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Crucial Technologies for P&C During COVID

Technologies like machine learning, the Internet of Things (IoT), robotic process automation (RPA) and natural language processing (NLP) were already hot topics in P&C insurance before the world was turned upside down in 2020 due to the pandemic. These and many other “transformational” technologies have great potential for insurers in the rethinking and optimization of distribution, underwriting, claims and many other parts of the business. So, it is important to ask the question – how have the initiatives that leverage these technologies changed due to the pandemic?

Are personal and commercial lines carriers still moving forward with projects in 2021? Do executives still have the same expectations about the potential of these technologies to transform their business?

We answer these questions in detail for 13 specific technologies in two new SMA research reports, one covering personal lines and the other covering commercial lines.

However, I won’t leave you hanging in this blog, wondering about the answers to those questions. The short answer is yes – P&C insurers generally plan to move forward in 2021 with projects that leverage various technologies that have the power to deliver significant results and competitive advantage. The technologies we follow closely and have profiled in our reports have been organized into three strategic planning horizons: short-term, near-term and long-term.

For both personal and commercial lines, technologies in the AI family play heavily in the short-term category. Machine learning, NLP, RPA, computer vision and new user interaction technologies all rank high in terms of their potential to transform and in the level of activity underway or planned by insurers. Technologies that fall into the near-term or long-term horizons include wearables, blockchain, voice, AR/VR (augmented reality/virtual reality), 5G and autonomous vehicles. All have potential in insurance and will likely be incorporated into projects by innovators over the next couple of years but will not make it into broad, mainstream application until midway to late in the decade.

Our research on transformational technologies, when viewed in concert with our SMA Market Pulse surveys, shows that in some cases proofs of concept (POCs) and new projects have been put on hold in 2020, but all indications point to full steam ahead in 2021. Major projects already underway are continuing, and insurers state that they do not want to lose momentum for foundational projects like core systems. Projects that include transformational technologies needed to address digital gaps that were exposed during the pandemic have been raised in priority.   

See also: AI in a Post-Pandemic Future

In many ways, the pandemic is accelerating digital transformation across all industries, including insurance. Transformational technologies will play an outsized role in that transformation and look to be important components of insurers’ plans for 2021 and beyond.

Keys to ‘Intelligent Automation’

With new technologies and evolving customer expectations driving rapid change in the insurance sector, research suggests that more than 65% of insurance carriers will adopt at least limited automation by 2024. But, today, the insurance sector largely relies on multiple layers of manual processes that make customers wait while employees try to make sense of complex documents.

Intelligent automation (IA) offers insurance businesses an opportunity to revolutionize the way they operate to meet increasing demands from customers and pressures from the market and to plan for future, unanticipated interruptions. Through the combination of robotic process automation (RPA) and machine learning (ML), IA solves complex enterprise issues through the end-to-end automation of a business process.

The Insurance Ecosystem Involves Many Parties and a Deluge of Data

Many third parties are involved in the end-to-end insurance lifecycle. That’s the case whether you are in commercial, employee benefits, retail or any other type of insurance. A lot of information gets passed around. 

Brokers and insurers share data and documents. Advisers working with clients provide information, as do others, such as loss adjusters and lawyers. And the data arrives in the format preferred by the person who shares it.

That Data Comes in a Variety of Formats 

Data used for insurance purposes comes in many formats — structured, unstructured and semi-structured — and must be ingested, understood and digitized with accuracy before any automated processing takes place. This involves making sense of data such as cursive handwriting, which is commonly found in life insurance change-of-address and name-change forms, as well as in beneficiary documents. Insurance entities must extract data from highly unstructured employee benefits documents, such as dental, income protection, long- and short-term disability and medical documents. 

Brokers and insurers also need to compare and extract data from binders/slips, which can be up to 400 pages long and may use different words to describe the same thing. Insurers looking to ingest unstructured data (like email attachments, handwritten documents, PDFs and unlabeled data) — which is estimated to compose 80% of any enterprise’s data — can find their answer with cognitive machine reading (CMR).

While the industry’s standard data ingestion tool — optical character recognition (OCR) — can digitize structured data, it falls down when it comes to reading and extracting unstructured data such as tables, checkboxes and many other forms. In addition, OCR can’t read and digitize handwriting and signatures. 

