Tag Archives: rpa

New Tool: Cognitive Process Automation

Much of North America is seeing lower interest rates across the board, which bodes well for consumers making large purchases but puts the insurance industry under intense scrutiny. Carriers with bond-heavy portfolios may see a decline in returns and, as a result, lower profit margins. Despite the insurance industry’s overall acceleration toward technology in 2020, carriers of all lines of business will need to move much more quickly – or risk falling even further behind their profit margin.

Insurers should cut unit costs, but not corners.

Insurers must cut costs; however, with more consumers requiring personalized attention from their insurance company, insurers must walk a fine line. Reducing expenses may be necessary, but insurance companies must be careful not to lose their existing customers in the process. Automation — especially a newer form, called cognitive process automation (CPA) — allows for reducing costs while still providing the service that customers require.

In some departments, such as underwriting and billing, insurance companies should prioritize responsivity for a more convenient customer experience. This can be done by using process automation to streamline communication between the carrier and the policyholder.

In other departments, such as claims, policyholders will appreciate careful and attentive human interaction. While responsiveness is still paramount, customers will have more trust in the company’s claim-handling process when they have access to a dedicated claims adjuster. 

Where resources are scarce, technology is a viable solution.

Even prior to the impact of COVID-19, carriers like Protective Insurance had begun implementing CPA, a more advanced version of robotic process automation (RPA).

Many carriers have at least discussed the features and capabilities of RPA. However, RPA and even intelligent process automation (IPA) products are primarily limited to structured data. 

CPA is the new disruptor in both the insurance and automation industries. Combining the repetitive abilities of traditional RPA with artificial intelligence (AI) and machine learning, CPA relies on bots that capture data and scan documents via optical character recognition (OCR) but that also do much more. The bots can fully automate entire underwriting and claims processes, from start to finish, with minimal human intervention.

See also: 20 Issues to Watch in 2021

As an example, policy underwriting has traditionally been considered a manual undertaking, but CPA has demonstrated that underwriting can be largely automated — everything from policy submission to risk rating and underwriting to issuing declinations and binders. Using CPA, insurers can write more new business, streamline the renewal process and even detect cases of potential fraud with minimal human supervision.

Claims departments can significantly reduce the manpower needed for largely repetitive processes. Bots programmed with CPA can fully automate the first notice of loss (FNOL) process, fraud investigations, benefits calculations and even payments. In fact, time-consuming processes like claims communications can be automated up to 95%

Employees can instead focus on more engaging tasks and provide better service on edge cases.

Increased efficiency is more remunerative than reduced overhead.

In cutting expenses, the matter of efficiency is sometimes overlooked. If time is money, shouldn’t carriers condense time-consuming processes, as well?

Automation saves time and money. Whenever carriers optimize a process by implementing an automation solution, a precious resource has been created: time

As the policyholder mindset continues to grow in favor of more personalized experiences, cognitive automation allows insurance carriers to use their best asset – their human workforce – to focus on retention efforts, customer satisfaction and even cross-selling additional lines of business. At its heart, insurance is a people-focused business, and even tech-friendly consumers prefer personalized human interactions.

The takeaway

With lower interest rates threatening profit margins, insurance carriers must target cost reductions – and sooner rather than later. Companies can use new process automation tools, such as RPA and CPA, to cut redundant work often found in underwriting and claims departments. Insurers are then able to reprioritize the focus of their workforce on customer retention, or even scaling for growth.

Despite COVID, Tech Investment Continues

Insurers will continue to experiment with emerging technology in 2021, despite the challenges of 2020. When the COVID-19 pandemic hit, many insurers paused their 2020 innovation plans, emphasizing digital workflows and cost control at the expense of emerging technology pilots. Heading into 2021, technology priorities for many insurers, especially those in the property/casualty space, are similar to those of 2019.

The U.S. is still in the midst of the pandemic, and some insurers are anticipating lower premium revenues for the coming year. In spite of this, insurers are investing in technologies like artificial intelligence and big data, though some are narrowing the scope of their innovation efforts for the coming year.  

Understanding Emerging Technology Today

Insurers typically take a few main approaches to emerging technologies. Early adopters experiment with the technology, typically via a limited pilot. If the technology creates value, it’s moved into wider production. Insurers that have taken a “wait-and-see” approach may launch pilots of their own.

