Tag Archives: The Hartford

Does Your Structure Fit Your Strategy?

Balancing growth and profitability is no easy trick as major changes unsettle an industry that has been used to gradual change. “Business as usual” approaches are faltering in the face of generational shifts in customer needs, rising capital requirements, new regulatory burdens, low interest rates, disruptive technology and new competitors. Many companies aren’t getting the results they need from textbook moves, such as fine-tuning marketing programs, updating products, enhancing customer service systems and beefing up information technology systems.

Strategic success now requires a structural response, and companies can’t adapt to current conditions without modernizing often antiquated structures. Before attempting to implement new strategies, companies need to re-evaluate operating model dimensions such as capital deployment, organizational design, tax positioning and governance.

In a changing insurance industry, strategic execution often requires a new structure. We recognize this is easier said than done. Structural impediments take many forms. Some companies lack scale to generate profitable growth under new capital requirements. Others with siloed, hierarchical organizations lack the flexibility to respond quickly to market shifts. Poor technological capabilities often hamstring old-line insurers facing newer, more digitally oriented rivals. And tax reform looms as a potential threat to profitability in certain business lines.

We’ve seen three common industry responses to these pressures:

  • Anticipation of the effects of marketplace trends and make appropriate structural adjustments, clearing the way to profitable growth. For example, life insurer Metlife avoided costly regulatory mandates by selling registered broker distribution to MassMutual and spinning off its Brighthouse retail operations.
    Other companies, including Manulife and SunLife, have made substantive acquisitions to consolidate scale positions.
  • Recognition of the need for structural change, but have yet to carry it out. Some companies have plans in the works, or are debating their merits, opportunistically waiting for the right deal to come along.
  • Hunkering down behind existing structures, making only minor tweaks, and hoping to emerge from the storm without too much damage. For some this is rational because they are constrained. For other companies with more viable options, company culture may be removing certain options from consideration too quickly.

Companies in the first two groups are giving themselves a chance to compete and ideally prosper. But the third group is not making strategy equal structure.

A time for structural change

Most insurers work diligently to improve their businesses across several dimensions. They seek more insight into consumer needs and behaviors, nurture unique capabilities to differentiate
themselves from competitors, modernize products and distribution strategies, and embrace digitization. These are all sound approaches, but are inadequate to address the uncertainties facing insurers today. The familiar “good to great” rallying cry assumes a certain stability in underlying economic and market conditions that hasn’t been the case since the financial collapse of nearly a decade
ago.

The crash and its aftermath undermined pillars of many insurance business models. We’ve seen years’ worth of modest industry growth – just over 3% for P&C companies, and barely more than 1% for life insurance companies.

This long stretch of sluggish global growth has pressured revenues and forced insurers to compete harder on price. Persistent near-zero interest rates are squeezing profit margins, especially in life insurance. Moreover, tougher accounting rules are driving up costs while heavier capital requirements weigh down balance sheets
and dilute returns. Compounding these challenges are potentially destabilizing effects of recent U.S. tax legislation on earnings and growth. Taxes may rise for some insurers, an unexpected outcome that could force them to raise prices or find other ways to protect shareholder returns. Substantive impacts may result from falling corporate tax rates, offset by the limiting of deductions for affiliate premiums, limits to the deductibility of life reserves, accelerated earnings recognition and a slowing down of deferred acquisition cost deductions.

Competitive dynamics also are shifting as expanding “pure play” asset managers such as Vanguard and Fidelity block growth
avenues for insurers. Other companies and some new entrants are innovating and experimenting with strategies to disrupt distribution. Still others, including private equity firms, are looking at ways to
change the cost curve through aggressive acquisition and sourcing strategies

See also: Next for Insurtech: Product Diversity  

To be sure, some long-term trends could benefit selected insurers or at very least shift the risks. Longer life spans and the shift of responsibility for retirement funding to individuals may drive demand for annuities and other retirement products.

Many companies are as unprepared to capitalize on new opportunities as they are to meet long-term challenges.

However, many companies are as unprepared to capitalize on these opportunities as they are to meet long- term challenges. Often the problem comes down to scale. Some insurers lack the resources to build new distribution platforms and customer service capabilities in growing markets like group insurance, ancillary benefits and retirement plans. While markets for individual products may be easier for new entrants, establishing expensive platforms for asset management, retirement, and group are more difficult – driving a desire for scale and putting more pressure on sub-scale competitors.

