Tag Archives: data analytics

Do You Know What You Don’t Know?

On March 12, 1928, William Mulholland, Harvey Van Norman and Tony Harnischfeger hiked down the dry side of the newly filled St. Francis Dam in California, inspecting worrisome leaks. Mullholland was the mastermind behind Los Angeles’ new aqueduct — a 233-mile series of man-made rivers and reservoirs that would eventually bring much-needed water to the San Fernando Valley. Before he left that day, Mulholland reassured Harnischfeger that, even though the leaks needed to be fixed, they didn’t represent a serious structural danger. That evening, the dam gave way, and 400 people living below the dam, including Tony Harnischfeger, were washed down the valley. It was a tragedy that could have been avoided.

We still have preventable tragedies, such as breadth of the pandemic and the Beirut explosion, but we also have advanced science that can give us quick vaccines, improvements in cyber security, fewer auto accidents through safer vehicles and greater access to relevant data from health and wellness to weather and water leaks.

Those of us in insurance technology know how knowledge can help us take advantage of opportunities and avoid risks. At Majesco, to help us look ahead, we gather insurer viewpoints and assess strategic priorities. This year’s survey report, Strategic Priories 2021: The Insurance Industry Shift Hits Hyper-Acceleration for Digital Business Models, the first of two reports, is especially crucial to an understanding of priority shifts: how COVID-19 affected insurer priorities and how those who are accelerating key strategies are leaping forward. 

In today’s blog, we’ll see that the COVID pandemic is not the only challenge insurers face. We’ll take a close look at what concerns insurers today.

Internal Challenges — Insurance Execs Share Their Views

Majesco surveyed insurance executives regarding these internal challenges:

  • Digital capabilities
  • Data security
  • Innovation
  • Legacy systems
  • Budget
  • Aligning IT and business strategies
  • Data and analytics capabilities
  • Talent (retention, availability)
  • Change management
  • Aging workforce/retirements
  • Post-COVID work environment

Of the 11 internal challenges in the survey, five show indications of becoming more the “norm” of doing business (Figure 1): Digital capabilities, Aligning IT and business strategies, Data and analytics capabilities, Data security and Talent (availability, retention). These challenges showed steady increases in concern over time but peaked in 2018-19 and have trended lower over the past two years. This trend likely suggests that companies have adapted to these norms through improved capabilities, adjusting expectations or increased confidence via the benefit of experience gained. 

However, the question remains … Do these perceptions match reality? A few examples:

  • Many insurers embraced portals, but most of these do not deliver a next-gen, holistic digital experience that customers, employees and distribution partners want and expect and that is part of a digital maturity that is needed.
  • Insurers have made progress getting their internal data sources in order but still struggle to realize the benefits of a fully integrated data environment consisting of internal and new external structured and unstructured sources, with AI/ML-generated insights. 
  • Given increasingly severe data breaches, data security may be more of an illusion than reality for many critical industries like insurance and why the need for cyber insurance is so critical.
  • The reliance on an aging IT workforce to keep old legacy systems running should shift insurer priorities to rapid legacy transformation, including no code/low code capabilities.

Figure 1: Declining levels of concern about internal challenges

The challenges regarding Budget and Legacy systems remain steady, with above average concern in every survey. Budgets will likely always be a universal source of discomfort, but as companies begin to establish new operating models that have new cost structures and shift IT expenses from capital expenditures to operating expenditures, companies open up the opportunity to reallocate resources to new initiatives. 

While many insurers have completed or begun legacy transformation projects, many of these are non-platform modern, on-premise implementations that are the “new legacy” systems and are now being replaced by next-gen, cloud-based digital insurance platforms. This next-gen platform has superior capabilities that meet the needs of today’s and tomorrow’s insurance customers, including no code/low code capabilities. 

