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Considering the pivotal role played by insurers, all data must be given accurately and all processes compliant. So, insurers must be able to count on the effectiveness and stability of their operations--and robotic process automation can help. 

Robotic process automation in insurance refers to the use of software bots to replace rule-based tasks and procedures, including onerous or repetitive manual processes like data input, report production and document updating. 

Employees can then concentrate on activities that call for emotional intelligence, logic and creativity. For a distributed workforce and remote operations, RPA, together with artificial intelligence and natural language programming (NLP), are ideal:

• Data can be copied and pasted between programs. 
• Information can be removed from emails and other documents, then transferred to a central system. 
• Front-end and back-end operations can be synced up.
• Whole workflows can be automated by integrating with legacy systems and rules engines already in place. 
• Customer service can be enhanced by automated messaging.

Automation has already been widely accepted by the banking and financial industries. It is high time for insurers to adopt RPA. 

Challenges for Insurers

Now, let's look at the major hurdles that complicate the life of insurers:

1. Regulation and Compliance

Many laws and guidelines, including HIPAA privacy regulations, PCI standards and tax laws, must be followed by insurance businesses. The consequences of disobeying are severe fines and punishments. 

Moreover, it is difficult for insurers to comply with these laws and regulations because they are frequently updated or altered. As RPA software is rules-based, it can easily comply with regulations. And changes established in one location affect all operations, eliminating the requirement for manual system modifications. 

2. Scalability and Innovation

Customers' demands for exceptional experiences and individualized services have increased. Manual procedures, however, obstruct growth and innovation by causing delays and bottlenecks. 

By integrating data from several systems at the business process level, RPA bots can automate entire workflows. This makes it possible for smooth information exchange, improved coordination and increased workflow effectiveness. This gives the insurer more room to grow and explore the opportunity for product innovation. Innovative companies have already introduced fresh goods and services, including interactive consumer portals, on-demand quotations and policy management apps. 

Agility is essential for organizations undergoing digital transformation to adapt to a business and technological environment that is changing quickly. It is more important than ever to deliver on and surpass organizational expectations using a solid digital mindset supported by innovation.

3. Customer Experience

A poor client experience can damage a company's reputation. Sadly, clients today do not excuse agents for a "poor day" or a "sick leave." In the age of mobile insurance, consumers are constantly searching for the ideal solution with the highest level of loyalty, dependability and transparency.

Because RPA automates tedious processes, people can personalize and improve the customer experience, while producing a more contented workforce.  

See also: The 5 Top Trends in AI and RPA

4. Cumbersome Data Management

RPA improves the efficiency of data operations, not to mention making them quicker and error-free. A McKinsey study found that RPA for insurance may bring down the data processing time by 34%.

The processing of data is ultimately far more effective, and less bored employees can engage in complex activities and be more productive. 

5. Cutting Cost & Errors

According to research by Capgemini, RPA can enhance productivity in insurance companies by at least 50% and reduce turnaround times for services by 80%.  

The bots reliably identify risks, identify even the smallest mistakes in data reconciliation and insurance periods, verify claims, run background checks automatically and perform claim verification. Companies are exempt from fines because the bots update compliance policies on a regular basis. 

Insurance Industry Seals Operational Excellence with RPA 

A sizable number of large and mid-sized insurance companies still need to transform their existing systems to achieve superior customer experience and operational excellence, even though leading insurers have already implemented automation solutions across HR, finance, IT and other departments.

The climate crisis has swept across the globe, forcing companies to adapt. “There is nothing natural about the new scale of these disasters,” UN Secretary-General Antonio Guterres said. “Floods, droughts, heatwaves, extreme storms and wildfires are going from bad to worse, breaking records with an alarming frequency.” The UN’s United in Science says greenhouse gas concentrations are at record highs and fossil fuel emission rates are now above pre-pandemic levels after the lockdown trough. The climate crisis will continue to escalate, bringing with it damaging and unpredictable weather patterns.

Climatewise, a global network of 29 insurance industry organizations based at the University of Cambridge, warns that the disparity of uninsured or underinsured assets has quadrupled over the past 30 years, posing considerable risk to society. Data shows that if more people are insured at the time of a disaster, the recovery is quicker and less money is taken from the taxpayer. AI can be an ally in narrowing the protection gap and the answer for insurers wanting to stay ahead of the climate threat.

Storms, floods and fires caused $260 billion in losses in 2022. With uninsured losses come large bills that customers, businesses and governments must pick up. As insurers tighten their rates in the areas affected most by climate disasters, insurance premiums have risen to unaffordable levels for low- and average-income customers. Ironically, these are likely to be the same people most affected by climate change. Thus, the protection gap – the difference between total losses and insured losses – widens.

AI Is an Insurer’s New Best Friend

Artificial intelligence is already used daily within our personal and private lives, for example in voice assistants, image recognition, weather monitoring, shopping, online banking and healthcare. AI and deep machine learning also have the potential to make significant contributions within the insurance industry, making processes more accurate, secure and efficient.

Through AI, insurers can price competitive premiums and personalize policies to customers. Artificial intelligence can be used to collect large amounts of accurate, real-time data. With this high-quality data obtained from aerial imagery, properties can be accurately 3D-mapped at scale. Insurers can then use this information to predict the likelihood of a claim being filed as well as the likely cause of the claim. For example, when pricing a home insurance policy, information about the property’s location, roof condition, risk of flood and such like, can help insurers set premiums based on defining criteria. Insurers would be able to predict and prevent claims before they happen, ultimately saving time, money and resources.

