein, the automation of processes and decisions empowers insurers to use straight-through processing for new applications—quoting and binding policies entirely through an e-commerce experience, without involving staff or consuming staff time. Typically, this is a far more streamlined process for both the insured and insurer, and delivers improved customer experiences.

2. Expedited Claims Processing

Small businesses are acutely sensitive to how long it takes insurers to pay claims and how good (or bad) their experiences are. Analytics helps insurers triage claims while suggesting different processing options.

According to a LexisNexis study, the availability of this data helps shorten processing cycle times by up to 15%. For example, through IoT (internet of things) devices, an insurer can detect water heater leaks and other high-risk problems in real time, enabling the insurer to anticipate potential claims and possibly even prevent them.

Of course, being fast is only part of the equation—the process must also be accurate. Thankfully, automation and analytics improve processes by catching overlooked data points. When sophisticated analytics are applied against a large sample of detailed claims data, the resulting insights can, for example, highlight the best way to get an injured employee back on his or her feet and offer a customized plan to do so.

See also: What’s Beyond Robotic Process Automation  

3. Improved Risk Identification

By using reliable third-party data, such as information available through a data consortium, insurers can more quickly and accurately identify risk—even if it’s in a sector where they have little or no experience—and ensure that risk-appropriate pricing is quoted. Analytics thus becomes a valuable growth engine for insurers to confidently expand into different business lines and regions.  In an environment where 40% of the smallest organizations have no business insurance whatsoever, insurers that embrace modern technology could reap significant rewards. By combining analytics with automation, the small business insurance market could be transformed—which would be welcome news for both insurers and their customers.

The Future of Work: Collaborative Robots

Recent developments in robotics and artificial intelligence have changed the playing field for automated technologies. (Here is an earlier blog on the topic.) Historically, automation was beyond the reach of small and medium-sized companies. Robotics were costly, required highly sophisticated programming expertise, took months to integrate and could only perform single, discrete tasks.

In 2012, the advent of artificial intelligence (AI) was a game changer. AI brought collaborative robots to the market — robots that see and feel like humans, learn (including integrating new data sets and information) and perform multiple tasks. These collaborative robots are also more cost-effective and easier to integrate, making them available and attractive to small and medium-sized businesses.

AI and robotics are now transforming many traditional labor-intensive industries, such as farming, construction, factories and fast food. While Amazon continues to be a global leader in leveraging AI and smart robots, there are plenty of examples of smaller businesses across the country embracing these new automated technologies.

Agricultural farms are using automated tractors and drones to help with growing their crops. Construction firms are purchasing automated brick-laying machines (to lay 3,500 bricks per day). Restaurant owners are investing in new automated machines that can store, prep and cook fast food in a highly controlled environment without any human intervention.

If the adoption of these new automated machines continues, there will be fewer jobs and payrolls in these industries. Over time, the job and payroll loss will affect insurance carriers that specialize in writing workers compensation insurance for these industries.

Historically, technology’s disruption was limited to blue-collar workers; however, AI technology now has its sights set on white-collar workers, including insurance underwriters, claims executives and legal professionals. The insurance industry, which has not been easy to disrupt, is primed for transformations due to developments in AI and automation.

Two years ago, Cambridge University predicted that insurance underwriters were vulnerable to automation. Since that time, we have seen a greater demand among U.S. carriers to invest in new AI technologies that allow them to automate the underwriting and settlement of claims for small commercial insureds. Given the shortage of new talent available to fill expected insurance and claims executives retirements, coupled with new AI technologies, we expect this trend to accelerate.

See also: Measuring Success in Workers’ Comp  

Developments in AI and automation are already changing the U.S. legal profession, one of the most regulated and specialized professions in the U.S. — McKinsey estimates that 22% of lawyers’ and 35% of paralegal tasks can be automated today. A recent HBO documentary, “The Future of Work,” supports this prediction. It highlighted how LawGeex, a new AI-driven computer software, performed against skilled corporate lawyers on a common task — analyzing complex legal documents. LawGeex proved its ability to review and interpret the documents, identify potential legal issues and provide substantive advice to a client in half the time — and with much greater accuracy — than the corporate lawyer.

While LawGeex and other AI technologies will not displace lawyers in the short term, it will exert pressure on lawyers to shift their time to more highly skilled work – such as negotiating and deal structuring – and away from research, writing and reviewing documents. The result could significantly change law firm practices and economics.

Have you considered how robots, AI and automation will change the workplaces of your insureds – and your own company? Stay tuned for my next blog, “Navigating the Fourth Industrial Revolution,” for ideas on how to navigate AI and developing technologies.

Automation Lets Compassion Scale

The modern workers’ compensation system was devised to provide a guarantee of care and medical treatment to injured workers from their employers. Organizations also are expected to offer guidance on the resources available to injured workers as well as how to navigate issues such as finding the right doctor and taking time off work. In short, organizations are expected to be compassionate toward their injured workers and get them back to a productive and motivated state as soon as possible. The reality, however, is that we’ve shifted very far away from these principles.