Sometimes the issue isn’t scale but a failure to respond quickly enough as conditions change. Buying habits are changing, notably through online channels (though our research indicates that for bigger and more complex transactions, most people still want help of some sort of “human” interaction before actually buying). It takes investment and experimentation for companies to try and then refine new models. Some companies haven’t built needed assets and capabilities or adjusted to evolving distribution patterns and consumer buying habits.

The ideal response to each challenge and opportunity will vary for each company, depending on its unique characteristics and circumstances. Few companies have the scale to fix all of their problems on their own. In virtually every case, the right solution will involve structural change.

Structural change drives strategic execution

The link between strategy and structure has become apparent to many management teams, particularly in life insurance. Major life insurers are taking dramatic steps to add scale, open new distribution channels, augment capabilities, drive down costs and rev up growth and, where regulation is burdensome or profit-prospects dim, exit geographies and business-lines. Recent
transactions in the sector show the range of structural options to advance strategic goals in a changing marketplace.

As companies recognize that traditional approaches to annual planning, project funding approvals, and technology architecture may be getting in the way of innovation and their ability to respond to changing market conditions in real time, they are rethinking and redesigning core processes to help the company change.

Traditional approaches may be getting in the way of innovation and the ability to respond to changing market conditions in real time.

Sometimes, the best choice is to move out of harm’s way. Companies can preserve margins by exiting businesses targeted for higher capital requirements or costly new accounting standards. For example, Metlife’s 2017 Brighthouse spinoff bolstered its case for relief from designation as a SIFI (systematically important financial
institution) and associated capital requirements. Exiting U.S. retail life insurance markets also enabled Metlife to focus on faster-growing businesses that are less vulnerable to rock-bottom interest rates. As another example, The Hartford recently announced the sale of Talcott Resolution to a group of investors, completing its exit from the life and annuity business.

When scale is an issue, the solution may lie outside the company or in new structural approaches:

  • Some insurers form partnerships to expand distribution, diversify product portfolios or bolster capabilities. Companies also adjust their scale and capital structures through mergers, acquisitions and divestitures. Sun Life paid nearly $1 billion in 2016 for Assurant’s employee benefits business, filling gaps in its product portfolio and gaining scale to compete with larger rivals. MassMutual’s purchase of MetLife’s broker/dealer network in 2016 enlarged the MassMutual brokerage force by 70%, and freed Metlife to pursue new distribution channels.
  • New product lines offer another path to faster growth or fatter profit margins. Several insurers have moved into expanding markets with lower capital requirements, such as asset management. Voya, Sun Life, and Mass Mutual have acquired or established third-party asset management units to capitalize on investment expertise they developed managing internal portfolios.
  • The Hartford recently announced an agreement to acquire Aetna’s U.S. group life and disability business, deepening and enhancing its group benefits distribution capabilities and accelerating the company’s digital technology plans.
  • We also see companies establishing technology-focused subsidiaries, like Reinsurance Group of America’s (RGA) RGAx and AIG’s Blackboard.

Still other companies have moved aggressively to improve their cost structures:

  • Insurers seeking greater financial flexibility have divested assets that require significant capital reserves.
  • An insurer that offloads its own defined-benefit plan to another via pension risk transfer (PRT) frees up capital and eliminates ongoing pension funding requirements. Other cost-saving moves focus on workforce expenses. In addition to reducing staff, such measures include relocating workers to low-cost areas or jurisdictions offering significant tax incentives.

Structural change requires cultural change (or vice versa)

Companies that launch ambitious structural initiatives may under-
appreciate the role of culture in making new structures work. Culture is a set of norms, mindsets and behaviors that have
developed around existing organizational structures. The two are tightly linked, and one can’t change without the other changing, too. Structural change will force changes to operating models and
cultural change may be necessary to drive it.

A new structure without corresponding changes in culture amounts to little more than a redesigned table of organization. Culture makes or breaks the new structure, influencing everything from resource
allocation to governance and even profit formulas. It’s not uncommon for a company to expend tremendous effort and resources on a complete structural overhaul, only to see incompatible cultural norms thwart strategic execution. For example, a new, streamlined operating model intended to accelerate decision-making and foster cross-functional collaboration won’t take root in a culture that exalts hierarchy and encourages
employees to focus on narrow functional priorities.

A new structure without corresponding changes in culture
amounts to little more than a redesigned table of organization.