AI can also make claims processing quicker and easier while maintaining efficiency and accuracy. Currently, application processing and check distribution takes weeks or even months, with teams needing to physically inspect damages. AI could automate the process to hours or even minutes, for example by using footage from street and garage cameras to reference vehicle damage after an accident, prompting customer loyalty. For those uninsured or under-insured, the reduced transaction costs of automated technology also mean that AI can make insurance more affordable and easy to use.

Extended reality can be used to automate underwriting by leveraging virtual and augmented VR. Insurers are able to virtually inspect homes both before issuing a quote and after a claim is filed. Rich and precise data allows companies to perform online adjustments to claims rather than physically inspecting damages, painting a more accurate picture of the value of the claim. This would reduce the number of employees needed for time-consuming processes around claims management and payout.

Insurers also have the opportunity to help communities prepare for climate disasters and better equip them with the tools and knowledge needed to evolve with the climate crisis. Using collected data, companies can advise communities against bad planning decisions, such as erecting high buildings near coastal flooding zones, or by encouraging building resilient infrastructure that will mitigate rising sea levels. Insurers can also contact property owners directly, advising them on their property’s current state and hazards. By reaching out to customers before a disaster, highlighting, for example, problematic trees and foliage growing on or around their property or a nearby wildfire risk zone, insurers have the ability to thwart disasters, saving time, money, resources and, in extreme cases, lives.

The cost of insurance fraud is more than $309 billion a year – nearly $1,000 for every American. Data collected and curated by AI can spot repeated behaviors and trends and can help insurers detect fraud and prevent risk. It can spot abnormalities in data as well as false information that customers use to get bigger claims payouts and lower premiums. Similarly, AI’s ability to can help insurers identify inconsistencies and can draw attention to fraudulent claims, preventing unnecessary payouts and drawn-out investigations. Artificial intelligence would also assist in the learning of customer habits, making valuable recommendations by simplifying how products are categorized and promoted.  

See also: Time to Embrace AI in Climate Change Fight

The Importance of High-Fidelity Data: Climate Change and Beyond

Reliable, top-quality data will give insurance companies the competitive edge they need to survive the climate crisis. The quality of AI models will only be as good as the data on which it operates. AI systems that use outdated historical data are ill-equipped to assist insurers with the ever-evolving threat of climate change. Rich data is therefore essential for AI to operate effectively and be accurate enough for insurers to use. Thus, one of the biggest trends we can expect to see is the dramatic increase in data veracity and a move toward making data more accurate to ultimately allow better business decisions.

The gathering and refining of in-depth, high-quality data can help insurers set risk, determine premiums, develop products, triage claims, prevent fraud, enhance customer loyalty and decide on what markets to target. AI can assist insurers in adopting a whole-system planning approach when responding and preparing for a climate emergency, fortifying the industry against this systematic threat.   

Insurers, governments and businesses must work in cooperation to protect both society and the economy from the adverse implications of climate change. Thus, organizations should be aiming for cross-sector collaboration, building a system of risk management fueled by intelligent data. The insurance industry is on the verge of a tech-driven shift that relies on the sharing, using and refining of data and AI resources.

Extreme weather caused 18 disasters in the U.S. last year costing $165 billion in damages, $10 billion more than the 2021 total, making 2022 the third-most-costly year since records of major losses began in 1980.

Insurers need deeper, more detailed insights into individual properties to underwrite accurately. Advanced technology can provide high-resolution satellite imagery of residences, allowing for desktop analysis of individual properties. By applying machine learning and AI technology to this aerial imagery, insurers can assess risk factors and, when necessary, quantify damage with increased efficiency and speed. High-resolution aerial imagery has the potential to bring data to life, helping insurers to competitively and sustainably underwrite properties.

Climate Change and Insurance

Using aerial imagery analytics within insurance has a range of benefits. It can be particularly useful when surveying properties that are hard to access, for example rural or coastal residences. Risk factors can instead be measured at a distance. High-quality images can contribute important property condition elements to quoting and renewal underwriting, ultimately saving businesses time, money and resources.

One major risk factor that influences the underwriting of a property is roof condition. Roofs are particularly vulnerable to constant wear and tear, as well as potential hazards, such as overhanging trees. These are also major impact areas during damaging weather catastrophes such as storms, tornadoes and hurricanes. Roof claims caused by severe weather are sometimes filed a year or more after the storm has passed. As a critical underwriting attribute, it is essential that the assessment process is accurately informed to ensure the client is completely covered for any damage. Current manual methods of assessing risk are outdated, relying on historical data that is often inaccurate and inefficient. However, with the assistance of modern aerial imagery technology, more robust data for each residential property can be ascertained. Damage from extreme weather has individual consequences for specific locations, thus, more granular data such as building elevation and soil conditions also need to be taken into account.

Aerial imagery can highlight potential hazards and capture how the roof condition is changing. An aerial perspective can also identify expensive attachments, like solar panels, as well as differentiate various materials, from shingle, tile, concrete or metal. The gradient, shape and surface area or the roof can also be pinpointed. This information can be combined with location-specific weather data, roof replacement cost estimates and claims insights on surrounding residences to help manage risk and ensure accurate underwriting. As a critical property underwriting attribute, aerial imagery can be used to collect specific and unique data points on the roofs of properties to ensure full and competitive insurance coverage.

Extreme weather events are categorized within the insurance industry as "low-probability, high-consequence," but the probability is only rising, meaning so, too, are the consequences. As threat levels dramatically increase, underwriters are under more pressure than ever to undertake new approaches and better modeling to underpin their decision making.

Another key factor insurers must consider when underwriting properties within a landscape dominated by climate catastrophe is wildfire risk and damage. Between 2021 and 2022, wildfires accounted for over $11.2 billion in damage across the U.S. Areas in the West, such as California, Nevada and Arizona, which suffer from long stretches of arid climate and little rain, are especially susceptible to wildfires. Wildfire damage is particularly hard to predict and measure as it is incredibly data-intensive and the risk profile can vary widely.

