The Future of Insurance - March 2022

The Future of Insurance – March 2022

Genetics helps estimate the risk of disease – but how much does it really tell us?

Genetics research has made momentous strides in the 21st century. At the start of the century, we had a broad understanding that most medical problems in the developed world are partly genetically determined but lacked the technology to fully explore the secrets hiding in our genome.

This century’s technological advances have allowed us to make substantial progress in identifying the genomic underpinnings of heart disease, mental health disorders, cancer, dementia, and other diverse diseases that medicine still struggles to prevent, diagnose and treat.

We can now quantify the overall genetic contribution (heritability) and identify specific genetic variants that contribute to the risk of these diseases. But, as with most things genetic, it’s complex, it’s incomplete and scientists are still working out how to make it clinically useful.

Each of these diseases and disorders has a “polygenic” underpinning. This means that instead of only one or a few genes playing a role in the risk of diseases, it is likely to be thousands of genes where inherited changes in each gene make a modest impact on our risk of each disease. This dispersed genetic component combines with other environmental risk factors – such as smoking, diet, trauma and stress - to further increase or decrease our risk of ill health.

This polygenic underpinning is good news in many ways: our risk is determined by multiple genetic variants and usually not by a single yes/no genetic risk factor. Some single genetic risk factors do exist, such as variants in the BRCA1 and BRCA2 genes that substantially increase the risk of breast and ovarian cancer. But these single-hit genetic variants are rare – meaning that, for most of us, genetic predisposition comes from the combined risk of many variants.

Genetic studies over the past 20 years have identified many such variants that contribute to the polygenic loading for disease: more than 100 for breast cancer, depression and coronary artery disease, and 38 for Alzheimer’s disease. All these variants can give us information on the underlying biology and new targets for drug development. These variants can also contribute to the calculation of risk scores, indicating those who are at genetically high risk of disease and those who are at low risk.

The term “score” is appropriate here because it can be calculated by simply adding up the number of high-risk genetic variants carried across the genome, weighted by the importance of each variant.

If you look at polygenic scores for a large group of people, most people will have a score that is near average, so their genetics adds little information to their disease risk. A few people will have a high polygenic score, putting them at increased risk of developing a particular disease. Others will have inherited few risk variants, putting them at lower risk of disease.

So are polygenic scores useful? Potentially, yes, but mostly no – not yet. Polygenic scores can give us a personal estimate of our genetic risk for a certain disease, which remains constant throughout life and can be calculated at any point. They could give an impetus to lead a healthy lifestyle, undertake appropriate screening, or be watchful for early symptoms.

Polygenic scores are not available through the NHS but can be derived from the genetic data generated by direct-to-consumer genetic testing companies, such as 23andMe, and ancestry companies, such as AncestryDNA. These companies test your DNA from a mailed-in saliva sample and give you the option to download your genetic data to your desktop.

23andMe shows you information on your polygenic risk of type 2 diabetes. And websites such as impute.me allow you to upload your own genetic data to calculate your polygenic scores.

Making sense of your score

The MyGeneRank app gives you your coronary artery disease score on your phone by linking to your 23andMe genetic data. This sounds wonderfully accessible, but what can your coronary artery disease polygenic score actually tell you? First, it can tell where your risk lies compared with other people of the same genetic ancestry as you. For example, if MyGeneRank tells a person that their polygenic score lies at the 55th percentile of the distribution, then their risk lies very close to average.

What about someone who is at the 95th percentile, in the top 5 per cent of people with the highest genetic risk? That might be worrying information, but to interpret a polygenic score you need two further pieces of information. First, you need a “relative risk”: how much does your polygenic score change your risk compared with an average person? Does it double your risk or increase it tenfold? Second, you need a “lifetime risk”: what is your chance of being diagnosed with the disorder?

These figures depend on your genetics, how many people develop the disease and how much of the disease risk the polygenic score explains, which is small for most diseases. For example, a woman with a breast cancer polygenic score in the top 1 per cent of the population has a lifetime risk of about 30 per cent. For most disorders, lifetime risks are lower.

A high polygenic score for schizophrenia might be worrying news for people, but our genetic knowledge of schizophrenia is far from complete. The estimated chance of developing schizophrenia for someone with a high polygenic score, seen only in one in 100 people, is 4 per cent compared with a 1 per cent risk for most of the population. This result may be reassuring, but also shows that schizophrenia polygenic scores should not be used clinically. We have developed an online tool to calculate how the lifetime risk of disease changes at different levels of polygenic scores.

Polygenic scores give you a snapshot of your genetic risks, but for most disorders, the partial information captured is not strong enough for it to be useful. The next decade will determine whether polygenic scores remain a personal curiosity, or whether they become an important medical tool.

Cathryn Lewis, Professor of Genetic Epidemiology & Statistics, King's College London and Oliver Pain, Postdoctoral Research Associate, King's College London

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Solving the problem of complex document processing for insurance companies

Data and documents are the lifeblood of every insurance company. Yet they also represent one of the biggest challenges insurers face

Insurers handle, on average, more than 100,000 documents every year, which take a huge amount of time and effort to process manually, and are open to human error. Most of them contain unstructured data (which accounts for 80 per cent of data produced globally), meaning the documents don’t follow a predefined or known structure: for example, insurance contracts and policies.

Most document-processing software, even when leveraging AI, cannot process all the possible variations of the content. In addition, natural language approaches based on statistical models will also fail to accurately analyse unstructured documents because the amount of similar content is not high enough to enable generalisation.

One promising approach you may have heard of is intelligent document processing (IDP), which is used to automate data extraction and processing by combining artificial intelligence (AI) and natural language understanding (NLU) with tools such as optical character recognition, thus improving efficiency and margins and reducing cost.