Culture also influences a company’s willingness to make the deep structural changes in time to avert a crisis. Those who wait until changing market conditions have undermined their operating models put themselves at a disadvantage. Nevertheless, few companies attempt structural change in “peacetime.”

See also: Creating a Customer-Insight Strategy  

Absent a crisis, directors usually provide guidance and perspective and monitor indicators such as growth and profitability, while management takes responsibility for achieving specific strategic objectives. Successful companies, by contrast, continually reassess their structures in light of evolving market conditions. They understand that organizational structures aren’t permanent fixtures, but strategic choices they need to reconsider as circumstances and objectives change.

Implications: Is your culture ready for structural change?

Amid the constant drumbeat of change in today’s insurance industry, successful companies are meeting structural challenges with structural solutions. Approaches vary from company to company. Some add scale or enhance capabilities, while others streamline cost structures or exit lagging business lines. With the right cultural support, these structural responses position a company to capitalize on industry changes that confound competitors.

Based on our experience, companies that adjust their structures ahead of a crisis exhibit three distinctive cultural traits:

  • Directors track management’s allocation of resources against key strategic priorities.
  • Directors and managers make clear to everyone throughout the company that “the truth” is not only welcome, but expected.
  • Directors make sure the company’s talent, capabilities and know-how align with its goals.

Complacent organizations that don’t make structural changes until a crisis hits also have three distinguishing characteristics:

  • They over-emphasize “cascaded objectives” that often conflict.
  • They rely excessively on “can-do spirit” as a plan of action.
  • They exhibit unwarranted confidence in their own prescience and planning capabilities.

Which scenario typifies your organization? Are you confident your structure and culture are fit for purpose?

Claims Advocacy’s Biggest Opportunity

We know the single greatest roadblock to timely work injury recovery and controlling claim costs. And it’s not overpriced care, or doubtful medical provider quality or even litigation. It is the negative impact of personal expectations, behaviors and predicaments that can come with the injured worker or can grow out of work injury.

This suite of roadblocks is classified as “psychosocial” issues – issues that claims leaders now rank as the No. 1 barrier to successful claim outcomes, according to Rising Medical Solutions’ 2016 Workers’ Compensation Benchmarking Study survey.

Psychosocial roadblocks drive up claim costs far more than catastrophic claims, mostly due to delayed recovery, and claims executives told us they occur regardless of the nature of injury. In other words, one cannot predict from medical data the presence of a psychosocial issue; one has to listen to the injured worker with a fresh mind.

See also: Power of ‘Claims Advocacy’  

It’s likely no coincidence that, while the industry has progressively paid more attention to psychosocial issues this past decade, there’s also been a shift toward advocacy-based claims models over adversarial, compliance- and task-based processing styles. Simply put, advocacy models – which treat the worker as a whole person – are better equipped to control or eliminate psychosocial factors during recovery. According to the 2016 Benchmarking Study survey, claims advocacy and greater training in communication and soft skills, like empathy, are associated with higher-performing claims organizations.

Psychosocial – What It Is, What It Is Not

The Hartford’s medical director, Dr. Marcos Iglesias, says that the “psych” part does not mean psychiatric issues, such as schizophrenia, personality disorders or major depressive disorders. Instead, he points out, “We are talking about behavioral issues, the way we think, feel and act. An example is fear of physical movement, as it may worsen one’s impairment or cause pain, or fear of judgment by coworkers.”

The Hartford’s text mining has found the presence of “fear” in claim notes was predictive of poor outcomes. Similar findings were recently cited by both Lockton (“Leading with Empathy: How Data Analytics Uncovered Claimants’ Fears”) and the Workers’ Compensation Research Institute (“Predictors of Worker Outcomes”).

Emotional distress, such as catastrophic reaction to pain and activity avoidance, is predictive of poor outcomes. Other conditions, behaviors and predicaments include obesity, hard feelings about coworkers, troubled home life, the lack of temporary modified work assignments, limited English proficiency and – most commonly noted – poor coping skills. Additionally, being out of work can lead to increased rates of smoking, alcohol abuse, illicit drug use, risky sexual behavior and suicide.

When peeling back the psychosocial onion, one can see how adversarial, compliance- and task-driven claim styles are 1) ill-suited for addressing fears, beliefs, perceptions and poor coping skills and 2) less likely to effectively address these roadblocks due to the disruption they pose to workflows and task timelines.