What is therefore required is up-to-date, location-specific data points that survey the topography of the landscape to identify areas of wildfire susceptibility. Action points can then be taken to reduce risk of damage, such as removing or trimming thick vegetation. Vegetation is identified on aerial visuals by its relatively consistent shape, color and texture. As a result of its low height, vegetation does not portray any shadow, and its variance in texture makes it detectable from the sky.

Aerial imagery can also reveal property elevation, as well as characteristics such as the defensible spaces between structures, vegetation and potential fuels in high-risk fire hazard areas. By using this data, insurers can not only assess which properties are prone to fire damage and are susceptible to wildfire risk, they can also understand which properties would need to be evacuated in the case of a climate disaster. This can be reflected in both their underwriting as well as in community disaster plans and future building regulations.

See also: Global Trend Map No. 4: Industry Health

Accurate Data Collection and Aerial Imagery

Moody’s Risk Management Solutions estimated there were $67 billion in insured losses from Hurricane Ian, which hit Florida at the end of September. Before the storm, the Florida insurance market was already in a precarious state following a recent spate of insurance company insolvencies over the past couple of years. Earlier in the year, six Florida property insurers had already declared themselves insolvent amid widespread financial problems within the industry.

Furthermore, Hurricane Ian was the most devastating hurricane since Hurricane Katrina in 2005. Officials have said this was down to a lack of preparation, not serving evacuation orders fast enough and a suboptimal approach to defending key infrastructure. This is why better data analytics and modeling is needed: so both insurers and their customers can better prepare for climate disaster through more accurate predictions about their properties.

In delivering damage reduction action points, insurers, reinsurers and brokers can sustain insurability and reduce losses for themselves, their customers, wider communities and the insurance industry as a whole.

Aerial imagery is one of the ways in which insurers can raise the standards of reliable, cost-efficient property data. It has the potential to enrich underwriting through identifying each properties unique risk factors.

By undertaking an integrated approach, combining high-quality images with expert interpretation, insurers can more confidently assess risk and underwrite profitably and sustainably. In the light of the climate disaster, insurers, now more than ever, need to adapt and update their methods of operation and delivery to keep the insurance industry afloat.

The number of ransomware attacks declined substantially in 2022, leading to a 40% decrease in the payments from victims. Whether a short-term trend or indicative of a permanent change in cybercrime activity, fewer attacks and better-prepared organizations have shifted the focus in cyber insurance. 

While ransomware claims may be in a lull, cyber insurers are finding themselves busy with a new wave of cyber claims stemming from class action lawsuits and enforcement actions due to data privacy violations. Once seen as a low-risk cyber coverage grant, cyber underwriters and claims teams are now scrambling to revise their policy language (and rates) to address the growing data privacy risks in their books.

Over the past six months, there has been a wave of data privacy lawsuits and enforcement actions on several fronts hitting cyber insurers’ policies:

Hospitals inadvertently share patient data with Facebook via the “Meta Pixel.” Examples include Dignity Health and UCSF in California and Advocate Aurora Health in Illinois. 

Retailers collect and share consumer data via online session replay tools. Examples include Zillow, Lowe’s and Expedia, sued in September. 

Financial services providers are sharing data with the Meta Pixel on tax preparation websites. 

Online news, sports and quick-serve restaurants share customers’ online video-watching behavior with social media networks. Examples include the recent Chick-Fil-A lawsuit (January 2023), as well as CNN and NBA lawsuits in 2022.

Why Are Cyber Underwriters On Alert?

Beyond the significant legal expenses related to the allegations and regulatory fines levied by state AGs, in some instances the privacy violations have escalated to reportable HIPAA breaches, which bring additional notification and remediation costs. For example, BayCare Clinic in Wisconsin recently informed the U.S. Department of Health and Human Services about a breach involving 134,000 of its patients who had been affected by online tracking technology. BayCare said the trackers potentially sent patient information to third parties, including the dates, times and locations of scheduled appointments; the type of appointment or procedure; patients' proximity to a practice location; and their insurance information.

Similarly, in 2022, the Advocate Aurora Health online privacy violation led to a reported breach of 3 million patients’ personal data. The health system of over 500 healthcare facilities in Illinois and Wisconsin reported itself to the Department of Health and Human Services on Oct. 14, saying the breach involved unauthorized access or disclosure. 

Beyond healthcare, insurers are also seeing claims activity among media networks, retailers and financial institutions, as allegations of violations of the Video Privacy Protection Act and state wiretapping laws are growing nationwide.

In light of these growing claims, some cyber underwriters are adding exclusions for coverage. Others, eager to build their customer relationships, are looking for opportunities to underwrite with greater intelligence about these privacy risks. 

See also: How Insurance Can Halt Ransomware

As With Ransomware Response in 2015, a Privacy Economy Is Growing Today

Insurers, attorneys, regulators, tech service providers, forensic firms, PR agencies and consultants rallied as the cybercrime wave grew over the past 10 years. Insurtech innovation was also fueled by the growing cyber threats.

Today, we see similar activity in the data privacy ecosystem. Federal regulators and state legislatures are implementing new laws, stimulating the plaintiffs bar to pursue class action lawsuits. This, in turn, drives insurers to create coverage for these new risks that their policyholders will face. Subsequently, tech innovators are creating tools to help provide intelligence to insurers during underwriting, while also creating better software for companies to not only comply with the new laws but mitigate the risk on their end. All the while, everyone in the "privacy economy" is seeking to learn more about the risks and how to protect themselves.