See also: 5 Things to Know When Integrating AI

Interestingly, four new internal challenges in this year’s results all garnered lower levels of concern: Innovation and Change management, Digital and Data/Analytics capabilities and IT-Business alignment. Low levels of concern over Aging workforce/retirements and post-COVID work environment align with the downward trend in concern over Talent (availability, retention). These could be blind spots for many companies, which we’ll discuss in a moment.

Talent and Retention — IT Crisis or Minor Business Conundrum?

It is not surprising that IT executives are much more concerned about internal talent issues than their business counterparts, as seen in figure 2. Their 25% higher concern about the availability and retention of talent is driven by the fact that they face stiff competition with insurtech and Big Tech for employees with the skills needed to transform to digital-first insurers. Until they can achieve the transformation, however, IT needs to retain as much of its aging workforce as possible to maintain legacy systems and keep the current business running. This lack of alignment is problematic, meaning resources are allocated to business as usual rather than focused on creating the business needed for the future.

Figure 2: Levels of concern over talent issues, IT vs. Business

External Challenges — Insurance Execs Share Their Views

Majesco surveyed insurance executives regarding these external challenges:

  • Changing customer expectations
  • Emerging technologies
  • Regulatory requirements
  • COVID-19’s impact on target markets
  • Pace of change
  • Insurtech
  • Growing market availability of new/innovative insurance products
  • New competition from outside the insurance industry
  • New competition from inside the insurance industry
  • The rise of direct sales (B2C and B2B)
  • The rising cost of the agent channel
  • AM Best innovation score

This year, we see declining concern for several external challenges, including Changing customer expectations, Regulatory requirements, Pace of change and New competition from inside the insurance industry. It appears that insurers are feeling more confident in these areas, either through improved capabilities, adjusted expectations or experience (or a combination of these). 

However, as with the internal challenges, is there a risk of being too comfortable, confident and complacent, particularly coming out of COVID? Andreessen Horowitz says that e-commerce increased more in the last six months than in the entire decade beforehand! Add to this the emerging customer experiences from fintech and retailers such as Sofi and Amazon that are creating experiences, rather than transactions. Majesco research indicates that customers are willing to share their data if it leads to more personalized products and pricing that fit their changing lifestyles and risks, like on-demand and embedded insurance.

In addition, concerns have been nearly flat or declined slightly in concern over Emerging technologies, New competition from insurtech, startups, MGAs and New competition from tech giants from outside the insurance industry. This last challenge issue is a concerning blind spot, as experimentation and market entries by players from outside the industry accelerates. Consider a few recent examples:

  • Intuit launches QuickBooks insurance and 401K services — QuickBooks customers can now protect their businesses with comprehensive insurance coverage and offer their employees a 401(k) benefit, traditionally offered only by large companies. This integration will enable QuickBooks users to seamlessly obtain a customized quote and easily purchase general liability, professional liability and workers’ compensation coverage from Next Insurance with a few clicks of a button, directly from their QuickBooks account.
  • Petco launches insurance — In October, Petco announced the launch of Vital Care, a paid annual plan providing pet parents with a convenient, affordable way to meet their pets’ routine wellness needs.
  • Walmart offers pet insurance and health insurance — In November, Walmart announced it added pet insurance as animal adoptions soar during the pandemic. The company is offering insurance through Petplan and connecting people to pet sitters or dog walkers through Rover. In July 2020, it was announced that Walmart was launching a health insurance arm — dubbed Walmart Insurance Services — to sell plans to consumers. Walmart’s low prices and wide footprint could pose a threat to insurance startups — especially those firms breaking into the Medicare Advantage market.
  • Tesla insurance approved in Texas — In December 2020, the Texas Department of Insurance approved filings for insurance to be underwritten by a third-party Austin-based insurer and distributed by Tesla Insurance Services, which follows the company’s 2019 launch in California. Tesla’s Texas program uses driver behavior-based data collected by the vehicle as an input to determining at-fault collision rates. The program also covers cyber identity fraud expenses, electronic key replacement, loss or damage to the original wall charger and loss when the vehicle is being driven by autopilot.