A CMR-enabled intelligent automation platform (IAP) can analyze and process large amounts of unstructured data and complex business contracts in a fraction of the time it takes with traditional, manual processes. An IAP enables insurance companies to address the error-, labor- and time-intensive challenges involved with human-driven processes. 

For example, a global broking client wanted to extract 17 data points from commercial policies and endorsements. The documents came in from many different insurance carriers and in varying formats. All the data points required rules or reference tables to make the output usable, and most of the data didn’t have labels. In just a three-week period, with the samples of only 220 documents, with 40 different formats, multiple insurers and 10 coverage types, an IAP learned to extract 98.7% of the data, with 96.8% accuracy. Following this proof of concept, the client decided to implement this solution in multiple geographies.

See also: Automation Lets Compassion Scale   

CMR Allows Insurance Entities to Do More

John Hancock illustrates the many benefits businesses can derive from a CMR-enabled IAP. The company originally used manual processes to handle the large volume of policy management documents it received. Many of those documents held vast amounts of unstructured data — especially handwritten text in bold and cursive. 

Since adopting AntWorks’ CMR-enabled IAP solution, John Hancock has enjoyed higher business productivity, lower turnaround times and a more than 65% increase in accuracy for handwritten cursive recognition. Because the AntWorks IAP uses assistive and adaptive machine learning to learn from exceptions, the system’s accuracy gets better over time.

Insurance entities also can greatly increase their case volumes with the help of CMR. Using manual processes would require armies of people to do validation checks and take a lot more time, while producing higher error rates.

One of the world’s largest human resources consulting firms implemented AntWorks technology to manage large volumes of data and provide optimized quotations to customers for new policies and renewals. This company eliminated manual keying and automated healthcare claims-related processes by extracting data from paper documents and validating for accuracy. That enabled 70% faster processing and a 40% increase in accuracy.

A Fortune 500 insurance company that provides title insurance protection and professional settlement services found that the manual process of validating title documents was leading to error-prone and inconsistent output. CMR technology enabled this company to increase field accuracy across orders by more than 75% and increase productivity by 200%. (Field accuracy is one of the key performance indicators that insurance companies, their technology suppliers and analyst firms like NelsonHall use to evaluate automation solutions. For example, NelsonHall in its SmartLabTest evaluation of document cognition platforms looks at the proportion of fields correctly recognized, accuracy of extraction of recognized fields and proportion of fields overall that are 100% accurate and require no manual intervention.)

IAP Equates to Faster Time to Revenue and Richer User and Employee Experiences

When insurers adopt automation, they dramatically improve the experience for all parties — the broker, the customer and the insurer. They relieve employees from doing what is considered value-added but repetitive work like manual data entry. Automation also eliminates the need for error-prone, stare-and-compare work that’s common in the insurance industry.

That elevates the customer experience because IA allows insurance companies to process requests and respond much more quickly. Digitizing processes also delivers a better experience because customers don’t have to contend with the cumbersome process of filling out and handling paper forms. Meanwhile, IA enables insurance businesses to enrich their data with both structured and unstructured data from other sources and use data analytics and predictive analytics to make their propositions better and more personalized. 

IA also can enable businesses in the insurance ecosystem to move faster. That can help them to be more profitable.

Imagine a person is underwriting a life insurance case. If the data that person submits for the case is referred, an insurer would then have to go out to a doctor to get a medical report value. The underwriter would need to assess that report to understand whether it’s an acceptable case and communicate with the customer.

Getting and processing all the data can take weeks, delaying the policy kick-off. But if you can use intelligent automation to understand the data within medical reports, use rules to decide whether to accept or decline and automate the outcome, things happen much faster. 

The title insurance protection and professional settlement services insurance company mentioned earlier reduced its processing time by 70% after adopting a CMR-based IAP solution. Meanwhile, the human resources consulting firm noted above increased its operational efficiency by speeding turn-around time, leading to higher customer renewals, an uptick in new customers and increased revenues. 

Process Discovery Helps Companies Better Understand the Work They Do

Often, a lack of knowledge and understanding of process flows leads to automation failure. If you’re not quite sure which processes are the most optimal to automate or you’re not clear on all the steps involved in your process (and you don’t have time to do workshops with lots of analysts and business subject matter experts to figure things out), then process discovery is an excellent way of understanding exactly how the process is conducted. 