Building Greater Privacy Risk Intelligence 

With each new data privacy lawsuit and regulatory enforcement, cyber insurance underwriters are going to develop new language for their cyber policies to help protect their policyholders (and their loss ratios). Insurers, having learned from the ransomware and cybercrime waves of the past, are building intelligent underwriting tools that can help them assess privacy risk prior to issuing coverage and, likely, will be adding new tools to help their clients mitigate risks, as well.

Cyber threats and cyber insurance are in constant evolution. What started as "data protection" for a business’ network security issues, evolved to cover HIPAA regulatory risk, which then quickly evolved to cover broader customer data breaches, which then evolved to cover cybercrime and business interruption. Behind the rapid growth of cyber insurance has also been a wave of federal and state government regulations pushing companies to take responsibility for cybersecurity, as well as insurers to provide a backstop. Today, the new wave is focused on driving corporate responsibility for online privacy.

See also: Risk Barometer for 2023

The Online Privacy Revolution

In 2023, the regulatory environment is heating up again with new laws (GDPR, CCPA, FTC enforcement actions, OCR guidance and four other newly enacted state laws) around protecting customer data and online privacy. This is driving insurers to consider how to best provide cover for insureds while also mitigating risks. Perhaps this year will be seen as the start of an online privacy regulatory revolution. Not only is the regulatory environment ripe, but consumers are also more aware due to constant spam, scams, tax fraud, cyberbullying and identity theft. 

Already in five states (California, Colorado, Connecticut, Utah and Virginia), new data privacy legislation has been enacted. Huge fines have been levied against Google and Facebook in Europe for privacy violations. And you can’t watch a major sporting event on TV without at least a few ads promoting data privacy as a key reason to buy their phone, insurance, credit card or broadband subscription. 

The insurance industry has been instrumental in shaping how companies around the world adopt new technologies and practices to fight ransomware and cybercrime. It’s time now for the industry to take up the cause for online privacy and help companies evolve how they safeguard their customers’ personal data.

With extreme weather events such as Hurricane Ian and its $112.9 billion of damage, and more than 7,490 wildfires recorded just in California last year, climate change is making it increasingly difficult to accurately forecast property damage risks. Insurers using obsolete, patchy or outright inaccurate data are bound to suffer significant losses.

This, in turn, has contributed to a sharp rise in premiums. Homeowner policy premiums rose 12% across the U.S. from 2021 to 2022.

Insurers can no longer rely on historical trends, so they are resorting to virtual models to simulate the impacts of future risks: The so-called digital twins are digital counterparts to real-world physical objects or properties. They can be used by insurers to assess their exposure and continue to underwrite profitably in the era of climate change.

Insurers may not be able to see into the future, but they do have access to increasingly rich and up-to-date property data. By feeding these insights into a digital twin simulation, insurers can identify the potential extent of damage to a property under different weather conditions.

Digital twins model a range of future weather-related scenarios 

The ability to simulate real-world conditions to ascertain how physical systems or buildings would be affected under various circumstances is transforming a range of industries, from large-scale construction projects to cybersecurity. However, more than anywhere else, digital twins are making their mark in the insurance industry.

By creating comprehensive property models with reliable data points, insurers can determine the risk of damage in severe weather conditions. With high-quality insights obtained from aerial imagery, properties can be 3D-mapped at scale. Using artificial intelligence to extract and make sense of large datasets, insurers can obtain enough information to create high-fidelity digital twins of almost any kind of physical structure, regardless of size or condition.

It then becomes possible, for example, to simulate high winds and low pressure zones and evaluate potential damages at different wind speeds. This can be particularly useful for insuring areas frequently affected by hurricanes. Similarly, the risk of properties that insurers already know are in flood or wildfire zones can be modeled under different weather conditions.

The success of the insurance industry hinges on the availability of rich and accurate insights. The "virtual data" collected from digital twin simulations can then be used for a range of critical processes such as underwriting, claims processing and fraud detection, without the need to base damage predictions on previous, potentially outdated or irrelevant historical trends.

See also: Time to Embrace AI in Climate Change Fight

Digital twins will become more important than historical risk trends

The impact goes beyond just underwriting. Digital twins can help accelerate claims processing by reproducing the scenarios or circumstances behind a claim, such as conditions of damage. Moreover, the ability to simulate an event can be valuable when combating insurance fraud, by rapidly verifying the accuracy of a claim.

For example, if a homeowner intentionally sets fire to a section of their property, using a digital twins simulation the insurer can identify if the fire spread from an area where an electrical fault may have occurred, or alternatively if the cause was unnatural. All in all, digital twin capabilities reduce the time and cost of producing insurance products. When used in tandem with other technologies such as aerial imagery and advanced AI, insurers can produce digital twins at scale. This is particularly useful for insuring against large-scale natural disasters such as flooding and wildfires, for which large amounts of property intelligence are outdated due to erratic weather patterns.

Digital twins are one of the best examples of technology working to counteract the adverse consequences of climate change. By using accurate, high-quality data points to create virtual representations of physical structures, insurers can improve their risk evaluation and decision-making processes.

The ability to simulate different weather conditions against these high-fidelity replicas, also allows insurers to make better predictions and recommendations to clients. It’s only a matter of time before digital twin data becomes more important than historical data for insurance risk management.

Digital twins provide insurance companies with insights that help them stay ahead of the competition and better navigate the challenges posed by extreme weather events. Those without digital twins will find it very difficult to stay afloat in a fiercely competitive and rapidly changing industry.

By 2027, experts expect the surety industry to reach a valuation of more than $25 billion.

However, for the surety market to continue growing, it needs to digitize and embrace the latest technology — including blockchain technology.