Unstructured data challenges

However, most IDP providers are solely focused on structured and semi-structured data solutions. And the out-of-the-box solutions they do provide for unstructured data are also incapable of processing documents that don’t match a template in their repository, and therefore don’t deliver accurate results.

The two biggest problems for these vendors are the variability and ambiguity of natural language used in the documents. The range of terms used within an organisation and by its customers and vendors is too broad to be completely captured and will be constantly evolving. Even the most advanced term-based natural language processing (NLP) tools are unable to process terms they haven’t seen during training.

Then there is the ambiguity of language used, meaning the same concepts can be expressed in different ways. Even advanced NLP algorithms can’t associate phrases with similar meanings but different wordings, because they are based on word statistics, not high-level conceptual ideas.

That’s where Cortical.io comes in. With 11 years’ experience of researching and developing NLU solutions, we specialise in processing unstructured and complex documents with a high level of accuracy. Our novel approach to NLU, Semantic Folding, means we’re better at extracting the meaning of sentences and paragraphs in documents, even if the wording is different.

The Cortical.io solution

We provide two core intelligent document processing solutions: Contract Intelligence and Message Intelligence. Using NLU, Contract Intelligence enables insurers to quickly review policies and other key documents by accurately identifying, extracting and classifying important information.

Message Intelligence automates the intake/submission process by classifying and extracting key information from email messages and attachments. Both solutions deliver accuracy and efficiency, reduce manual labour time and are easy to implement without the need for data scientists or AI experts.

A major issue for insurers is accurately and efficiently reviewing and extracting key information from prior insurer plans and submissions and entering it on their own system. They have to review tens of thousands of policies every year using a process that’s tedious, error-prone and expensive.

The process is further complicated by plans with complex tables, multiple employee classes of coverage, each insurer using its own jargon and format, provisions needing to be interpreted, and multiple products intermixed in one plan. Manual checking can also overlook key provisions, resulting in inaccurate quotes and profit losses.

Automating the workflow

To tackle the problem for one client, a Fortune 500 insurer, Contract Intelligence was trained on five different products: long-term disability, short-term disability, vision, dental and life insurance. The solution enabled the insurer to find all the necessary provisions and thus produce more accurate quotes.

By automating its quoting workflow, the insurer was able to extract key information from the different insurance products from other insurers, interpret that information into its own language, detect employee classes and associate extractions with class descriptions, classify clauses and export document extractions as an Excel document.

Contract Intelligence also delivered a 30 per cent reduction in labour, translating into 7,500 man hours saved every year. Read the case study here.

Another problem for global organisations is making sure their local policies in different regions are correct and consistent. One of our clients, a commercial property insurer, has offices worldwide and 2,000 customers with as many as 30 policies each.

Because the insurer doesn’t use industry-standard forms, the binding copies of its locally issued documents may differ from the originals. Added to that, the process of reviewing these policies is extremely time- and labour-intensive, with the team spending one third of their time looking for differences in provisions between the original policy and final version. Seventy per cent of the documents also contained errors even after review.

Natural language understanding

To solve the problem, Contract Intelligence was trained with documents from different regions based on annotations from the insurer’s subject-matter experts. It then compared policies on a word-by-word and clause-by-clause basis to understand the different variations of the same concept – for example, recognising a Force Majeure provision where the word “war” has been mistakenly replaced with “conflict”.

Contract Intelligence quickly and accurately reported the differences in terms and conditions between the original and locally issued policies, enabling the insurer to make any timely corrections, thus keeping the risk of policy differences to a minimum. Read the case study here.

The key indirect benefit of automating policy reviews is a reduced risk of missing key clauses or misinterpreting provisions. It also enables policies to be reviewed and compared quicker and, thus, quotes to be issued faster and more deals closed. Furthermore, it allows for a better pricing strategy because the quotes are more accurate.

Essentially, Cortical.io’s solutions enable insurers to improve their business processes by speeding up the time they take to handle incoming submissions and extract key information from documents required for quotes or claims, thus improving their turnaround time, and by extension, customer satisfaction and the chances of winning new business. They also improve efficiency and reduce errors, thereby also lowering insurers’ exposure to risk.

To learn more about Cortical.io’s Intelligent Document Processing solutions, call (+1) 888 933 6658, email info@cortical.io or visit cortical.io

INDUSTRY VIEW FROM CORTICAL.IO

An asteroid impact could wipe out an entire city – a space security expert explains NASA’s plans to prevent a potential catastrophe

The Earth exists in a dangerous environment. Cosmic bodies, like asteroids and comets, are constantly zooming through space and often crash into our planet. Most of these are too small to pose a threat, but some can be cause for concern.

As a scholar who studies space and international security, it is my job to ask what the likelihood of an object crashing into the planet really is – and whether governments are spending enough money to prevent such an event.

To find the answers to these questions, one has to know what near-Earth objects are out there. To date, NASA has tracked only an estimated 40 per cent of the bigger ones. Surprise asteroids have visited Earth in the past and will undoubtedly do so in the future. When they do appear, how prepared will humanity be?

The threat from asteroids and comets

Millions of objects of various sizes orbit the Sun. Near-Earth objects include asteroids and comets whose orbits will bring them within 120 million miles (193 million kilometers) of the Sun.

Astronomers consider a near-Earth object a threat if it will come within 4.6 million miles (7.4 million km) of the planet and is at least 460 feet (140 meters) in diameter. If a celestial body of this size crashed into Earth, it could destroy an entire city and cause extreme regional devastation. Larger objects - 0.6 miles (1 km) or more - could have global effects and even cause mass extinctions.