Today, claims teams are overwhelmed by the number of cases they are expected to handle, which often hamstrings their ability to service injured workers the way they would like. Additionally, skepticism has crept into the claims process, establishing a more adversarial relationship between organizations and injured workers. Feeling neglected or disrespected, many injured workers then turn to litigation for a remedy. According to a report by the Workers Compensation Research Institute (WCRI), some states now see attorney involvement in more than 50% of workers’ comp claims, which can add significant costs and duration to claims. This is a broken system for everyone — workers, organizations and the claims agents caught in between.

So, how do we get out of the present situation?

The Human Element

The claims process has eroded because of a lack of compassion and awareness. Claims are filed by real human beings — often at their most vulnerable. These are people who are physically hurt, so they are already in pain. They also face the prospect of being away from a job they need to pay their bills and are uncertain of when or how much they will be paid while out of work. Tack on the fear, whether warranted or not, that someone will replace them if they are gone from their position too long, as well as the complications of the claims process. If they don’t hear back from the claims agent handling their case or if bills are taking too long to process, injured workers will turn elsewhere.

At the same time, no one goes into claims adjustment (or at least they shouldn’t) thinking, “I want to make this person’s life as difficult as I can. I want to prove they are trying to milk the system and, therefore, will hold up their claim as long as possible.” Absolutely not. The average claims adjuster likely is dealing with a massive caseload as well as imposed processes and questions that are not geared toward moving claims forward.

That is not to say that corporations are evil. They want their workers back on the job as quickly and safely as possible.

See also: Untapped Potential of Artificial Intelligence  

To remove the distrust and frustration experienced by each constituent in the claims process, recognition of humanity must be present, and compassion needs to be injected. You may be thinking something along the lines of: It’s easy to handle a claim with compassion on an isolated basis. A handful of claims in an agent’s caseload may capture more personal attention, but there is no way to address every claim with such care. I would argue that, even as the number of claims rise, with the next generation of tools it is now possible to provide compassionate care at scale.

The Role of Artificial Intelligence

As odd as it may sound, the way to insert more humanity into claims is by using machines. New technologies, such as machine learning and artificial intelligence (AI), can transform the system.

The end goal of AI is to create the best experience, efficiently and at scale. To do so, AI must be given a precise purpose. In the case of claims, AI can be charged with removing very specific hurdles that get in the way of care.

AI is not about replacing humans with robots. In this case, it is about removing the robot from humans. By automating the mundane pieces of claims management, AI frees agents to address emotional needs. In doing so, AI opens the door for a new model of “scalable compassion.”

Scalable Compassion

Until now, it’s been impossible for teams to deliver compassion at scale without breaking the bank. If AI automates significant portions of claims processing — whether it’s finding the right physician, helping to calculate an MSA, or signaling claims that raise red flags — adjustors can dive much deeper into the details that matter. They can weigh various factors based on data and predictive models to provide better answers to questions and make more informed decisions on a case-by-case basis. This results in a much smoother, happier experience for both injured workers and claims representatives who can spend time engaging with people and using their minds in positive ways.

The scalable compassion model works for businesses, too. If you provide the right experience to claimants, the economics will follow. With strategic use of AI-based technology, claims representatives can help get injured workers to the best doctors right from the beginning. When injured workers get in to see a doctor ranked in the top 50%, companies see a 26% reduction in the overall cost of the claim, even if the upfront costs appear higher. This is because the best doctors expedite recovery. Getting it right from the beginning limits the need for additional procedures and continuing doctor or physical therapy appointments.

Employees who receive the right care early in the life of their claim also return to work faster, minimizing problems for both the organization and the worker. And the increased personal attention and seamless delivery of care dramatically reduce litigation costs — which can account for $35,000 to $50,000 per claim if an attorney gets involved. Scalable compassion ultimately may save companies thousands to millions of dollars each year, while improving relations with and loyalty from workers who feel cared for in their time of need.

See also: The Best Workers’ Comp Claims Teams  

Today, workers’ comp sits at a critical junction. Something must be done to reform the system before it collapses. By implementing new intelligent technologies while embracing the role of advocate instead of adversary, a model of “scalable compassion” makes it possible to finally deliver on the intent and vision for workers’ comp. It represents a bold step forward but one well worth taking.

As first published in Claims Journal.

Power of Accelerated Underwriting

The use of accelerated underwriting processes has come a long way in the last five years. Although it was an innovative idea not long ago, most insurers now engage in accelerated underwriting to some degree and are increasingly looking for novel ways to remove inconvenience, delay and cost from the new business process.