Screening and the One Big Question

Albertsons, with more than 285,000 employees in retail food and related businesses, screens injured workers for psychosocial comorbidities. To ensure workers are comfortable and honest, the company enlists a third-party telephonic triage firm to perform screenings. “It’s voluntary and confidential in details, with only a summary score shared with claims adjusters and case managers,” says Denise Algire, the company’s director of risk initiatives and national medical director.

At The Hartford, Iglesias says claims adjusters ask one very important question of the injured worker, “Jim, when do you expect to return to work?” Any answer of less than 10 days indicates that the worker has good coping skills and that the risk of delayed recovery is low. That kind of answer is a positive flag for timely recovery. If the worker answers with a longer duration, the adjuster explores why the worker believes recovery will be more difficult. For example, the injured worker may identify a barrier of which the adjuster is unaware: His car may have been totaled in an accident. This lack of transportation, and not the injury, may be the return-to-work barrier.

It Takes a Village

Trecia Sigle, Nationwide Insurance’s new associate vice president of workers’ compensation claims, is building a specialized team to address psychosocial roadblocks. Nationwide’s intake process will consist of a combination of manual scoring and predictive modeling, and then adjusters will refer certain workers to specialists with the “right skill set.”

Albertsons invites screened injured workers to receive specialist intervention, usually performed by a network of psychologists who provide health coaching consistent with cognitive behavioral therapy (CBT) principles. This intervention method is short in duration and focuses on active problem-solving with the patient. The Hartford also transfers cases with important psychosocial issues to a specialist team, selected for their listening, empathy, communication skills and past claims experience.

Emotional Intelligence – Can It Be Learned?

Industry professionals are of mixed minds about how and if frontline claims adjusters can improve their interpersonal skills – sometimes called “emotional intelligence” – through training. These soft skills include customer service, communication, critical thinking, active listening and empathy. Experts interviewed agree that some claims adjusters have innately better soft skills. But they also concur that training and coaching can only enhance these skills among claims staff.

See also: The 2 Types of Claims Managers  

Pamela Highsmith-Johnson, national director of case management at CNA, says the insurer introduced a “trusted adviser” training program for all employees who come into contact with injured workers. Small groups use role-playing and share ideas. An online training component is also included.

Advocacy – The Missing Link to Recovery

Could it be that advocacy – treating the injured worker as a whole person and customer at the center of a claim – is the “missing link” for many existing claim practices to work, or work better? Whether for psychosocial issues or other barriers, organizations like The Hartford, Nationwide, CNA and Albertsons are paving the road to a more effective approach for overcoming pervasive barriers to recovery. Participants in the 2016 Workers’ Compensation Benchmarking Study confirm that higher-performing claims organizations are taking this road.

The coming 2017 study will continue to survey claims leaders on advocacy topics. A copy of that report may be pre-ordered here.

Data Opportunities in Underwriting

For more than a decade, Americans have been trained to assess and buy insurance products as commodities. This is partly thanks to commercials by Geico, the biggest advertising spender in insurance for many years, which has pushed the concept that “Fifteen minutes can save you 15%,” portraying policies as “the same,” where the only differentiator is the price. Some have dubbed insurance’s being viewed as a commodity as the industry’s biggest challenge.

On top of price-centric buying behavior, most consumers who are required to purchase certain insurance products — such as medical and auto — expect to have a wide selection and may switch insurance carriers at a blink of an eye. With competition increasing, big data and associated technologies provide timely opportunities by reshaping the modern insurance landscape.

The insurance business model typically comprises four parts:

  • Underwriting — where insurance companies make money.
  • Investment — where insurance companies invest money.
  • Claims — where insurance companies pay out (the cost factor).
  • Marketing — where insurance products and services are promoted and often advertised.

Insurance companies have always used data in each part of the business model — to assess risk, set policy prices and to win/retain consumers. Previously, insurers would formulate policies by comparing customers’ histories, yielding a simplistic and not-very-accurate assessment of risk. Today, our increasing ability to access and analyze data as well as advancements in data science allow insurers to feed broader historical, continuous and real-time data through complex algorithms to construct a much more sophisticated and accurate picture of risk. This enables insurance companies to offer more competitive prices that ensure profit by covering perceived risk and working within customers’ budgets. Such prices, or setting policy premiums, come from underwriting.

In this post, we will focus on an underwriting use case in the highly competitive auto insurance space, where accuracy of risk assessment and rate setting ultimately drive the insurers’ profitability. Future posts will address other parts of the insurance business model.