The most famous and destructive impact took place 65 million years ago when a 6-mile (10-km) diameter asteroid crashed into what is now the Yucatán Peninsula. It wiped out most plant and animal species on Earth, including the dinosaurs.

But smaller objects can also cause significant damage. In 1908, an approximately 164-foot (50-meter) celestial body exploded over the Tunguska river in Siberia. It leveled more than 80 million trees over 830 square miles (2,100 square km). In 2013, an asteroid only 65 feet (20 meters) across burst in the atmosphere 20 miles (32 km) above Chelyabinsk, Russia. It released the equivalent of 30 Hiroshima bombs worth of energy, injured over 1,100 people and caused US$33 million in damage.

The next asteroid of substantial size to potentially hit Earth is asteroid 2005 ED224. When the 164-foot (50-meter) asteroid passes by on March 11, 2023, there is roughly a 1 in 500,000 chance of impact.

Watching the skies

While the chances of a larger cosmic body impacting Earth are small, the devastation would be enormous.

Congress recognized this threat, and in the 1998 Spaceguard Survey, it tasked NASA to find and track 90 per cent of near-Earth objects 0.6 miles (1 km) across or bigger within 10 years. NASA surpassed the 90 per cent goal in 2011.

In 2005, Congress passed another bill requiring NASA to expand its search and track at least 90per cent of all near-Earth objects 460 feet (140 meters) or larger by the end of 2020. That year has come and gone and, mostly due to a lack of financial resources, only 40 per cent of those objects have been mapped.

As of Feb. 14, 2022, astronomers have located 28,266 near-Earth asteroids, of which 10,033 are 460 feet (140 meters) or larger in diameter and 888 at least 0.6 miles (1 km) across. About 30 new objects are added each week.

A new mission, funded by Congress in 2018, is scheduled to launch in 2026 an infrared, space-based telescope – NEO Surveyor – dedicated to searching for potentially dangerous asteroids.

Cosmic surprises

We can only prevent a disaster if we know it is coming, and asteroids have sneaked up on Earth before.

An asteroid the size of a football field – dubbed the “City-killer” – passed less than 45,000 miles from Earth in 2019. An asteroid the size of a 747 jet came close in 2021 as did a 0.6-mile (1-km) wide asteroid in 2012. Each of these was discovered only about a day before they passed Earth.

Research suggests that one reason may be that Earth’s rotation creates a blind spot whereby some asteroids remain undetected or appear stationary. This may be a problem, as some surprise asteroids do not miss us. In 2008, astronomers spotted a small asteroid only 19 hours before it crashed into rural Sudan. And the recent discovery of an asteroid 1.2 miles (2 km) in diameter suggests that there are still big objects lurking.

What can be done?

To protect the planet from cosmic dangers, early detection is key. At the 2021 Planetary Defense Conference, scientists recommended a minimum of five to 10 years’ preparation time to mount a successful defense against hazardous asteroids.

If astronomers find a dangerous object, there are four ways to mitigate a disaster. The first involves regional first-aid and evacuation measures. A second approach would involve sending a spacecraft to fly near a small- or medium-sized asteroid; the gravity of the craft would slowly change the object’s orbit. To change a bigger asteroid’s path, we can either crash something into it at high speeds or detonate a nuclear warhead nearby.

These may seem like far-fetched ideas, but in November 2021, NASA launched the world’s first full-scale planetary defense mission as a proof of concept: the Double Asteroid Redirection Test, or DART. The large asteroid Didymos and its small moon currently pose no threat to Earth. In September 2022, NASA plans to change the asteroid’s orbit by crashing a 1,340-pound (610 kg) probe into Didymos’ moon at a speed of approximately 14,000 mph (22,500 kph).

Learning more about what threatening asteroids are made of is also important, as their composition may affect how successful we are at deflecting them. The asteroid Bennu is 1,620 feet (490 meters) in diameter. Its orbit will bring it dangerously close to Earth on Sept. 24, 2182, and there is a 1 in 2,700 chance of a collision. An asteroid of this size could wipe out an entire continent, so to learn more about Bennu, NASA launched the OSIRIS-Rex probe in 2016. The spacecraft arrived at Bennu, took pictures, collected samples and is due to return to Earth in 2023.

Spending on planetary defense

In 2021, NASA’s planetary defense budget was $158 million. This is just 0.7 per cent of NASA’s total budget and just 0.02 per cent of the roughly $700 billion 2021 U.S. defense budget.

This budget supports a number of missions, including the NEO Surveyor at $83 million, DART at $324 million and Osiris Rex at around $1 billion over several years.

Is this the right amount to invest in monitoring the skies, given the fact that some 60 per cent of all potentially dangerous asteroids remain undetected? This is an important question to ask when one considers the potential consequences.

Investing in planetary defense is akin to buying homeowners insurance. The likelihood of experiencing an event that destroys your house is very small, yet people buy insurance nonetheless.

If even a single object larger than 460 feet (140 meters) hits the planet, the devastation and loss of life would be extreme. A bigger impact could quite literally wipe out most species on Earth. Even if no such body is expected to hit Earth in the next 100 years, the chance is not zero. In this low likelihood versus high consequences scenario, investing in protecting the planet from dangerous cosmic objects may give humanity some peace of mind and could prevent a catastrophe.

Svetla Ben-Itzhak, Assistant Professor of Space and International Relations, West Space Seminar, Air War College, Air University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The top automation trends in the insurance industry for 2022

In the search for a personalised experience, consumers are forcing insurers to adapt to their demands

Thanks to technology, we’re all used to speed and personalisation in our personal and professional lives. When a fast food app can greet you by name and ask if you want your favourite meal again, you expect that kind of customer experience from every company.