Novarica’s insights on insurers’ plans for emerging technology are drawn from our annual Research Council study, where CIOs from more than 100 insurers indicate their plans for new technologies in the coming year.

No insurer can test-drive every leading-edge technology at once, and every insurer’s priority is a result of its overall strategy and immediate pressures. Still, at a high level, several industry-wide trends are apparent:

There is big growth in RPA; chatbots continue to expand. More than half of all insurers have now deployed robotic process automation (RPA), compared with less than a quarter in 2018. Chatbots are less widely deployed but on a similar trajectory: from one in 10 in 2018 to one in four today.

AI and big data continue to receive significant investment. These technologies take time to mature, but it’s clear insurers believe in the value they can provide. More than one in five insurers have current or planned pilot programs in these areas for 2021.

Half of insurers have low-/no-code capabilities or pilots. These types of platforms are relatively new but have achieved substantial penetration in a short time. Early signs indicate they could become a durable tool for facilitating better collaboration between IT and business experts.

Despite continued tech investment, 2021 might be a more difficult year for innovation. Insurers’ technology priorities have generally reverted to the mean — more so for property/casualty than for life/annuity insurers — and technology budgets for 2021 are within historical norms. Still, some insurers are paring down pilot activity in less proven technologies, like wearables, to maintain their focus on areas like AI and big data. Technologies with substantial up-front costs, like telematics, may be harder to kick off in 2021. 

See also: Technology and the Agent of the Future

How Emerging Technology Grows

Emerging technologies have widely varying rates of experimentation, deployment and growth within the insurance sector. Their growth rates boil down to a few key related factors:

  • How easily the technology is understood.
  • How readily it can be deployed and integrated with existing processes.
  • How clearly the value it creates can be measured and communicated.

At one end of the spectrum are technologies like RPA and chatbots. These technologies create clear value, are readily added to existing processes and are relatively easy to deploy. As a result, insurers have adopted them rapidly.

Artificial intelligence and big data technologies require longer learning periods; sometimes, they require business processes to be completely reengineered. The technologies create value for insurers but have grown more slowly because they take time to understand and integrate.

Drones, the Internet of Things (IoT) and telematics can create new kinds of insurance products or collect new kinds of information. These can also create value, but their growth remains slow because developing these technologies may require orchestration across several functional areas, and they can be costly to ramp up.

On the far end of the spectrum are technologies like augmented and virtual reality, blockchain, smart assistants and wearables. Most of these technologies don’t yet have established use cases that demonstrate clear value, so it remains to be seen whether they will be adopted more widely.

Using Emerging Technology

One key insight from Novarica’s study is that technologies that integrate readily to existing processes can grow more rapidly than technologies that require new workflows to fully use. This observation comes with a few caveats for both insurers and technology vendors.

Insurers sometimes fall into the trap of “repaving the cowpath” — they adopt new technologies but integrate them into their existing (inefficient) business processes. Doing so means they can’t get maximum value from their investment. Ironically, it’s usually the shortcomings of legacy technology that have made these processes cumbersome in the first place.

It’s easy to understand the value that technology creates when it integrates with an existing process and can be measured with the same key performance indicators (KPIs). It’s much harder to create a new process enabled by new capabilities, train employees to execute it and demonstrate that the new way is better than the old way. Yet getting the most out of emerging technologies often requires rethinking how business might be done.

See also: 2021’s Key Technology Trends

For their part, vendors should focus on the value their products create and the problems they solve, aligning them to insurer needs. It’s not enough to use a new technology for its own sake, and using new tools sub-optimally may make them seem less effective. Vendors should coach their insurer clients through best practices and help them understand how their tools can ease, change or make obsolete existing processes.

At its core, insurance is a simple industry focused on connecting those exposed to risk to capital that can defray potential losses. At the center of that value chain are insurers, that continue to explore new technologies to better understand their risks, sell more and operate more efficiently. Even in uncertain times, insurers are innovating.