More accurate (and competitive) pricing for auto insurance underwriting

Auto insurance may be the most competitive part of the insurance marketplace. Customers shop around (often marketed to by price-comparison services) and change insurers at will. To offer competitive premiums that allow profitability, auto insurers have no choice but to assess risk as accurately as possible.

See also: Why Data Analytics Are Like Interest  

In auto insurance, insurers use both “small” and “big” data. David Cummings explains the two as:

“Traditionally, underwriters have developed auto insurance prices based on smaller data — such as the car’s make, model and manufacturer’s suggested retail price (MSRP). But ‘bigger data’ is now available, providing far more information and allowing insurers to price policies with a better understanding of the vehicle’s safety. From manufacturers and third-party vendors, insurers can learn about a car’s horsepower, weight, bumper height, crash test ratings and safety features. That big data helps insurers create sophisticated predictive models and more accurate vehicle-based rate segmentation.”

As data increasingly becomes the lifeblood for insurance companies, the combination of big data and analytics is driving a significant shift in insurance underwriting. For example, faster processing technologies such as Hadoop have allowed insurers like Allstate to dig through customer information — quotes, policies, claims, etc. — to note patterns and generate competitive premiums to win new customers.

The data and analytics movement has also made room for newcomers like Metromile to enter the market. Although the company started out with no proprietary data of its own, Metromile has quickly gained customers and collected data with a new model: auto insurance by the mile.

This entrance of Metromile into the auto insurance space has both disrupted the industry and put pressure on incumbent insurance providers to make advances with their own models.

In auto insurance underwriting, a number of ways to use new data to achieve more accurate pricing have gained attention:

  • Using usage-based insurance (UBI)
  • Leveraging external data
  • Leveraging real-time data

Usage-based insurance

UBI can be used to more closely align premium rates with driving behaviors. The UBI idea is not new — there have been attempts to align premiums with empirical risk based on how the insured actually drives for a couple of decades. In 2011, Allstate filed a patent on a UBI cost determination system and method. Progressive, State Farm and The Hartford are just a few examples of other companies that are embracing UBI methods in underwriting.

Technological evolutions like the Internet of Things (IOT) and all its attendant sensors provide new ways to capture and analyze more data. The UBI market has flourished and is expected to reach $123 billion by 2022. The U.S., the largest auto insurance market in the world, will lead the way in UBI marketing and innovation in 2017. With UBI’s market potential, there has also been a rise in business models such as pay-per-mile insurance for low-mileage drivers using UBI methods in underwriting. Embracing UBI methods in underwriting is no small feat, because of the huge amounts of data that must be collected and integrated. Progressive collected more than 10 billion miles of driving data with its UBI program, Snapshot, as of March 2014. For the most part, the data focuses on mileage, duration of driving and counts of braking/speeding events. These are all “exposure-related” driving variables, which are considered secondary contributors to risk. They can be bolstered with external data such as traffic patterns, road type and conditions, which are considered primary contributors to risk, to create a more accurate picture of an individual driver’s risk.

Leveraging external data

The idea of using external data is also not new. As early as the 1930s, insurance companies combined internal and external data to determine the rate for policy applicants. However, more recently, the speed of technological advancements has allowed insurers to dramatically redefine and improve their processes.

For example, customer applications for insurance today are significantly shorter than before, thanks to external data. With basics like name and address, insurers can access accurate data files that will append other necessary information — such as occupation, income and demographics. This means expedited underwriting processes and improved customer experiences. Some speculate that all insurers will purchase external data by 2019 to streamline their underwriting (among other things).

Another consideration is that the definition of external data has been evolving. Leveraging external data in an auto insurance risk assessment today may mean going beyond weather and geographic data to include data on shopping behaviors, historical quotes and purchases, telematics, social media behaviors and more. McKinsey says, “The proliferation of third-party data sources is reducing insurers’ dependence on internal data.” Auto insurers can incorporate credit scores into their underwriting analysis as empirical evidence that those who pay bills on time also tend to be safer drivers.

Better access to third-party data also allows insurers to pose new questions and gain a better understanding of different risks. With the availability of external data like social data, insurers can go beyond underwriting and pricing to really managing risks. External data doesn’t just go beyond telematics and geographic data; it may also have real-time implications.