Process discovery enables organizations to identify high-value processes for automation by recording actions that users undertake. If an organization can look at, say, 10 different people doing the same process, it can better understand not only how the process really works but also all the variations in the process, including things like the different process times and different applications accessed. The discovery enables the organization to see the steps involved and create automated processes that use the optimal approaches to those processes. The organizations can then apply what they learned to claims data extraction, fraud detection, mortgage verification and processing, account set-up, policy administration, quotation validation, title verification and a wide variety of other insurance use cases.

In addition to helping companies better understand their processes, process discovery can help in identifying opportunities for automation, expedite digital transformation and unveil previously unknown processes for in-depth process mapping.

See also: Evolving Trends in a Post-Covid-19 World  

Intelligent Automation Makes Companies More Resilient

Our new normal puts increased focus on the importance of business resiliency. Manual processes work against that because they often mean that workers need to go to physical business locations to handle paperwork. That creates risk in today’s environment. Intelligent automation frees people and organizations from on-site, paper-based manual processes and instead relies on processes that are better suited to today’s digital, distributed, remote work world. IA also scales, as needed, to adjust to changing circumstances.

The time has come for insurance companies to look at ways to improve their operational processes through technology innovation. IA has the capabilities to help insurance practitioners to do business much faster, more efficiently and with greater security.

Practical Uses of RPA for Insurance

In the fast-moving world of insurtech, new technologies such as robotic process automation (RPA), intelligent automation, artificial intelligence (AI) and machine learning are making it easy for insurers to dream about transforming processes. However, too often they get lost on how to put those innovative technologies into use. For a risk-averse industry, we’re seeing more insurers open to using these modern technologies to improve processes and ultimately better serve their insured – the way they expect to be.

Many insurers are building on proven and integral technology platforms, including content services and core insurance platforms, to include more modern solutions that will help them further streamline operations. By combining capture, workflow, integrations and RPA, insurers can take some of the tedious tasks out of their employees’ workload and automate those processes, leveraging a “digital worker” to replicate redundant and manual tasks.

For example, take a loss-run request process – which one of our customers completely transformed using capture, workflow and RPA.


Intake processes are often tedious because there are too many manual steps. Without standardization around the process, it is inefficient and doesn’t provide reliable metrics. To continue to move critical information forward, data needs to quickly and accurately get to the right people – where and when they need it. Many content services applications offer multiple ways to capture data and instantly digitize documents, including emails, PDFs and Office documents, and connect them to key processes. This ensures data is digital from the beginning and throughout the lifecycle. Once imported and classified, insurers can create a standard way to kick off processes, drive additional efficiencies, enable performance metrics to identify trends and better assess internal resources. For loss run requests, once the request is made – whether by email or through the insurer’s portal – integrations with the content services platform can capture the request to officially initiate the process.

See also: The 5 Top Trends in AI and RPA  


A workflow automation tool is an excellent way to help keep processes digital by electronically routing information to the appropriate person at the right step in the process. Additionally, because information is electronic, it is easier to monitor the status of items by incorporating real-time notifications. Within the loss-run request process, employees can use electronic workflows to take captured information and run that data through the applicable channels to get the claims history reports needed to make an informed underwriting decision. After the insurer receives those claims history reports, they can analyze how many claims were made, what types of claims were made and the financial impact of those claims.


RPA, intelligent automation, AI and machine learning are making it easier to take advantage of digital workers to further streamline processes and achieve greater efficiency. For the loss-run request process, once information is digitally captured, indexed and put through a workflow queue, the workflow can tell the digital worker to take indexed keywords and run them through third-party websites to gather any hits for the loss-run history. Once those are available, the digital worker can open the reports, download them from the website and upload them into the content services platform. There, the workflow process is finalized and the requestor, or agent, can access the report and make a decision. It took a digital worker three to five minutes to complete each item, saving more than 20 hours per day on run-loss automation requests, according to a customer using a combination of content services and RPA technology.