As industry and market trends evolve, so do the topics covered by our annual survey. We added five new external challenges this year. Although these don’t have historical results for trending, COVID’s Impact on Your Target Markets had the fourth-highest overall ranking among all 14 external challenges, reflecting the real uncertainty on the pandemic’s effect on the economic health of consumers and businesses, and the impact for insurance. 

Growing market availability of new/innovative insurance products ranked sixth, tied with insurtech, ranking higher than the challenges from new competition from inside and outside the industry, suggesting that incumbent insurers more strongly associate insurtech with these new offerings – again, another potential blind spot on new competition.

The remaining three new issues, rise of direct sales, rising cost of the agent channel and AM Best innovation score, had considerably lower ratings than all other external challenges. (See Figure 3.) Again, are these blind spots, or do insurers have a good handle on these challenges? With e-commerce rising across all industries and demographic shifts in channel preference, we expected higher ratings on these two channel challenges. With the lackluster results of AM Best’s first Innovation Assessment mentioned earlier, it is somewhat surprising that this issue came in dead last.

Figure 3: New external challenges added to the survey

Key Differences Among Segments

Aggregating the ratings across all internal and external challenges highlights some key differences between direct written premium (DWP) tiers and line of business segments. The largest tier is much more concerned about internal challenges overall. This suggests that the complexity of the large organizations leads to a higher number of challenges that can be a hindrance to agility. Smaller companies should have an advantage here with less bureaucracy and complexity – but old systems could also be a hindrance negating this advantage. Replacing these systems with next-gen core platforms should now be of the utmost urgency.

See also: Claims Development for COVID (Part 1)

Companies with new products are much more concerned about the internal and external challenges, as highlighted by the aggregated rating comparisons. (See Figure 4.) The L&A/Group Only segment is a close second on five of these. This makes sense when you consider that these companies are challenging the status quo with non-traditional products and blazing trails, which can exacerbate existing challenges (e.g. IT, talent, etc.) as well as bringing up brand new challenges.

Figure 4: Differences in aggregate levels of concern over internal and external challenges by lines of business

How does your opinion rank within these challenges? Can knowledge open your organization up to the opportunities in our midst?

Do you know what you don’t know? 

Covering for a Gap in Workers Comp Data

What happens when a key data source becomes less available, reducing carriers’ ability to evaluate risk? This has happened during the pandemic in workers’ compensation.

In workers’ compensation, OSHA is one of the top data sources that underwriters use. In particular, underwriters will look at OSHA inspections and violations to measure some aspects of the risk. 

Here at Convr, our focus has been to help carriers with the right insights at the right time for better decision-making, and we found, using a vast data pool, that planned inspections dropped 48% in 2020.

One reason is fewer claims; as operational capacity was reduced or suspended for many industries in 2020, workers’ compensation claims dropped by over 20%. As accidents declined, inspections that normally would have followed weren’t needed. In addition, OSHA reduced the number of planned inspections for the safety of their inspectors.

The reduction in inspections has led to a lack of reliable information for workers’ compensation carriers to evaluate businesses — but this is where technology comes in. With the help of artificial intelligence and advanced analytics, carriers can still determine the risk of a business by looking at past patterns.

These past patterns include types of structured and unstructured information that data scientists refer to as “features” in machine learning models. Often, significant features are high-dimensional nonlinear combinations of company and property characteristics, such as the size of the business, the year it was established and prior violations. Other features include social media information and product and services data.

See also: 9 Months on: COVID and Workers’ Comp

Applying AI to our data lake, which is informed by over 2,000 data sources, Convr has determined that, in place of the normal volume of OSHA inspections, carriers can use a workplace safety model to accurately quantify risk. A workplace safety model consists of a machine learning model that predicts how safe a workplace will be based on OSHA data and the different data sources mentioned above.

Companies labeled as the riskiest 10% by Convr’s proprietary workplace safety risk scoring model observed three times as many future violations as those labeled as the median risk.