The insurance industry should offer the best possible customer service and the most personalised experience. After all, it deals with the most important parts of our lives, from health to safety to our family’s futures. And it’s all possible through AI-based automation, from machine learning (making predictions based on data and learned experiences) to deep learning (using non-linear algorithms to model abstract relationships in historical data). So what are the trends in automation coming up for 2022 and how is insurance going to look different at the end of the year?

Incorporating individual preferences

The most obvious trend driving the insurance industry that benefits from automation is consumer personalisation. From shopping to entertainment, our expectations for personalised experiences have changed. Customers want speed, customisation and recognition of personal preferences, and insurers can actively meet their demand for individualism.

Insurers can’t afford to lag behind. According to Policy Advice, nearly 90 per cent of consumers want more personalised insurance products and policies. And, according to a JD Power survey, more than one-third of consumers say they’re interested in usage-based car insurance (using technology to track driving habits and adjusts accordingly). The volume of customer interactions already using that technology has doubled.

Channel switching is another common expectation from customers looking for individualized service and flexibility. Insurers predict the volume of inbound communication will increase substantially, and nearly two-thirds of consumers want to communicate using a variety of digital options – not by making a phone call. That means the insurance company has to have a platform to offer a unified experience.

Experts estimate there’ll be up to 1 trillion connected devices by 2025. This means devices in our everyday lives continually learn more about us, our preferences and our surroundings. Of all the industries leveraging that data with data science to build powerful AI, insurance is one ripe to benefit most by connecting data to products, claims and customer service.

Optimising operational efficiencies

The insurance industry is moving into a digital-first economy with successful firms quickly optimising their operations and making them more efficient. While AI can’t (and shouldn’t) replace human connection and intuition, there’s no denying that AI’s ability to find, digest and analyse huge amounts of data far exceeds most teams of analysts. That capability translates to a number of big business benefits.

Better pricing. Working with large pools of data allows for very specific solutions that save time and reduce the margin of error from prices that don’t line up with costs. For example, a home insurance company could use AI to quickly analyse geographical location, marital status and other factors to come up with pricing very specific to that person and that house. In effect, using data instead of intuition as a guide allows for very specific offerings and lets insurance companies develop them more quickly than a purely manual effort.

Faster response time/higher response rate. Insurance companies are investing in AI-enabled systems to crunch and learn from claims data more quickly than human beings and boost customer satisfaction. From a bottom-line perspective, that means companies will be processing more claims, faster and more accurately. All of this increases volume throughput (without necessarily increasing staffing costs) and reduces waste in the claims processes.

Increase employee engagement/reduce turnover. No matter what the form of automation, it reduces and in some cases eliminates rote work from employees’ daily responsibilities. If you look at a call centre as one example, customer service agents won’t have to deal with the same query over and over again, especially when it’s a simple question. Automation takes the ‘blah’ out of business and replaces it with employee engagement.

Insurance agencies will use artificial intelligence to boost their bottom lines, market share and operational efficiencies in 2022.

Integrating with ease

Organisations are going to take sophisticated artificial intelligence and easily integrate it into their automation flows in 2022, especially when organisations are able to apply AI intentionally and narrowly. With AI embedded into their capabilities, low-code/no-code platforms provide a win-win for the IT team, the organisation and the users (both customers and brokers/agents who have to adapt to new tools).

And while insurance carriers have lagged behind other industries such as retail, one of the benefits of investing in AI-based solutions later is that the insurance sector is learning from other industries, reducing the time and cost of integration and adoption. Insurers will continue to seek and onboard SaaS solutions with enterprise-grade SDKs and powerful APIs to support modernisation efforts alongside their existing technological investments.

The benefits outweigh the costs

Change is hard. Adopting new technology that also changes how you work can be even harder. Customer service representatives may have to learn new roles. Brokers and agents may need to adopt different ways of interacting with customers. None of those changes are fixed overnight.

There is both a tangible and an intangible cost to adopting AI-driven tools and technologies. But the benefits of AI are so clear to both the company and the consumer, and the pace of adopting AI is going to be so fast in 2022, that it’s critical to embrace that change for everyone’s benefit.

To find out more, visit ushur.com

by William Roberts, Senior Product Marketing Manager and Meredith Barnes-Cook, Global Head of Insurance & Industries, Ushur

INDUSTRY VIEW FROM USHUR

A new era of opportunities in insurance

Times of uncertainty and challenge often separate visionaries from their more traditional counterparts. Insurance is certainly no exception.

Much of the global insurance landscape has changed, thanks largely to the recent conditions which continue to challenge even the most tenured carriers. However, with increased data and device connectivity, coupled with an ever-evolving demand for personalised offers and experiences, the small commercial insurance market has become even more ripe with opportunity.

As a critical and strategic market for carriers, SMEs have historically been underserved in terms of the products, experiences and even technology necessary to operate and evolve in a dynamic landscape. Even now, with the rapid adoption of customer-centric products, policies and advanced technologies, SME ratings of their insurers again fell significantly, for a second consecutive year.

Recent times have upended markets, and both insurance carriers and SMEs stand to benefit significantly by adopting a more strategic, innovative mindset. That mindset must encourage the adoption of robust unified platforms, purposefully designed with agility, breadth and depth of functionality, and with high precision and execution speed.

In an era fueled by digital dexterity, the demand for digital requires carriers to continue to adapt, and to not merely survive but thrive in this new insurance dimension. The power to deliver distinctly tailored and more personalised options, engagements and experiences, and expand capabilities, will better serve the SME market, which continues to move forward to build new and stronger foundations.