This article explores the future of blockchain in the surety industry, discusses the pros and cons of digital bonds and explains how to get bonded and insured digitally. 

Why Does the Surety Industry Need to Digitize?

In response to the COVID-19 pandemic, businesses in all industries were faced with a sudden demand: Go digital or get left behind.

The surety industry has lagged on digitalization. In fact, bond execution still involves antiquated technology like typewriters and fax machines.

The slowness to adapt has to do, in part, with the exclusivity of the surety field. Surety bonding has always been a niche area within the specialty insurance and property and casualty sectors. 

Many people don’t know how to get bonded and insured, nor do they understand the difference between bonding and traditional insurance. This uncertainty has led to a slow transition to digitalization. 

What Do the Experts Say?

With more and more businesses going digital these days, it only makes sense for the surety industry to follow suit.

In an article published by Surety Bond Quarterly, Michael Lischer, the VP and director of surety at IMA and chair of the NASBP Automation & Technology Committee, asked why bonds can’t be issued digitally. He said it’s especially surprising that this isn’t an option because so many other processes, from buying cars to borrowing money, can be done online. 

Lischer acknowledged that, in the surety industry, obligees must have faith that their bonds are authentically and appropriately issued. Paper bonds provide this feeling. However, he believes digital bonds can, as well — particularly with the help of blockchain technology. 

See also: Is Blockchain Still on Track?

What Is the Role of Blockchain in the Digitalization of Bonds?

For those who are hesitant about incorporating blockchain technology into the surety industry, a lack of understanding about how blockchain works may be part of the problem.

Blockchain technology gets its name from the fact that it stores data in blocks, which are linked in a chain.

Blockchain allows for exceptional data consistency because no one can delete or modify the chain without a consensus from the entire network. The system features built-in tools and mechanisms to stop unauthorized transactions for maximum consistency and security. 

Blockchain is already used in many fields, including retail, to create unalterable ledgers for order tracking, account management and payment processing. 

When speaking about blockchain technology in the surety industry, Lischer noted that blockchain bond solutions have the potential to be more secure than traditional processes because of the automatic authentication process.

How to Get Bonded and Insured With Blockchain Technology

If blockchain technology were applied to the surety industry, the process would start with an electronic record.

A bond specialist would create the bond and share it with all parties. Any changes made to the bond would automatically be added, and then everyone involved would have access to and could see a history of the changes throughout the entire process. 

Blockchain technology eliminates the need for typical and inconvenient elements of the bonding process. Examples include wet signatures, raised seals and acknowledgments from notary publics.

Incorporating blockchain technology can also increase the speed at which bonds are issued and reduce the costs of issuing hard-copy bonds.  

In the same article from Surety Bond Quarterly, Patrick Schmid, the VP of The Institutes’ RiskStream Collaborative, explained that the digitalization of the surety industry could also transform insurance regulations and compliance.

Schmid noted that blockchain creates the possibility for regulators to monitor permissioned insurance information in real time. This real-time monitoring would also help when it comes to verifying information, including bonds and powers of attorney.

How Do Digital Bonds Reduce Financial Risk?

Increased security is one of the most significant benefits of incorporating blockchain technology — and digitalization in general — into the surety industry. 

Every year, fraud in the insurance industry costs U.S. consumers approximately $80 billion, according to the Coalition Against Insurance Fraud (CAIF).

The insurance industry leaves much room for error, which increases the risk of fraud. If insurance and surety companies could store claims and bond information on a blockchain, they would have an easier time identifying and stopping suspicious behavior before fraudulent activity can occur.

Blockchain technology would introduce several elements that reduce fraud and increase financial security, including efficient documentation authentication and the creation of a permanent record of all transactions.

Because blockchain technology is encrypted, participants in the surety issuing process can trust that transactions are secure and authentic. Encryption protects everyone’s privacy and helps to minimize confusion.

In an interview with Risk and Insurance, Brian Scarbrough, a partner at Jenner & Block, explained that encryption also eliminates the risks of multiple networks being hacked at once. 

Users trust an entire network of participants instead of putting their faith in one centralized party. This approach also encourages honesty because all parties must vote on a transaction before it’s added to the blockchain. 

See also: Blockchain: A Hammer Looking for a Nail?

What Are the Downsides of Going Paperless?

The decentralized nature of blockchain technology naturally increases security. However, it can also be challenging to maintain.

This issue is even more likely to occur if a single organization creates its own blockchain. The computers running the network could end up centralized, which defeats the purpose.

Many people also have negative ideas about blockchain. They don’t understand it and, therefore, don’t trust it, meaning they might be hesitant about moving forward with digital bonding and working with a bond specialist who offers this option. 

When Will Blockchain Become the Norm?

Blockchain certainly has the potential to revolutionize the insurance industry and the surety industry, specifically.

Several businesses are currently trying out blockchain technology to serve their clients better. However, it will take time before blockchain becomes the norm.

As more professionals successfully issue digital bonds, others will likely follow suit and develop more trust in the technology.

KEY TAKEAWAYS:

--Your brand must show customers who you are by connecting platforms, ecosystems and data -- not showing them the internal constraints you face. Every experience must feel natural.

--Applying the brand vision from three lenses -- execution, ecosystem and customer -- will let insurers see the customer properly and will let the customer see insurers accurately and positively.

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I am a book lover across a wide range of genres and keep book stores busy. Post-pandemic book sales are experiencing a slight decline, but one particular format of book is selling better than ever — the e-book. In fact, 30% of readers chose an e-book instead of a print book in the last year. While that number may not seem like a majority, it’s a 43% rise within the last decade.