The entire loss-run request process was simplified down to nine steps:

  1. Index data for transaction type, market and policy number in content services platform
  2. Navigate to market web portal based on market keyword
  3. Log into market portal
  4. Navigate to “Request Loss Run”
  5. Enter policy number and select submit
  6. Retrieve loss run report
  7. Save loss run report
  8. Import loss run report to content services platform
  9. Send report through final workflow steps

Building innovative solutions on proven technologies, like content services platforms, allows insurers to continue to evolve and modernize, as well as keep pace with the expectations of their clients. With any new solution, an organization needs to evaluate the best way to implement the technology into is business processes to ensure it helps them achieve greater efficiency and improve customer service. For RPA and intelligent automation it’s often easiest to incorporate and leverage these solutions in processes that:

  • Have a minimum number of steps
  • Are highly repetitive
  • Aren’t super complex
  • Have a quantifiable value

See also: 3 Ways RPA Enables Growth  

Understanding the ins and outs of each of your processes is the first essential step to know where new technologies like RPA, intelligent automation, AI and machine learning will best benefit. Once implemented, employees become more productive and can focus on higher-value tasks to deliver faster and better service to their prospects and customers.

What’s Beyond Robotic Process Automation

The insurance industry, which relies heavily on repeatable processes, is embracing robotic process automation (RPA). Gartner projects that global RPA software spending will reach $2.4 billion in 2022. But organizations need to understand that RPA is a primitive technology. And it represents only a small part of what’s needed to scale and enable straight-through processing.

What businesses need for end-to-end automation is an integrated automation platform (IAP).

RPA Is Very Basic – And Does Not Know How to Learn

Bots based on RPA can open spreadsheets and databases, copy data between programs, compare entries and perform other routine tasks, the Boston Consulting Group says.

But BCG adds that “RPA is a Band-Aid.” The firm explains that RPA can lead to a proliferation of spot fixes that threaten IT architecture and integrity.

BCG also notes that RPA bots don’t get smarter with time and experience. “When rules conflict with reality or when unexpected events occur,” the firm says, “a human needs to intervene.”

As a Result, RPA Greatly Limits What Organizations Can Automate

Insurance and risk automation companies currently use RPA as a data transport layer. That involves taking data from structured input sources and bringing that data to a target application by employing robot software.

This is a simple task that doesn’t involve any exception handling. But there’s far more to do.

Insurance companies and other organizations also need to analyze, contextualize, enrich, read and understand their data. However, insurance and risk processes are often complex and involve using data from various varied, unstructured formats and sources.

See also: Next Big Thing: Robotic Process Automation  

With unstructured data come multiple exceptions, which require cognitive ability and intelligence to bring out meaning and insights. This is where RPA fails.

RPA falls short because it is hard-coded and rules-driven. RPA is unable to scale and adapt to these more complex unstructured processes. When organizations need to use unstructured data – which has not been prepared or contextualized, or changes in target applications and sources – to power their automation efforts, RPA-based bots just don’t work.

That’s Why Now Is the Time for Insurance Organizations to Embrace IAP

To enjoy the benefits of straight-through processing, businesses need RPA, data availability and data usability. Yet RPA does not deliver these last two functions. Meanwhile, IAP does it all.

The data availability feature of an IAP solution ensures the data is made available and is accessible for automation. It includes technologies such as document classification and indexing, image pre-processing and machine vision for digitization.

Data usability – which an IAP solution also supports – makes sure the available data is ready for business processes. It prepares the data using business rules; data certainty; enrichment lookup; and natural language generation, modeling and processing.

IAPs Bring All the Automation Functions Businesses Need Together

Businesses can buy point solutions from separate vendors to address each of these functions. But working with multiple companies and systems needlessly creates complexity. It entails multiple contracts and integration efforts. And it leads to finger pointing when problems arise.

See also: How to Automate Your Automation  

That’s why insurance and risk management companies are looking to IAPs. They automate end-to-end business processes quickly, easily and in a scalable manner.

With IAPs, insurance companies can read and interpret data from unstructured documents – whether those documents are printed or handwritten, inferred or image data. Organizations using IAPs benefit from automation processes that grow smarter over time. And businesses that implement IAP solutions can leverage multiple technologies to drive data velocity to enable optimal business and customer outcomes faster.

5 Emerging Trends for Insurance in 2020

When it comes to implementing new technology, the insurance industry is rarely considered an early adopter. However, insurance companies have been taking early strides, somewhat in a migratory manner, to adapt to technology advances to help better run operations, improve underwriting and risk management, enhance customer offerings and services and profitably grow the business. Taking into account this early progress, we look to 2020 and several trends in the industry that have begun to take shape and will accelerate this coming year:

From RPA to IPA

Whether stemming from insurance carrier frustration that basic robotic process automation (RPA) — bots mimicking human tasks — hasn’t produced savings relative to carrier aspirations, or from insurance carriers’ increased understanding of machine learning (ML) and artificial intelligence (AI) capabilities, the industry will see an increase in “intelligent” process automation (IPA) that is more robust and combines the bot with learning, evaluative and decision-making capabilities for greater impact. This shift will be driven by carriers looking for higher business returns by solving a wider range of problems in the manual activity value chain with automation.