Banishing Busywork: Recruit the Robots

Robots, biometrics and smart devices. If you had told me 10 years ago I’d not only have access to these innovative technologies but would use them daily, I’m not sure I would have believed you. Yet here I am, using facial recognition to unlock my phone, log into apps and access my bank accounts. And at Hyland, I am working with insurance organizations to understand, strategize and implement disruptive technologies, including robotic process automation (RPA), which works hand in glove with a content services platform. Much like a smart phone provides a variety of tools and capabilities to streamline our day-to-day lives, a content services platform and RPA can help insurance organizations improve their operations, drive efficiencies and meet their digital transformation goals — all of which helps them thrive in an evolving business climate. 

The insurance industry has often been perceived as being slow adopters of technology, often relying on old processes and systems because they worked, even if inefficiently. What can we say…we’re risk-averse. Then, the insurtech era came along and presented us with different and more efficient ways of doing things. Keeping pace with all the new technologies can be stressful, but these technologies are impossible to ignore. As such, RPA has become a technology of interest because of the massive productivity and customer service benefits it offers.

In key business processes, getting all the necessary information and consolidating it often takes more time than deciding whether to issue the policy or pay the claim. RPA can do the gathering and consolidating without human intervention. 

A human might grow tired of doing the same task of collecting and consolidating data again and again…that’s when things can be missed, and mistakes can be made. A bot doesn’t get tired. And, while the RPA bots handle more tedious, manual tasks, your staff is freed to focus on more creative work that drives customer satisfaction. 

See also: Keys to ‘Intelligent Automation’

Setting an effective RPA strategy starts with structure

A successful RPA automation project relies on a vetted and structured format. To achieve this, organizations need to have control over the information feeding the RPA solution. Many insurers have implemented content services platforms as their information hubs, connecting all content within line-of-business systems and ensuring accurate, up-to-date content that RPA solutions rely on. Once fully connected, RPA and the content services platform provide a comprehensive suite to achieve intelligent automation.

To identify which internal processes, tasks and actions are the best candidates for automation via RPA, I recommend looking for those with the following qualities: 

  • Standardization: Look for tasks that have a defined sequence and don’t have too much variance. Ideally, the work won’t change any time soon. 
  • Structured data: The information and data that feeds the task should be relatively structured – or organized in a fairly predictable way so that it is easily classified. 
  • Rule-based: The task or action should be built on a series of well-defined, objective rules. That means it would not require human interpretation to make a decision. 
  • High-volume: The chosen tasks and actions should represent a substantial amount of staff time. Manually transferring data from one source to another is typically a good target. 
  • Digital data: A task or action that already involves and relies on digital data is best suited for automation. If the task still relies on physical and handwritten documents, optical character recognition (OCR) and machine learning can be implemented to convert them to digital formats. 

Implementation: Finding the right solution…and provider

How can insurers select a solution, and provider, that best fits their unique requirements? Look for an RPA solution that is scalable and configurable to ensure it meets your needs today and into the future. Selecting a solution that complements an existing content services platform or a vendor that can provide both creates an end-to-end, RPA-enhanced automation strategy — one that is designed to empower your organization to automate, optimize and transform tasks, actions and processes. Look for an RPA solution that helps your organization: 

  • Analyze: Look for platforms that quickly, accurately and intuitively analyze tasks and processes down to the click level and automatically document process steps. 
  • Build: The RPA solution should leverage a low-code toolset to allow you to quickly and easily create automation opportunities. 
  • Run: Efficiency is key here – the solution should efficiently run unattended or attended automations, ensuring maximum bot utilization and scalability.
  • Manage: The best RPA applications manage and orchestrate bots with ease, using real-time dashboards for live monitoring and intuitive management. 

When investing in any new technology, it’s also important to have a clear understanding of the total cost of ownership (TCO), which includes both the direct and indirect costs associated with the purchase. Be sure to calculate any additional fees for integrations, consulting, maintenance, training and other costs. 

See also: The Future of Work: Collaborative Robots

Farewell wasted time

The current global health situation has led many insurers to accelerate their digital transformation strategies and new technology. RPA provides a great opportunity to enhance intelligent automation capabilities and further business process automation strategies. Insurers that leverage a digital workforce to complement their human employees provide employees with additional ways to excel at the work they do best, while delivering increased value for the organization.

In today’s data-driven world, bots help combat productivity drains that deplete resources and allow employees to focus their time on higher-priority tasks that build more meaningful connections with the customers they serve.

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.