For some people, e-books represent convenience. There is no trip to the bookstore. You can purchase it 24/7. You can search the “shelves” more easily, and (if you’re using Amazon) you know that some algorithm somewhere is placing curated choices directly in front of you, based on your previous purchases.

Because e-books are digital files, they are accessible through multiple channels. This includes channels where the purchase is not the primary option. Nearly all public municipal libraries have partnered with digital providers such as Hoopla and Overdrive to allow checkout from the library’s catalog. For college students, the backpack has been getting lighter, with digital edition rentals available through common textbook providers and Amazon. Google has also been compiling rights-free titles to keep historical volumes alive for research and fun.

The brand, the brand culture and customer engagement.

In every case, e-book distribution is heavily tied to the brand — both the publisher and the distribution brand. This is beginning to be the case in insurance, as well. Insurance may be sold through a marketplace, sold as a part of another brand’s service package or even embedded invisibly in another brand’s product or service.

With books, once the distribution has occurred, it’s the responsibility of the publisher to have provided a usable title, with features such as easy search and robust interior links. The "customer journey" through the book is the key driver for retention. With insurance, the customer journey (along with price) is also the key driver for retention.

Never has the back-end insurance business been so connected to the brand. Insurers now wear their culture on their sleeves. Usability is paramount. Insurers show whether they care about their customers by the ease of the customer journey. The ideal insurance process draws customers in through multiple, easy-to-use channels, then keeps them happy through “invisible” engagement — processes and tasks that are so easy that even considering a competitive offering would seem like nonsense.

A unified vision that results in an invisible process.

In our last customer experience blog, we considered Six Technology Hurdles to Insurance’s Customer-Friendliness. In today’s blog, we answer those hurdles with one vision — using technology perspectives to plot a course for improved customer engagement. Create a compelling story with customers by turning your internal operations culture into one that you are proud to show off. Connect with customers by connecting platforms, ecosystems and data. Show them who you are by not showing them the internal constraints you face. Create an “invisible” process where every experience feels natural because it was made to feel that way.

The three lenses of insurance transformation.

Applying the vision from three lenses will allow insurers to see the customer properly and will allow the customer to see insurers accurately and positively.

These lenses are:

The Execution Lens

Implementing the technologies and processes that will make it all possible.

The Ecosystem Lens

Creating an ecosystem of partners that will allow the flow of information and data to automate and improve the process.

The Customer Lens

Delivering a 360-degree experience across numerous activities, unhindered by silos.

When the transparency of the culture and invisibility of the customer experience are aligned, they both tell the story of an organization prepared for the future. Customers can then look into an insurer’s culture and grasp the planning and care, instead of trying to peer in and get a glimpse of the mess.

Non-negotiable technology: the foundations of execution

Process and technology are inextricably linked. Supporting customer features and capabilities requires a specific set of digital technologies to enable the front-end user experience. The technology approach must include strong build-implement-run capabilities and options, including the pre-integration of key solutions. For successful execution, the solution must incorporate component-based design and assembly, plus APIs and pre-integrations.

A next-gen, robust architecture enables redefined business services. Maintenance and upgrades are fine-grained and frequent, far easier to test and place into production.

Application programming interface (API) libraries make re-use and similar connections simple. Insurers should use an extensive API library, such as Majesco API Management and Majesco EcoExchange with partner solutions. Using a library, in conjunction with policy, billing and claims, creates a unified platform for integration that can be implemented with key components at any time and in a flexible manner. APIs also give insurers the ability to more easily integrate with multiple vendor systems.   

Smart insurance: the framework for ecosystem design

Workflow will drive the next generation of system improvements, and data will make it possible. A digital mindset is important — recognizing that the capture, extraction and creation of digital data is required to support workflows and analytics across the enterprise. This forms the foundation to improve business intelligence and capitalize on analytics, AI and advanced technologies.

Straight-through processing is now more possible than ever. The ability to leverage AI in both underwriting and claims is essential for consistent success. Smart data capture involves the intelligent intake of structured data sources, leveraging data pre-fill capabilities and adaptive interviews to ask only the questions required from the customer. In addition, smart capture includes the extraction of unstructured data from PDFs, forms and other unstructured data sources such as emails. Critical abilities include not only capturing structured data or converting unstructured data into structured data but also the ability to index data and route it through relevant transaction workflows.

See also: Lowering Costs of Customer Acquisition

Never-ending journeys: the customer experience that satisfies

Both framework development and technology assessment MUST be used through the lens of customer experience. Here is why:

Let’s say, for a moment, that your company is now motivated to improve the customer experience. You spend time in meetings discussing what kinds of features you may like to add to your customer dashboard. You build a case for certain elements to be added to the mix. You consider the balance between what should and should not be shown to a customer without agent guidance. Without using the customer lens, you could end up with services where hurdles and silos are still acceptable and visible.

Is the company identifying the silos, not by what they think they have in the back office but by what they know they can’t allow customers to do for themselves in the same session?

There’s almost nothing more frustrating than starting over. For the customer, switching systems or apps is like getting sent back to the beginning of a streaming movie or losing a digital bookmark in an e-book. Insurers can begin looking at their customer journeys in light of hurdles, re-keying, re-logins and do-overs. And, the more insurers use security codes and greater password constraints, the more they will need to give full access in one location.

A Customer 360 Vision unifies not only the dashboards but the data sources to provide an experience without hurdles, multiple logins and start overs. The customer doesn’t want to know that they may be accessing multiple policies, billing and claims systems for one particular request. They want the complications removed. They want a process that simply works as it should and does not have any hindrances in their way.