COVID-19 has proven that circumstances can change unexpectedly, and carriers have to become adaptable and flexible enough to implement alternative solutions to minimize the impact. Advanced AI models, like the one Convr has created to quantify workplace safety, hold tremendous value for carriers, enabling them to better understand risks even when traditional sources of information are limited or unavailable.

When armed with technological advancements such as these, carriers are equipped with the right tools for better decision-making and optimal underwriting results.

How Analytics Can Tame ‘Social Inflation’

“Social inflation” is considered one of the major emerging risks that the insurance industry must face. While people may misconstrue the term as relating to the rising impact of social media on online behavior of netizens, it has actually to do with increasingly hostile legal environment that insurance carriers are facing today. This manifests in the form of much larger verdicts, liberal treatment of claims by boards, more aggressive plaintiff bars, etc. This article explains the trend and describes measures that carriers can deploy to keep a check on increasing legal expenditure.

Here are some signs of the phenomenon:

  • A major P&C insurer anticipates $40 million in quarterly legal costs for property losses alone
  • There is a 94% increase in assignment of benefit (AOB) lawsuits in the state of Florida in the last five years
  • An increased probability of “nuclear verdicts” (> $10 million) is a real trend. In 2018 alone, the top 100 verdicts ranged from $22 million to $4.6 billion

Here are some factors driving the trend:

  • Litigation Funding: Propelled by easy capital availability, a new class of plaintiff attorney funding model has emerged in the last 10 years. Essentially, this model provides funding for legal expenses to plaintiff attorneys in exchange for a portion of the judgment or settlement. The model is good in the sense that it levels the playing field against large, well-funded corporations, but the unintended consequences (for insurance companies) is an exponential increase in attorney representation and pursuit of aggressive legal strategies. Approximately $9 billion has been committed to this “industry.”
  • Rising anger against big corporations: The very premise of the big corporation versus the individual scenario is driven by anger. The perception among the consumers and the jurors is that a corporation has only one goal: profit. Stagnation of incomes/wages is another contributing factor to this mindset. Big businesses in America like to talk about the good jobs they provide, but median salaries in the U.S. have been flat for decades. This is not because of a failure of workers to become more productive; there were gains in productivity, but they did not go to workers. Gains mostly flowed to the organizations and their shareholders, including executives who received sizable stock-based compensation. Hourly compensation for workers remained practically flat.
  • Large verdicts being driven by general social pessimism and jury sentiments: New and interesting patterns are being observed in jury behavior, especially in personal injury and liability claims. Emotion and trust play a big role in how a jury rules. Some of the key reasons behind these large verdicts are: jurors’ distrust in big corporations and their lawyers; jurors paying less attention to lengthy testimonies and complex explanations; impact of social media on how millennial jurors view the court system; impact of emotional stories on how a jury thinks; and the “what if it were me?” attitude influencing how jurors approach justice. 

Leaving aside the financial burden from social inflation, which is quite significant on its own, increased litigation also affects carriers in other ways. There can be inaccuracies in reserving, which is based on counsel’s estimates of litigation spending. There can be negative press and poor customer engagement/satisfaction. And claims can increase in complexity, delaying settlement. .

Many insurers are either in the early stages of dealing with social inflation or are not moving as fast as they’d like on the problem. The most common reasons include:

  • Claim handling practices are often inadequately data-driven
  • There is limited ability to foresee litigation
  • There is lack of trust in analytical models
  • The company takes a semi-reactive approach for claim settlement and negotiations
  • Assignments are inefficient, either to claim handlers or attorneys

What can insurers do to manage this growing challenge?

See also: The Data Journey Into the New Normal

Enter machine learning and artificial intelligence

Use of analytics from first notification of loss (FNOL) until the claim is paid is now a norm rather than a competitive edge. All large carriers invest heavily in use cases such as fraud detection, severity-based claim assignment, automatic loss estimation, recovery optimization, etc. However, given the complex nature of how litigated claims are handled, only a few top U.S. carriers are able to weave these capabilities effectively into business processes. Insurance carriers that successfully use analytics to drive business process change in claims litigation will stay ahead of this massive threat.