However, as impressive as its uptake has been, the industry has yet to begin tapping the true – and transformative – potential of accelerated underwriting. This is because most of the time it has lacked the automation component. Automation has the potential to benefit insurers across their entire business, but this is especially true of accelerated underwriting, which at its heart is about streamlining and speeding policy issuance for simpler, lower-risk cases.

It’s here that automation can really shine. Take for instance a case where an accelerated underwriting process removes the need for an in-person examination. Without automation, that review by an underwriter will still take 24-48 hours to be completed, even though the relevant information is instantly available in seconds. With automation, the entire application and underwriting process can be reduced to a matter of minutes.

This is precisely what new technology platforms are enabling. Advances in AI and machine-learning have come to the point whereby technology can consistently and efficiently underwrite a large proportion of cases. Technology can respond intelligently to input, in real-time, determining what additional information is needed, and then make an underwriting decision and issue insurance coverage. What’s more, when appropriately linked with and integrated into the rest of the business, it can feed information back for better risk modelling in the future.

See also: 3 Ways to Optimize Predictive Analytics  

When you add in a layer of predictive analytics to the automation, things start to get really interesting. Predictive analytics can add value to the risk selection process and our understanding of the risk in a number of ways. The first, mentioned above, is a ‘bottom-up’ benefit – i.e. cases where the analytics engine can spot relationships in the underlying data that are then brought to the attention of underwriters, who can then investigate whether and how that pattern relates to real-world factors.

Another way predictive analytics can add value runs in the other direction – top-down. It means business managers and underwriters have access to a vast pool of analyzable data that they can use to help answer questions and test hypotheses or ideas. Having this ability can remove a lot of unnecessary trial-and-error, and can give all levels of the business a better view of information that is vital to long-term success.

A third and very significant value-add from predictive analytics is that it can help with the systemic stratification of ‘grey areas’ within the underwriting process– that is, the cases in the middle that aren’t either extremely healthy, or obviously high-risk. Segmenting this grey area and formulating better approaches to these cases is crucial for any insurer looking to reduce “RTUs” (Refer to Underwriter) and gain a market edge, and a sophisticated analytics engine can make the process a lot more efficient and smarter.

It is these elements – automation combined with predictive analytics – that could turn accelerated underwriting from a useful cost and time saver into something that could truly revolutionize the insurance business model as a whole. So far, insurers have predominantly used accelerated underwriting to target the same customers they’ve historically targeted. What automated underwriting and predictive analytics can unlock is the ability to actually grow the pie – to target new or previously untapped markets, and create a wider variety of more specialized products focused on particular customer niches. The time and cost savings associated with using this technology could enable different business models for distribution and make it more attractive to target markets that were previously viewed as uneconomical. This process is particularly well-suited to digital distribution, and to making headway into the underserved middle market.

Connected to this – and under-utilized at present – is the way in which automated systems are able to integrate new data sets quickly and holistically. Data has always been, in one way or another, the lifeblood of insurance. But in the modern digital age with its corresponding explosion in the amount of data available, a lot of potentially relevant data sets go untapped by the industry. The ability to access this data and integrate it into risk selection processes will be a big determiner of success for insurers in the near future – those that don’t succeed could get left behind.

Of course, opportunity and challenge are two sides of the same coin. The addition of automation and analytics to accelerated underwriting holds tremendous potential, but also poses a big challenge. Insurance isn’t renowned for being a particularly tech-savvy industry. Yet, to make full use of these new capabilities, firms are going to have to embrace technology and data science.

Most companies will need to work with partners to help the transition. Not just a software vendor to access the technology itself, but there will be a need to find the necessary expertise in both the technology and the insurance sector as whole in order to facilitate a business process revamp. Collaboration will be key in supporting the integration effort needed to fully realize new technology’s potential within a business. But automation does not mean a total overhaul of existing business structures and processes – because this too is likely to incur more risk than opportunity. Firms should take a modular and flexible approach, using systems that can sit within a variety of existing infrastructures with minimal disruption.

See also: How Underwriting Is Being Transformed  

The shift to automation and analytics is coming fast. And when it does, the implications for insurance business models – what’s possible and what’s not – could be just as profound as e-commerce was for the retail sector. For those that get ahead, the rewards could be just as great.

How to Improve Event Response Workflow

This is the third in a series. The first two articles can be found here and here

When catastrophes strike, you have no time. You’re under pressure to quickly understand the financial impact of an event and provide estimates to management. At the same time, you (and your team) are constantly tracking the event, processing hazard data, making sure exposure data is accurate, pulling reports and (let’s hope) beginning outreach to insureds. The last item—customer outreach—may suffer, though, when the other to-dos consume your time and resources.

Speed and quality of response following catastrophes can be an asset to your organization—and a key reason why your customers choose you over your competitors—but only if you can make your event response operations run like clockwork. This entails moving away from the status quo and integrating elements of automation into your event response processes. Let’s take a look at some of the challenges you may face and how to implement a more proactive approach for minimal cost and disruption.