Figure 1: Use Case with a Customer 360 View

Would insurers rather that customers see the inner workings of how silos force them into customer service corners, or would it be better to both cover and fix insurance service issues by creating systems and processes that hide any trace of hurdles and silos? A customer 360 service vision makes its own case for new approaches to systems and data.

Staying “on brand”

There’s a commonly used term in business today — “on brand.” The idea is interesting. It forces companies to assess whether their products, services and culture fit their brand, or if maybe the organization itself needs to shift to allow an internal “re-branding” that will fit the customer. Does your back-office brand fit the brand culture that you wish to portray? Are you able to engage the new generation of insurance customers? Is your organization growing uncomfortable with being able to stay on brand as an insurer with competitive offerings in the industry?

Now is the time to assess and shift. Grow the brand that will meet today’s and tomorrow’s needs by creating a brand-ready, brand-capable, brand-new digital customer experience.

Be sure to read Core Modernization in the Digital Era, or watch Insurance Growth & Opportunities — How Next Gen Technology, Products, Data, Channels and Ecosystems Are Driving Change in the Face of Increasing Market Changes.

You’ve just visited the grocery store, and it’s likely that you made a choice at the checkout. Did you scan and bag your items, or did you let a cashier scan and bag them for you? The choice may depend on just how many groceries you purchased or how you feel about your store’s tech level.  

Even now, though, it’s clear that there is a next step or two to be made in grocery store technology.

Where is the line where you simply feed your groceries onto a conveyor and they are automatically scanned and bagged? Where are the technologies that run and pick everything off of the shelves for you and deliver them to a waiting area or to your home without a delivery driver? Home deliveries are becoming more automated. Uber Eats and Postmates are already using robot deliveries in test locations with high population densities. 

Which raises the question: Just how far can technology take any industry? In many cases, technology makes its case so profoundly that we might call it a no-brainer — like making deliveries from a restaurant with no driver, no fuel, no insurance, no parking issues and no delivery tip. 

Underwriting technology for commercial insurance is also a no-brainer. When we look at all that today’s tech-enabled underwriting can accomplish and how it reallocates crucial resources to the brainy tasks, it makes its case. It is an “elegant” solution to so many commercial and specialty insurance challenges that it deserves an immediate look and quick implementation.

Drawing on joint research efforts between Majesco and Strategy Meets Action, let’s look at the benefits of today’s optimal underwriting technology to see how it produces real insights through collaboration.

The quest for insights, integration and intuitive workflow

Insurers seek to enable underwriters to focus on complex risk assessment, portfolio management and relationships with agents and brokers. Insurers need to improve the speed of underwriting, making it easier for brokers to do business with the company, and ultimately need to improve risk selection and profitability — not just for each policy and product but across the whole spectrum of business and geography. Insurers need to make complex concepts into simplified insights that leverage the underwriter expertise.

Digital thinking and platform vision

How can underwriting become a seamless hub of information, fed by the best data management and analytics practices available today? Which advancements make a real competitive difference — moving commercial and specialty insurers from capable to innovative? What does this environment look like?

A next-gen framework must support the workflow of relationship management, transaction processing, collaboration with brokers and portfolio management of the entire book.

It must also support processes to leverage new data, new models and new analytics to garner deeper insights, based on the three key attributes of digital thinking:

Intuition

In underwriting, the user experience should be tailored and personalized. No two underwriters are the same. The new underwriter has a completely different set of tasks compared with an underwriting veteran. As underwriters grow, their roles may change and shift. A digital underwriting platform will tailor its processes and workflow based on underwriter specifics to provide guided and balanced experiences. 

Today’s digital underwriting platform also must enable communication and collaboration among underwriters, brokers and others who may be involved in the process.

See also: Dramatic Shift in Underwriting Ahead

Integration

The first steps of automation were those made to integrate requirements data into the scoring process and to facilitate the underwriting workflow. Today, this integration is vastly expanded to contain connections with everything possible — data, collaborative communications and decisions. Application programming interfaces (APIs) need to connect with centralized data platforms to provide real-time synchronization with policy administration, rating engines, various tools and spreadsheets, analytic/predictive models, transformational technologies and new data sources (structured and unstructured).

Everything gains its power in the integration layers. Workflow becomes easier to automate. Data becomes easier to access and understand. With advanced digital communication tools, information sharing becomes more fluid and automatic, both within the company and outside its walls. There are so many valuable streams of data available today, but most are hindered by an inability to integrate the data into the current workflow.

Insights

Insights are made up of the “just-right” information presented in easily digestible views from multiple angles and layers. All relevant sources of data and analytics for the transaction, decisions and portfolio management are vital and accessible. Dashboards, alerts, business intelligence and advanced analytic tools are made available across any and all data points and through any lens: product, broker, underwriter, policyholder, market segment, region, etc.

Today’s digital underwriting provides the latest data and analytics for product/pricing/appetite and underwriting guidelines that are linked to intuitive and intelligent workflows and engines.  

Tech capabilities that advance underwriting for commercial and specialty insurance products

The technology to support new digital attributes goes beyond the basic underwriting found in policy solutions. It is more advanced and comprehensive than even the underwriting workbench of the past that was focused on workflow and process. What is required is a digital underwriting platform that not only enables today’s workflow and process but elevates the underwriting process and decision-making, as well.

This evolution of underwriting is powered by solutions that leverage:

• a digital no code/low code platform
• AI and advanced predictive analytics
• new communication and collaboration tools

This can be accomplished through a next-gen underwriting workbench that runs standalone and integrates seamlessly with other systems and data for rapid implementation and flexibility for future enhancements and upgrades.

Underwriting technology is a wise investment

Most commercial and specialty insurers are adept at understanding their customers and niches. Now is the time to pay close attention to the pains that their business customers are encountering.