There are two unmistakable trends that carriers need to leverage:

  1. Aggressive use of information sitting in claims and policy systems (structured attributes, adjuster notes) to develop signals around plaintiff attorney behavior. These signals then need to be deployed within claims operations to encourage early case assessment and litigation prevention.
  2. Use of increasingly clean and comprehensive sources of external litigation information (from state courts, where most insurance litigation lies) to inform your litigation strategy. This includes past verdicts information by venues, judges, attorney firms, case types, etc. A thoughtful use of this information can help claims adjusters and defense attorneys devise the litigation strategy to avoid worst outcomes. There are multiple firms providing research tools that are based on these. Our recommendation, however, is for carriers to ingest the information, merge it with internal claims data and develop models and tools providing a comprehensive view

How carriers can leverage this trend

Carriers can use predictive and descriptive analytics solutions in claims management to mitigate the problem. At a high level, such solutions can lower ballooning legal costs by avoiding litigation and optimizing litigation strategy. Both these approaches call for incorporating advanced analytics processes and models at the key steps of the claim settlement process. This includes:

  • Advanced analytics and machine learning models to predict and avoid litigation and post-suit strategy formulation
  • Natural language processing (NLP) to leverage unstructured data and extract additional insights
  • External data ingestion to augment internal data and enhance model accuracy
  • Enhanced data management capabilities

Any insurance claims raised can result into any of the three possible outcomes: claims without litigation, claims that involve litigation without trial and claims that involve litigation with trial. With the help of the aforementioned technologies, carriers can engineer the following mechanisms in the claims management process (Figure 1) to optimize the legal expenditure for a given product:

  1. A – Prediction of litigation likelihood 
  2. B1 – Defense counsel selection
  3. B2 – Law firm benchmarking 
  4. C1 – Attorney insights 
  5. C2 – Prediction of settlement failure
  6. D – Legal activity duration, legal expense prediction
  7. E – Case-level insights 

See also: Growing Risks of Social Inflation

Conclusion

Claims data within insurance companies is being increasingly seen as a key asset, not a byproduct of the claims process. However, the path to using internal and external sources of data to drive business outcomes is long and arduous. It is becoming increasingly important for carriers to incorporate insurance analytics processes geared toward optimizing legal spending. To achieve this, insurers require a combination of capabilities to these engagements, i.e. ability to handle big data, ability to develop advanced analytics solutions and knowledge of “what, why and how to deploy” in claims business processes.

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.

3 Technologies That Transform Insurance

The combination of artificial intelligence (AI), robotic processing automation and predictive data analytics is fundamentally redefining how businesses operate, how consumers engage with brands and, indeed, how we go about our daily lives. The field of insurance is no exception. Outlined here are three ways smart technology is affecting insurance, with a focus on identifying lessons learned and defining specific keys to success.

Back Office Robotic Process Automation

The impact of rules-based robotic process automation (RPA) on insurance operations has been well-documented. RPA tools are driving efficiency and productivity gains in generic back-office functions such as F&A and HR, and insurers are tackling processes related to claims administration and account management.

One key challenge is scalability. In many cases, concept initiatives have failed to gain traction, resulting in isolated pockets of automation that yield limited benefit. In others, overly ambitious enterprise-wide projects struggle with boil-the-ocean syndrome. A well-defined center of excellence (CoE) model that develops and documents best practices and then propagates them across different business units has proven effective.

Another critical lesson has been the importance of CIO involvement. This was lacking in many early RPA projects. For one thing, because RPA tools focus on process and business functions rather than programming skills, CIOs often weren’t interested. Business unit heads, moreover, feared that CIO involvement would lead to bureaucratic logjams and derail aggressive adoption schedules. Practice has shown, however, that CIO oversight is essential, to avoid both general shadow IT problems as well as specific interoperability, stability and security issues related to RPA functionality.