By incorporating data and technology, and the innovative mindset necessary to incorporate intuitive decisioning and data-fuelled personalisation into all facets of their business and across distribution channels, insurers are better able to not only support SMEs but also capitalise on the growth potential. Insurers are better empowered to deliver the essential functionality required to serve small and medium businesses, including the ability to rapidly introduce and launch new products, elevate user and customer experiences, and reinvent the beneficial value delivered with an agile business model.

For more than 40 years Sapiens has been dedicated to serving the insurance market with robust, feature-rich platforms, enabling carriers to deliver the agility, efficiency and human-centered technology they need to continue providing service, security and stellar experiences to their customers.

For more information visit us at www.sapiens.com or contact us directly at digitalnow@sapiens.com.

INDUSTRY VIEW FROM SAPIENS

Rethinking risk can unshackle Africa’s small scale farmers from the grip of poor weather

Right now, countries in the Horn of Africa are in the midst of a multi-season drought. There have also been years in which the rains have come with such force that floods wash out the season’s labour, sometimes along with homes, as happened in Mozambique just two years ago.

Climate change is making these disasters more common. It is hard for rural families whose livelihoods depend on the food they can grow. A 2015 study by the World Bank estimated that climate change over the next 20 years would increase the number of desperately poor people by 122 million, if nothing was done about it.

While the drivers of deep-seated poverty in rural Africa are many and complex, climate shocks are one of the most important.

Research I’ve done with colleagues in the past has shown how climate shocks affected rural households in Ethiopia and Honduras. We found that acute shocks – a devastating hurricane or prolonged drought – could push households into chronic poverty and hardship.

Not only can climate shocks push families into poverty, it can also keep them there. The dreaded anticipation of shocks, or risk, discourages investments that could otherwise raise families’ living standards and reduce their vulnerability to poverty. From the Sahel to Central America, small-farm households keep their modest savings in the form of food stocks. While understandable, indeed optimal given the constraints they face, this behaviour closes the vicious circle of shocks, risk and poverty.

But there may be a way to reset this relationship between risk, shocks and poverty. An emerging body of evidence reveals that new risk management tools that make households resilient to shocks further empower them to invest more in available technologies and economic opportunities.

My recent work in Mozambique and Tanzania , with colleagues (more on this later), adds to this evidence, showing what farmers can achieve by combining these tools.

Tools and their limitations

One of these risk management tools is agricultural index insurance. It provides payments in the event of crop losses, so a household doesn’t lose everything in a bad year. Experiments with index insurance have shown that when protected, farmers increase investment in their farms by as much as 30%, and reap matching increases in income.

I was involved in what has been one of the most successful index insurance interventions to date. In a 2010 collaboration, researchers from the International Livestock Research Institute, the University of Wisconsin and Cornell University launched a scheme like this for pastoralist households in northern Kenya. In 2015, it was adopted by the Government of Kenya and paid out over US$10 million to vulnerable pastoralists in the first five years.

Emerging evidence also shows that genetically encoded risk management technologies can achieve some of the same benefits as financial instruments. They include stress-tolerant crop varieties.

Both types of resilience-building technologies – financial and agronomic – have shown considerable promise but also have limitations. Insurance can be an expensive way to manage risk. It also brings costs each year whether or not insurance pays out.

An improved seed variety has no such continuing costs. Beyond the initial, substantial cost of developing the new variety, the seeds can be reproduced and purchased for little more than any other seed variety. But the seeds only protect farmers against specific peril (limited flooding or a specific type of drought). Beyond this they provide little or no protection.

Surprising result

These observations suggested to us that bundling different risk management technologies might be the way to unshackle Africa’s small-scale farmers from the vicious circle of shocks, risk and poverty.

In Mozambique and Tanzania, my colleagues and I conducted a four-year randomised control trial that combined index insurance and stress-tolerant maize to exploit the synergies of both together. The insurance expanded the protection that the seeds gave. The seeds’ tolerance of some drought reduced the cost of the insurance. It would also provide higher yields than other varieties even in normal years.

We designed the bundle so that in a severe drought, seeds would be replaced and farmers would be able to plant again in the next season with the same stress-tolerant seeds.

The project had a surprising result: farmers who experienced the steepest losses to drought grew a bigger harvest than they ever had before in the year immediately after.

When we analysed the data, we found that insured farmers could not only feed their families following a drought, they also increased the amount of improved maize seed they purchased. In fact, they invested more improved seed than they ever had before. Those additional purchases drove the increase in yields in the following year as farmers became convinced that the technologies worked, enabling them to deepen their investment and increase their incomes.

A new ending to the same old story

These and many other field trials are building evidence that effective tools to manage risk can create a new ending to the same old story about shocks, risk and poverty. These tools not only protect current well-being and promote resilience, but can also provide a solid foundation for future improvements. Together, resilience to shocks and the resulting investments into growing more food build what we call Resilience+.

Accelerating climate change has made it especially urgent to create flexible bundles that improve resilience.

If tools are effective and provide value, farmers will use them to increase their agency to choose improved inputs, to expand or diversify their planting, or to try new and unfamiliar practices that may produce more food. We believe this approach could have a transformative impact where risk is primarily what holds families back from stronger livelihoods.

Michael Carter, Professor of Agricultural and Resource Economics, University of California, Davis

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Inflation: Real challenges, real opportunities for insurers

Inflation poses real risks to insurers today. Yet effective insurance technology not only mitigates these challenges but also adds a competitive edge

It’s been a rough two years for the entire insurance industry. A global pandemic. Supply chain disruptions. And now, rising inflation in the US, UK and European markets makes it difficult to model volatile production costs or the real cost of resolving claims.