Leveraging real-time data

Real-time data is a subset of the rich external data set, but it has some unique properties that make it worth considering it as a separate category. The usage of real-time data (such as apps that engage customers with warnings of impending weather events) can cut the cost of claims. Insurers can also factor data such as weather into the overall assessment at the time of underwriting to more accurately price the risk. In the earlier example of using external data to shorten the underwriting process, accessing external information in real-time and checking with multiple sources makes the information in auto insurance application forms more accurate, which, in turn, leads to more accurate rates.

Underwriters can also work with integrated sales and marketing platforms and can reference data such as social media updates, real-time news feeds and research to provide a more accurate assessment for those who seek to be insured. Real-time digital “data exhaust” — for example, from multimedia and social media, smartphones and other devices — has offered behavioral insights for insurers. For example, Allstate is considering monitoring and evaluating drivers’ heart rate, electrocardiograph signals and blood pressure through sensors embedded in the steering wheel.

See also: Industry’s Biggest Data Blind Spot  

Insurers can influence the insured’s driving behaviors through real-time monitoring, significantly altering the relationship with each other. A number of insurance companies, such as Progressive — in addition to the pay-per-mile insurer Metromile — are monitoring their customers’ driving real-time and are using that data for underwriting purposes. Allstate filed a patent on a game-like system where drivers are put in groups. Those in the same group could monitor driving scores in real-time and encourage better driving to improve the group’s driving score. Groups can earn rewards by capturing better scores.

Conclusion

There’s no doubt that the risky business of insurance is sophisticated. The above examples of leveraging UBI, external data and real-time data merely scratch the surface on data-driven opportunities in auto insurance. For example, what about fraud? Efficiency and automation? Closing the loop between risk and claims? Because only 36% of insurers are even projected to use UBI by 2020, those that embrace data-driven techniques will quickly find themselves ahead of the game.

While it’s outside the scope of this post, we should note that leveraging data and methods shouldn’t be done without careful consideration for consumers. As consumers enjoy easier insurance application processes, as well as having more products to choose from and compare prices on, increasingly they will want to understand how these data and analytics techniques affect them personally — including their data privacy and rights.

As we pause and reflect on how data and analytics have driven changes in auto insurance underwriting, we welcome questions and discussions in the comments section below. In the future, we’ll examine other ways the insurance market is becoming more data-driven, including the changes that data and analytics are driving in auto insurance claims and the rising focus of marketing.

This article first appeared on the site of Silicon Valley Data Science

Telematics: Moving Out of the Dark Ages?

While the number of usage-based insurance (UBI) policies reached 14 million at the end of September 2016, most insurance companies are still overwhelmed by the challenge of using collected data to rate their customers’ driving habits.

This conclusion is based on analyzing the world’s 27 largest UBI programs, including those of Admiral, Allianz, Allstate, AXA, Generali, Desjardins, Direct Line, State Farm, the Hartford, Unipol, Uniqa and Zurich.

See also: Why Exactly Does Big Data Matter?  

Progressive, the No. 1 telematics insurer globally, still uses a temporary device and does not collect GPS data. Unipol, the No. 2 player, still only collects mileage data from its customers.

We believe, however, that the prehistoric age of connected insurance analytics is ending. The era was based on the premise that all policyholders are reluctant to be “tracked.” But with most of us giving daily credit card, fingerprint, driving speed or location details to companies such as Apple, BMW or Vodafone, how to make sense of the self-censorship that insurers apply to their programs?

The truth is that more data benefits insurance companies… and the careful drivers! At the center of this change is advanced data analytics – the ability to extract insights from real-time data sources and discover risk-predictive patterns.

Our analysis, detailed in the Connected Insurance Analytics report, shows that the glaciation period’s ice is melting and that all the key insurers are now moving.

See also: Data Science: Methods Matter (Part 3)  

Progressive started a vast recruitment plan to attract data scientists. Generali also made a strong move by acquiring MyDrive, an analytics provider with early footsteps in smartphone UBI. Allstate just created Arity, which will collect data on drivers and sell analytics products to third parties. Simultaneously, Unipol created Alpha, a self-standing analytics and telematics operation.

The bulk of insurance companies is yet to act. To help them adapt to this new climate, Ptolemus published the Connected Insurance Analytics (CIA) report as a step-by-step guide to advanced analytics. It describes, analyzes and illustrates the process by which advanced analytics companies take raw driving data and transform it into real-time, individual risk profiles.