When the economy is posing headaches for companies large and small, and many businesses are struggling to survive, areas of expense come under fire. It’s vitally important that insurers are seen as the protectors of business — with insurance as a high-value asset — as opposed to just a necessary expense that can be shopped around like any capital expenditure. Commercial and specialty insurers need to remain competitive by providing the accuracy, value and innovation that will keep company customers loyal. Commercial and specialty insurance underwriters need to transform underwriting into a center of support, engagement and insights, ready to contribute to cost savings for their own company and all those whom they serve.

If you think about it, this makes the decision to modernize commercial and specialty underwriting with digital and advanced data technologies a no-brainer. Every front-end improvement contributes to the insurer’s bottom line. Every step forward makes commercial and specialty products more competitive and makes insurer solutions more collaborative. For organizations that consider themselves business partners, digitally enhanced underwriting will build trust in the business relationship and protect both businesses and insurers from the unknown. Is your organization ready to take advantage of today’s next-gen underwriting platform?

To hear the latest from Majesco and SMA on Underwriting and analytics, be sure to watch Majesco’s webinar, The Art and Science of Underwriting Powered by Artificial Intelligence and Machine Learning. 

While many promises of disruption from insurtechs have fallen flat, now is the time to lean into digital transformation. On the cusp of a potential economic downturn, organizations must continue to adopt advanced solutions that foster quantifiable ROI and impact. And while there is a focus on how artificial intelligence (AI) and automation are critical when functioning with fewer resources on hand (and rightfully so), there isn’t enough focus on explainable AI. 

Explainable AI, or the ability to look inside the “black box” of decision-making of an algorithm and understand the reasons behind its predictions, is pivotal to better understanding customers, detecting fraud and staying ahead of potential legislation that has ability to disrupt the industry. Insurers are not alone in their struggle to obtain useful and unbiased data. Explainable AI paves the way for improving business outcomes and keeping companies accountable to new and emerging ethical standards. 

Mass customization through AI 

On the surface, using explainable AI to predict customer churn might seem less exciting than AI-driven telematics or using AI to configure risk models for states affected differently by natural disasters. But picture this: Your AI model alerts you that Customer X has a significant chance of taking their business to a competitor. Without explainable AI, which makes it easy to understand precisely why this is likely, your organization would have to accept defeat and watch the customer close their account once their contract was up. 

With explainable AI, it may be revealed that the customer is extremely price-sensitive, and because their rates went up due to an accident last year, they’re looking for solutions that are more within their price range. The AI arrives at this prediction from extensive first-party data, including past interactions with customer service representatives and subsequent surveys. In this instance, offering a lower rate could reduce the chance of Customer X leaving to 50%. One customer may not hold an organization afloat, but thousands of instances like this one can help keep consistent growth in economic fluctuations.

Aside from the obvious benefit to the insurer, targeted, customer-centric personalization of policies and customer service interactions contribute to a better customer experience and thus, a loyal customer base.

Detect fraud faster and improve your data

It wasn’t long ago that Geico unveiled its use of AI to speed collision estimation. Essentially, after an accident, customers submit photos of damage to their vehicles, which helps speed claims and repair processes. Without explainable AI to outline why certain damages or costs were identified, the software could cause considerable challenges if customers were unhappy with the decisions. It’s only fair for the insurer to provide details for why a claim was denied or only partially approved.

In the case of fraudulent claims, insurers need a way to quickly detect when something’s amiss. In verticals like retail, with lots of data constantly being added to systems, data can be updated instantaneously based on real-time interactions to improve AI-backed decision-making. However, this method requires a steady cadence of data to keep up with changing trends. Machine learning (ML) models predicting insurance claim fraud may be limited to adapting on a much less frequent basis, causing what is sometimes called model drift. 

This means that enterprise data, and therefore ML models, may be inaccurate for a period, until the feedback loop closes and the model is able to update. Implementing rules systems on top of ML can provide an automation stop-gap so that, until relevant data is fed into the system, rules can act as a guardrail and reduce risk for ML models when data drifts from its training distribution.

Further, the ability to analyze a model and its recommendations is crucial for identifying erroneous or biased data that should never have made it into training. Data science workflows that use explainable AI to drive upstream data improvements continuously boost the quality of their organization's data, while boosting confidence in output and results. 

See also: Modernizing Insurance for the Digital Era

Stay ahead of pending legislation 

In the last few months, regulators have upped the ante with a clear desire to create uniform, ethical standards for using AI and automation. For example, New York City is instituting a law that penalizes employers for bias in AI hiring tools starting in January 2023; as a result, companies are scrambling to audit their AI programs before the deadline. On the federal level, the Biden administration released a Blueprint for an AI Bill of Rights, which will likely inform more rigid legislation focused on transparency and accountability.

Compliance-minded insurers have no choice but to turn to explainable AI, using software to understand — and prove — the variables that came into consideration for sensitive decision-making. This is underscored by a December 2022 lawsuit alleging disparities between how a leading insurance carrier processes claims for minority policy holders. The suit cites the company’s relationship with a claims management platform provider – and its partnership with a Netherlands-based AI firm that delivers a fraud detection score to indicate the likelihood of fraud throughout the claims process. 

This lawsuit is a bellwether: as wider adoption of AI and automation software penetrate the insurance industry, the use-cases for ethical, transparent AI will skyrocket.

Insurance needs explainable AI

Insurers can’t stop the momentum of digital disruption. With rumblings of an economic downturn, insurers can't pump the brakes while competitors ramp up processes reliant on AI and automation. With the help of explainable AI, insurers are set up to succeed in attracting and retaining customers, detecting fraudulent activities and staying compliant with pending legislative efforts ensuring AI is accessible and fair.