For example, consider the automotive industry and how inflation is wreaking havoc as insurance companies seek to update the way they manage entire portfolios of auto policies. It seems like everything in this industry is suddenly much more expensive, from new cars or used vehicles to the cost and availability of replacement parts and services. Additionally, bodily injury in auto insurance has also seen a marked increase in medical remediation costs.

This means that insurers’ existing actuarial cost models – which factor in repair, replacement and injury costs – need to be overhauled to properly include inflationary factors and consequences of longer turnaround times for repairs and medical remediation. This new process is certainly not as simple as putting in an arbitrary inflation percentage assumption.

For example, supply chain and inflation delays now mean auto replacement parts are harder to find and more expensive. This can lead to dissatisfied claimants who blame insurers for not addressing the ongoing challenges as well as higher costs, such as longer use of courtesy cars while repairs are being made. In such a scenario, does this insurer make provisions for additional customer services interaction?

All of this means that insurers need to improve their underwriting performance. This requires insurers to have a better understanding of the total risk that is transferring from the insured to the insurer, updating pricing models to reflect this risk and providing the right coverage for the needs of the insured, including new offerings or bundles.

The customer angle is an important one. When inflation rises ahead of salaries, customer budgets tighten and insurance companies need to do all they can to win new business, in addition to increasing retention and maximising profits for each customer. Inflation clearly affects consumers’ buying habits, too, especially in the areas of total demand and how receptive they may be to new product offers. Consumers are willing to pay a fair price for goods and services – including insurance premiums and add-on bundles – yet they may resist anything they consider to be unnecessary or that doesn’t add additional value.

Inflation can present a number of new challenges for insurers today. Yet for those insurers willing to embrace new technologies, these market conditions can also present a real opportunity.

Personalise and win

Highly personalised insurance is a perfect example of how insurance companies are combining new technologies with new strategies and gaining a competitive edge. In this case, many insurance carriers are using new types of data (e.g. usage-based insurance), modelling it using advanced analytics and applying machine learning and artificial intelligence algorithms to develop and deploy highly personalised offers.

Personalisation helps insurers generate more precise estimates while also offering more attractive prices to consumers. For example, consider the case of usage-based insurance (UBI). UBI for auto insurance can show that a given customer drives mostly on motorways and is therefore offered a different price than a customer who mostly drives in cities.

This type of personalisation strategy can lead to creative new ideas for even better results in the future. Imagine a customer who commutes to and from work several times each week. After modelling the data against other variables, such as weather, traffic and accident reports, the insurance company realises that this route has a higher number of incidents than an alternative route of similar distance and commute time. The company informs the customer of this information and they soon start using this alternative route. All of this is mutually beneficial to both parties but wouldn’t be possible without UBI. This is an example of ‘preventative’ insurance.

Insurers are still looking to collect new types of data for UBI, but there are already enough use cases and ways for them to use it to create highly personalised offers.

Enter enhanced risk modelling

These same powerful data modelling tools can also help insurance companies understand new risk implications caused by rising inflation. This includes powerful predictive capabilities to gain valuable insights into various trends related to claims frequency, severity and cost.

Without these advanced tools, insurers could face too much risk related to inflation-driven cost volatility. As a result, they may subject themselves to an inordinate amount of risk. As the entire insurance industry continues to refine capital reserve strategies and policies, pricing teams must respond accordingly. New shifts will require less focus on the volume of business written and more on the overall quality of business.

Yet if the old saying is true – if every challenge is truly an opportunity in disguise – then the opposite can also be true. Insurance providers who make the best use of the data they have available to them in the insurance pricing process will improve their ability to protect their underlying performance while giving customers a better experience and value for money.

New challenges require new strategies – and technologies

It’s a challenging time. If insurance companies don’t have an effective strategy to harness the impact of inflation as part of their predictive modelling efforts, they stand to incur significant losses, suffer customer turnover and miss new opportunities to generate vital revenues and a stronger bottom line.

It’s an important point. Insurers who don’t take decisive action now by implementing next-generation technology to improve underwriting performance through risk modelling, pricing and personalisation will fall behind. Yet it’s not too late. Embracing insurance solutions that offer all of these capabilities and more can help insurers not only improve their underwriting performance but also gain a new – and sustainable – competitive advantage.

To see how Earnix is helping insurance companies enhance their combined ratios, improve underwriting performance and develop highly effective personalisation programmes, visit earnix.com today.

by Andrew Collins, Head of Business Solutions Insurance, International at Earnix

INDUSTRY VIEW BY EARNIX

Does Insurtech really deliver?

Insurtech full-stack carriers struggle to find a sustainable business model, but insurtech is more than start-ups and D2C

Insurtech has come of age. Over the past few years, around $60 billion has been invested in start-ups focused on insurance innovation. The variety of these should remind us that insurtech is more than online distribution. Even insurance incumbents – traditionally considered averse to change – have recognised the relevancy of technology and data for sustaining their competitive position. In the past 14 months, we have seen players such as Allstate and Travelers sharing their commitment to innovating the way risks are assessed, managed and transferred. Insurtech is definitely more than start-ups.

I previously wrote about the journey of Archimede SPAC and Net Insurance, highlighting that it has kept its promise.

We have heard concerns about the insurtech trend, mainly driven by the falling evaluations of the listed US full-stack insurtech carriers. I have been pretty critical over the years about their approaches and financials, aiming to stop insurance innovation professionals from adoring the wrong idols.

A player such as Lemonade is able to fascinate the minds of many insurance professionals by talking about giving back to charity, behavioural economics and business models based on charging a fixed fee. This has allowed it to become the insurtech poster child and represent the foundation of its highest evaluation at almost $10 billion in the first months of 2021.