Screen Shot 2016-12-06 at 9.42.20 PM

The investigation shows that acceleration, braking and mileage are the most used — unsurprisingly — but also that the range of factors is much wider and illustrates the complexity involved in selecting the correct criteria.

To offer a predictive driving score, the report demonstrates that insurers must gain a deep understanding of driving conditions. Adding contextual data, such as road type or relative speed, is a necessary step to price customers fairly.

The full article from which this is extracted is available here.

A Biopsychosocial Approach to Recovery

Watching people try to recover from injury can be baffling. Some recover function quickly; others do not. Why is there so much variability with severity and duration of disability, given similar injuries or illnesses? Why do some individuals get stuck in delayed recovery?

Our medical system has tended to focus on the physical: If there is back pain, there must be something going on in the disc, vertebrae or nerve roots. That approach isn’t bad. Medicine has made a lot of progress with that tactic. But sometimes a physical cause isn’t apparent.

If we examine what else may be happening in people’s lives, what they’re thinking and what they’re feeling, we start to uncover circumstances and behaviors that may be delaying their recovery.

The Hartford is focusing on a different and promising approach that looks beyond the physical aspects (such as symptoms, physical findings, test results) and looks at the whole person as a biopsychosocial being who may have non-physical barriers that are delaying recovery. The Hartford has developed a program that offers help to assist people in getting unstuck.

Internal data analytics indicate the presence of psychosocial risk factors can account for a two- to four-fold increase in disability duration of work-related injuries.

Background

The biomedical model has served as the traditional foundation of our understanding of the body and has formed the bedrock of modern Western medicine. In essence, this model reduces illness and injury to their most basic units; the body is seen as a machine that operates on the basis of physical and chemical processes. In other words, find out what’s wrong with the body and fix it.

The biopsychosocial model seeks to amplify the biomedical model by addressing an individual holistically as a physical, psychological and social being.

The 1970s saw pioneering work in the treatment of chronic pain by using psychological — or behavioral – principles. For instance, W.E. Fordyce at the University of Washington found that helping patients with pain behave normally (that is, getting them to stop displaying pain behaviors) led to improvements in function.

In the 1980s, cognitive behavioral therapy (CBT) began to be used in treating chronic pain patients. CBT tries to change patterns of thinking or behavior that are behind a person’s difficulties all to change how they feel.

In the past 20 years, some have shown the usefulness of interventions based on specific psychosocial risk factors for pain and disability. Much of this work has been carried out in Canada, Europe, Australia and New Zealand.

See also: Better Outcomes for Chronic Pain

The medical and research literature points to social and behavioral factors — like fear, expectation of recovery, catastrophic thinking and perceived injustice — as powerful forces that can delay recovery after an injury or illness. As one example, a 2015 WCRI study showed that fear of getting fired could affect a worker’s return to work after an injury.

The Hartford Approach

Armed with an understanding of these drivers of disability, The Hartford is using its advanced data analytics and developing innovative solutions to help workers at risk regain the function they had before an injury or illness.

A patented text mining technique allows us to look for psychosocial, comorbid and other risk factors to identify, early on, individuals who demonstrate a likelihood to have a prolonged disability. By combining this early identification tool with a growing toolkit of interventions, we are finding new ways to help individuals restore their lives after an injury or illness.

One such tool is a proprietary, telephonic coaching intervention. Having identified claimants who show an elevated risk for prolonged disability, we invite them to participate in a program that matches them with a specially trained coach who helps them overcome psychosocial barriers. By equipping individuals with skills and techniques to change the way they think, feel and act, we help them develop confidence to take control of their recovery. This confidence allows them to increase function in all areas of life, including return to work.

The voluntary program, called iRECOVER(SM) uses phone calls with the coach, along with a workbook and homework assignments. It can last several weeks.

Although still in its early days, iRECOVER shows promising results: earlier return to function and return to work.

Participant feedback has been very positive. For instance, we have received emails and letters from injured workers that say:

  • “There’s light at the end of the tunnel.”
  • “I feel confident going back to work. A good part of this is due to my participation in iRECOVER.”
  • “I think what you do is probably as important as medical treatment.”
  • “iRECOVER helped me be courageous and strong.”

See also: Data Science: Methods Matter (Part 1)

Conclusion

By considering the whole patient, applying potent data analytics and developing innovative solutions, we are getting to the root of delayed recovery for many individuals. The results will benefit all concerned, especially the injured worker, who just wants life to get back to normal.