From Point Solutions to Digital Ecosystems

While today’s “exploration era” in insurance — characterized by new technology proofs of concept, use of point-solution providers and insurtech accelerators — has generated some progress and buzz, it comes with a down side. Single-solution or shiny software objects that address an individual problem or portion of the business will soon become too confusing and difficult to manage, actually creating a gridlock in carrier movement to true transformation. The fact is that no single solution can bring about transformation on its own and will instead require a sum-of-the-parts approach managed in a smart ecosystem. Similar to a conductor’s role in astutely incorporating the needed instrument — which in and of itself can only perform one thing, as a trumpet can only make trumpet noises — so too will orchestrated digital ecosystems begin to take priority as carriers look at enterprise platform solutions versus traditional bolt-on approaches.

See also: 3 Phases to Digital Transformation  

From Data Warehousing to Data-in-My-House

As an anonymous poet once said, “It is a great day when one discovers the beauty that lies within oneself.” So, too, will carriers be focused on unlocking the value of their own information that has accumulated over time. The focus on data infrastructure, lakes and warehouses now takes aim at using the very data that has been collected or can be mined — particularly the plethora of historical in-house data that has been generated by the carrier itself in the execution of risk evaluation, providing coverage, taking losses, servicing inquiries, etc. Content management systems and capabilities will start to transform into intelligent management systems with outputs infused into future-facing decisions and actions. Using AI and content mining capabilities to convert traditional in-house “flat” files — policy, risk and loss reports, correspondence, etc. – into usable insight, combined with the continued use of outside data and emerging sources (such as the Internet of Things), will enable carriers to take a significant step in becoming analytics-driven businesses.

From Digital Customer Experience to Digital Risk Management

While the term “digital” is used — and even overused — in a variety of contexts, many would agree that the digital movement was and is centered on digitizing the customer experience. Making things easy for the customer, creating experiences that will keep them coming back, and identifying customer service as a top priority are all common objectives in insurance, and a great deal of digital emphasis is placed on these initiatives. However, the heartbeat of an insurance company is effective risk management — and quite often, the most reluctant to join the digital parade are chief underwriting officers (CUOs), not because they’re grumpy progress-stompers, but because they want to ensure that good risks are put on the books and that underwriting disciplines and philosophies are upheld. Not enough digital ambitions have been focused on the CUO world, and that is where digital convincing needs to occur to bring them on board and excite them about digital. As a result, while digital customer experience will remain a priority, emphasis will broaden toward using digital technologies — be it AI, data analytics or risk assessment technologies — for a better underwriting result. Digitizing phases of the underwriting process to optimize underwriting time capacities and drive consistency of risk assessment and decision-making will be more in focus, adjacent to making customers happy.

From Call Centers to Intelligent Customer Interaction Centers

Customer servicing enabled by natural language processing, AI and voice assistants, such as Alexa or Siri, will become more common. This customer call automation, combined with web and email channel automation technology, will move carriers toward omnichannel customer interaction management that is driven by technology engines. This shift will be driven by carriers looking for efficiencies in workforce management, faster customer issue resolution and tracking of customer interaction data to improve products and services.

Given these other trends, carriers will be looking more to an outside perspective —outside of the insurance industry, outside of traditional insurance approaches and outside of traditional insurance vendors and suppliers. Insurance companies move as somewhat a pod, and, historically, the benchmarks of what constitutes progress and advancement has been focused on others in the pod. Over the next year, we’ll see a shift toward the new benchmark, which is now the broader world, other industries and the digital economy being built outside of insurance. This is the economy customers of insurance carriers are experiencing in their worlds — whether they are individual or commercial buyers of insurance — and their expectation of what insurance should be or should look like is shaped by these outside forces. As a result, insurance carriers will need to rely more and more on partners in 2020 who may not be traditional vendor insiders, but outsiders who have helped create digital ecosystems in other industries and enabled digitally born companies.