Instead, the company today has a cap of around $1.2 billion, with around $1 billion in cash among its assets. Many of the aspects – welcomed with enthusiasm from commentators – seem like great tales, but they had a small business impact. Back in 2018, I investigated its iconic ‘slice of pizza’ mechanism. Now, it is interesting to observe its financials.

Lemonade has written almost $370 million in premiums (showing 42 per cent growth from 2020) with a combined ratio (gross of reinsurance) above 180 per cent in 2021. This means that, for each dollar of premium paid by the client, its risk transfer approach cost more than $1.80.

Back in the day, Lemonade’s mantra was about some behavioural economics mechanisms that should contain their claims: “Knowing you’re not in conflict with your insurer, and that you embellish claims at the expense of a cause you believe in, may change your behaviour, too, setting off a virtuous cycle. Ultimately, we’re after a new Nash equilibrium, one where aligned interests breed trust, resulting in a product that is inexpensive, hassle-free and lovable.”

Its policyholders’ behaviours don’t seem positively modified by the above mechanisms. For each dollar of premium, claims cost 90 cents, including the loss-adjustment expenses, which is far higher than the market average. To acquire this business, Lemonade has also spent almost 60 cents in marketing for each dollar of premium, and all other costs added almost 40 cents.

Lemonade’s incapacity to disrupt the insurance sector, or even change it a little bit, should not lead to any reasoning about insurtech capability successfully delivering its promise.

I believe insurtech approaches have a strong potential for improving the way risks are assessed, managed and transferred. There are many different start-ups and incumbents’ initiatives out there that are showing great returns on the innovation investment and can be used as inspiration.

To find out more, subscribe to the monthly newsletter Insurtech Facts & Figures

By Matteo Carbone, Founder and Director, IoT Insurance Observatory

Re-designing from the customer up: how to transform insurance with digital technology

Ross Sinclair, Founder and Chief Executive at EIP Limited

Insurance is being transformed through new digital technologies, with customers as well as insurance companies benefitting.

Traditional insurance companies have a reputation for cumbersome customer sign-up processes. And while innovations in technology can provide a more streamlined customer experience, there is often scepticism around “insurtech” and the role that technology plays in the sector.

Ross Sinclair, CEO and Founder of EIP Ltd, believes that insurtech can deliver a customer experience that is simple, efficient, dependable and hassle-free while at the same time driving higher margins and more efficient operations for insurance companies.

The insurance industry has undergone major change over the past decade. Companies are offering a far broader range of financial services to their customers. However, physical retail channels are disappearing, something that has been made worse by the pandemic. This means that they often don’t have the platforms to offer these new products to their end users effectively.

Negative consumer perceptions

A major problem for the insurance industry is that many customers are suspicious of insurers, who they often see as hiding behind small print and with slow bureaucratic processes. But insurance companies are not uniquely at fault here. Increased regulation, intended to protect customers, has complicated the insurance purchase and, to some extent, claims processes.

There is quite often sufficient latitude within the regulations to find a customer-friendly solution to a given regulatory requirement. Unfortunately, compliance professionals in large corporates often take a very cautious approach.

In addition, the quality of claims processing varies wildly across the industry. Inevitably people remember – and share on social media - the bad experiences. Over time, a public perception has, perhaps unfairly, grown up that insurers will often try to avoid claims settlements.

All in all, many consumers feel that insurance is designed with the interests of the insurance companies, rather than the customers, at the centre.

A failure to understand risk

Traditional insurance companies are further damaging consumer confidence with their approach to assessing risk. Take the insurance premiums on mobile devices. These are often wildly inaccurate because of a traditional approach to pricing, where underwriters simply apply a higher premium on more expensive handsets, with the assumption being that these devices present a higher risk.

In reality, while the brand and model of the handset is a factor (although interestingly not the cost of the handset), the biggest risk factors are the behaviour and demographics of the user. A little old lady potentially presents a very different risk of damaging her phone than a 22-year-old scaffolder. No-one has been taking that into account, until now.

Because insurance products involve high customer volumes, and therefore high claims volumes, it’s possible to build a picture of the types of customers that are claiming: age, location, occupation, gender and so on. This data can be fed into pricing engines in real time. The customer then receives a quotation which is specific to them and based on up-to-the-minute claims data.

Pricing can be used to encourage lower risk customers to take out insurance, while high risk customers can be discouraged with higher premiums. This approach gives insurers stability of margins as well as significantly increased profitability.

New solutions for old problems

Insurtech companies such as EIP have developed software and solutions that help businesses such as mobile network operators, banks, retailers and insurers offer insurance products to their customers in more cost-effective ways.

Contextual pricing gives consumers the best price for their individual circumstances. For example, an insurance provider might choose to introduce a “safe location” discount where premiums are reduced when a customer is in a lower risk environment, such as at home.

By applying this intelligent pricing technology, customer satisfaction is enhanced, while overall profitability can be increased by more than 40%.

The claims side of insurance can also be strengthened through technology. One example is EIP’s automated claims processing that makes it far quicker and easier for the customer to make a claim and get a decision. This is now being successfully used in many countries.

However, it is possible to go further. EIP has developed an “Autoclaim” feature for mobile device insurance that anticipates the customer’s needs. Autoclaim mines the gyroscope and accelerometer in the device to detect automatically when a device has been dropped and potentially damaged.

If a device is dropped then the data is immediately used to create a draft claim automatically. The software then checks with the customer whether the claim is needed. If the policy holder says ‘yes’ then the claim is submitted, approved and the repair arranged. The whole process takes less than 10 seconds and two button presses from the time of damage.

Designing from the customer up

Of course some insurers will feel that there is a danger that this type of automation might increase the number of claims submitted. But it is important to look at these innovations ‘in the round’ rather than in isolation.