Hurricanes, in particular, illustrate the problem of quickly deriving insight from data. For example, does the following scenario sound familiar?

Imagine a hurricane strikes…

…and it’s affecting Texas, Florida or the Carolinas (probably not too hard to imagine, actually). Management is asking for the estimated financial impact of this event, and your stress levels are rising. It’s all hands on deck!

1) Get event data
You go to the NOAA website, pull down wind datasets from the latest update and work to get them into a usable format.

2) Intersect with your portfolio
Now, it’s time to intersect the footprint with your portfolio data, which may take another hour or so.

3) Update portfolio
After you get everything set up, you realize your portfolio is six months old, which may over- or underestimate your actual exposure. Do you pull an updated snapshot of your exposures? Probably not, because there isn’t enough time!

4) Run financial model SQL scripts
With a manual intersection process, you are likely unable to easily access the impact of policy terms and conditions, so you’ll need to run some financial model scripts to determine the actual exposure for this event.

5) Create and share reports
You finally get some financial numbers ready and format them into a nice report for management.

Then, you think about what you actually had on your to-do list for the day before the hurricane was in the picture…or, wait, maybe not…because just then you see that NOAA has published the next snapshot of the hurricane.

Rinse and repeat. It’s going to be a long night.

See also: How to Predict Atlantic Hurricanes  

Let’s face it, if you can’t extract insight from data fast enough to mitigate damage or provide a timely course of action, your operational efficiency and downstream customer satisfaction go downhill fast. And just think, this was for a single data source. Realistically, you have to perform these same steps across multiple sources to gain a complete understanding of this event. (e.g. KatRisk, Impact Forecasting, JBA flood, NOAA probability surge).

What makes the process so inefficient?

  • You had to source the data yourself and operationalize it (i.e., get it into a usable format)
  • You had to navigate the complexity of the data, which can be exceptionally time-consuming (depending on the source, resolution and other variables)
  • You realized your portfolio data was out of date (this is a big problem because how can you determine actual financial impact against outdated information?)
  • You had to manually run a financial model after determining the exposures that could be affected by the event
  • And, of course, you had to manually pull the information together into a report for stakeholders

So what can you do?

Application programming interface (API) integrations help to solve these challenges by ensuring you always have the latest hazard data and portfolio snapshot available. If you invest just a few hours to get your data configured with a data import API like SpatialKey offers, you’ll always have the latest view of your exposures ready to analyze—without ever lifting a finger. You’ll save countless hours by investing just a few up front. This also enables quicker and more accurate analyses downstream because you won’t be over- or understating your exposures (not to mention making errors by scrambling at the last minute to get a refreshed snapshot).

Imagine another hurricane strikes…but this time you’re set with automation

Those couple hours that it took to get your portfolio data integrated and automation in place with a solution like SpatialKey are paying off (no deep breaths required).

Within moments of NOAA publishing an update, you receive an email notifying you of the financial and insured impact. With the click of a button, you’re in a live dashboard, investigating the event, your affected exposures and more.

You still have to get those numbers to management, but this time you can breathe easy knowing that your numbers are not only accurate, but that the whole process took a fraction of the time. Now when NOAA (or any other public or private data provider) pushes the next update, you’ll be set with a highly scalable infrastructure that enriches your data, calculates financial impact and produces a report within minutes.

Why was this process much more efficient?

  • Because you invested a couple hours up front to integrate API technology, your exposure data was up to date
  • You had access to pre-processed, ready-to-use hazard footprints as they became available
  • The event was monitored 24/7 so you didn’t have to constantly track it and pull reports to understand what changed
  • Custom filters and thresholds ensured you were never inundated with notifications and only received metrics that you care about
  • You saved a bundle of time because a financial report was auto-generated for you to pass along to upper management
  • You were able to quickly share reports across teams so claims could get a head start on their customer outreach

Now, you’ll never be a bottleneck in the process of understanding and communicating the impact of an event to your stakeholders. And, with all the time you’ve saved, you can use advanced analytics solutions to contextualize the event and dive deeper into investigating it some more.

Tick tock: It’s time to make your event response run like clockwork

It’s clear there’s a better way to tackle the growing challenge of deriving insight from data and quickly understanding the impact of an event. If you lack the ability to operationalize and extract insight from time-critical data, you’re operating in status quo when your management team and customers expect to know more about an event, and sooner.

Fortunately, automation doesn’t have to be a time-consuming or costly endeavor. There are simple ways to automate your manual processes, such as API integrations, that save time and steps along the way. “Automation” can carry with it preconceptions of disruption and heavy investment, but this is not true of a data enrichment and geospatial analytics solution like SpatialKey. Automating your event response operations can improve your customer retention and drive efficiencies now—not years from now.