See also: Using Technology to Enhance Your Agency  

Leading early adopters have also continually pushed the envelope of automation levels. In a claims processing environment, 70% of claims may be simple and straightforward, and therefore ideally suited to an RPA application. At the other end of the spectrum, 5% to 10% of claims are complicated and unusual, and therefore require a human’s expertise and judgment to evaluate. While doable, automating these complex outlier claims isn’t cost-effective. The challenge then becomes to focus on the remaining 20% to 25% of claims. By analyzing the frequency of different types of claims, insurers can identify cases where the time and effort needed to configure a bot will yield a return.

Applying Cognitive Capabilities to RPA

RPA has delivered impressive benefits to insurance operations in terms of cost reduction, accuracy and auditability. That said, the tools are limited to the specific if/then rules they’re configured to follow. If a bot encounters a scenario that doesn’t align with what it’s been taught, it gets stuck.

More advanced cognitive systems apply pattern recognition to analyze unstructured data to identify key words and phrases in context. This promises to take insurance automation to the next level. While an RPA bot can extract a specific piece of data such as a policy number from a specific form, it can’t interpret underwriting rules or aberrations from a form on which data is unstructured and organized differently.

A cognitive application, meanwhile, can scan documents of various types and formats and apply machine logic and learning to identify relevant data in spite of discrepancies in how the data is structured or presented. This allows people to focus on policy/claim exceptions rather than formatting issues. More specifically, by injecting cognitive applications into operational workflows at key “intelligent gates,” insurers can more easily identify aberrations in unstructured data and highlight the policies and claims that require further human involvement.

IoT, AI and Insurance Underwriting

The combination of Internet of Things (IoT) and artificial intelligence will have perhaps the most transformational impact on insurance. By deploying networks of smart, connected IoT sensors, insurers can collect and analyze volumes of data at the point of critical business activity. Leveraging the pattern recognition and predictive analytics powers of AI, meanwhile, creates insights that insurers can use to refine actuarial tables and improve the rules of underwriting.

Consider these examples:

  • Sensors in vehicles ranging from commercial trucks to passenger cars monitor and document speed and driver behavior. Insurers can analyze data to calculate accident probabilities of safe vs. risky drivers over time. Based on those calculations, premiums could be adjusted. Smart sensors and cameras can also detect drowsy drivers or erratic behavior, triggering alarms.
  • Smart home technology that monitors suspicious activity and automatically shuts off water pumps in the event of a burst pipe can lead to lower homeowner policy costs, particularly for premium coverage such as insuring valuable artwork from theft and damage.
  • Pharmacies that store and transport medicines can deploy temperature and humidity monitors to ensure that supplies stay within required guidelines. Reducing the risk of tainted medicine reaching consumers could reduce liability risk.
  • Smart video analytics can determine wear and tear of roofs, oil pipe damage from foliage and animal migration and levels of water and soil contamination. Such insights enable corrective action before catastrophes strike and reduce the level of unforeseen risk for underwriters.
  • By monitoring pressure or fluid flow in an oil pipeline, sensors can trigger shut-off valves if limits are exceeded, thereby preventing costly environmental damage.

Innovative insurers are exploring how to deploy these capabilities into policy formulation. For instance, customers who adopt the technology could qualify for discounts. (Given the privacy issues surrounding driver monitoring, the voluntary aspect would seemingly be critical for auto insurance policies.) Another option: Insurers team up with technology partners to offer smart sensor services, thereby helping policyholders while creating revenue streams.

See also: Smart Home = Smart Insurer!  

The combination of sensor array and intelligent technology is refining underwriting and claims payment. Insurers can tune actuarial tables and pricing models to cover potential losses before they occur, as well as avoid incidents by advising policy owners to take corrective actions. In other circumstances, sensors can take action on their own.

Ultimately, these capabilities will enable insurers to fundamentally redefine their operational and customer engagement models.