On one side, claims will inevitably increase slightly as the customer experience is massively enhanced. But balancing that are the very material improvements in programme profitability and management costs – which offset a softer claims attitude. The customer gets an exceptional experience, and the insurer gets higher margins – win/win!

The principle is to start designing insurance from the customer up, rather than insurer down.

Insurtech is transforming the traditional insurance industry, enabling insurance companies with a maturity of vision, to provide highly efficient, light touch, low cost digital journeys that are super-attractive to consumers and profitable for the business.

Insurance companies that design products and create user journeys that are centred on the customer, rather than the insurance company, will not only create competitive advantage in pricing but also build such a compelling service offer that it will crush the competition.

For more information please visit our website.

INDUSTRY VIEW FROM EIP LIMITED

Why the cost of mitigating climate change can’t be boiled down to one right number, despite some economists’ best attempts

Back in November 2019, before the pandemic began, would you have guessed how important videoconferencing like Zoom would be in people’s lives just a few months later?

That’s the kind of challenge economists face when they try to put a single number on the long-term cost of mitigating climate change or the cost of allowing global temperature to keep rising. Human behaviors shift as public policies change and new technology arrives and evolves.

I am a microeconomist who investigates the causes and consequences of climate change. When I think about the climate change challenge in 2040 and beyond, I anticipate many “known unknowns” about our future. Thus, I am amazed to read precise climate cost estimates like those recently published by economic consultants McKinsey & Co.

McKinsey pegs the global cost of transitioning energy and other sectors to net-zero emissions by 2050 at US$9.2 trillion a year. The insurer Swiss Re has estimated that doing nothing will cut global GDP by as much as 14 per cent, or about $23 trillion, by 2050.

Numbers like these are widely used to encourage action by governments, companies and individuals. Economists agree that climate change, left unchecked, will harm economies. But these estimates are produced using formal models that feature many assumptions, any one of which could throw off the accounting in a big way, leaving the estimates either wildly high or low.

While people might think they want “precision,” precise predictions raise the risk of conveying too much certainty in a constantly changing world. Here’s what goes into climate economic models and why certainty isn’t an option for future cost projections.

The prediction challenge

Climate economic models seek to answer several prediction questions, such as:

“How will the performance of the world economy be affected if we enact a carbon dioxide tax today?” The answer to this question helps us to understand the “cost of taking action.”
“If the entire world does enact this carbon tax, how much will greenhouse gas emissions decline by in each subsequent year?”
“What will we gain economically by reducing our greenhouse gas emissions?”
“What will be the economic and quality-of-life impact if we do nothing and just allow greenhouse gas emissions to rise under ‘business as usual’?”

To answer these complex questions, climate economists make a series of assumptions that are “baked” into their mathematical models.

Known unknowns

First, economists must predict the world’s average income per person for each year in the future.

Macroeconomists have faced challenges predicting the timing and duration of recessions. Predicting future economic growth over the course of 30 or 40 years requires predicting how the quantity and quality of the world’s workforce and our technology will evolve over time. Predicting the world’s population growth is also a challenging exercise, as increases in urbanization, women’s access to education and improvements in birth control are all associated with reductions in fertility.

Second, they must make an informed guess about what technologies will exist in the future concerning our sources of power generation and the energy we use in transportation. If they can estimate the future world population level, income level and technology, then they can measure how much extra greenhouse gas emissions the world produces each year.

Third, they use a climate science model to estimate the extra climate change risk caused by the production of greenhouse gas emissions. This is typically measured by the increase in the world’s average surface temperature.

Fourth, they must take a stand on how our future economy’s production will be affected by rising climate change risk. Ideally, these models also tell us how releasing more greenhouse gas emissions increases the likelihood of disaster scenarios.

By combining all of these equations with their own respective assumptions, a research team generates a single number such as: The world will face $23 trillion in damages due to climate change if we take no serious actions to mitigate emissions.

The ‘art’ of predicting future emissions

Economists estimate future global greenhouse gas emissions by multiplying the predicted global gross national product – the total value of goods and services – by the average emissions per dollar of gross national product.

If the world succeeds in ending fossil fuel use, this latter figure could be close to zero. The innovation and deployment of low-carbon technologies – think electric vehicles and solar farms – can significantly shift the costs and benefits that economists are trying to quantify.

Many factors determine this path of technological advance, including investment in research and development. International politics also don’t always factor into climate economic models. For example, if China chooses to become more insular, will it increase its coal consumption because the nation is endowed with coal? Conversely, could China choose to use its powerful state to push the green tech sector to create a booming future export market that greens the world’s economy?

Forecasting future climate change impacts

Economic mathematical models boil down the impact of climate change into a single algebra equation called the “climate damage function.” In my book “Adapting to Climate Change,” I provide several examples for why this function is continually changing and thus is very difficult to predict.

For example, many companies are developing climate risk ratings systems to educate real estate buyers about the different future climate risks specific pieces of real estate will face, such as wildfires or flooding.

Suppose this emerging climate risk rating industry makes progress in identifying less risky areas to live, and zoning codes are changed to allow more people to live in these safer areas. The damage that Americans suffer from climate change would decrease as people literally “move to higher ground”.

The confident climate modeler cannot capture this dynamic with inflexible algebra.

Prediction under uncertainty

Climate economics models can play a “Paul Revere” role – educating policymakers and the public about the likely risks ahead. As economists build these models, they must be honest about their limitations. A model that generates “the answer” may lead decision-makers astray.

As much as everyone might like a concrete answer to how much climate change and acting on climate change will cost, we’ll have to live with uncertainty.

Matthew E. Kahn, Provost Professor of Economics and Spatial Sciences, USC Dornsife College of Letters, Arts and Sciences

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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