Tag Archives: machine learning

AI in a Post-Pandemic Future

The COVID-19 pandemic put businesses under extreme pressure and has led to a massively accelerated digitalization of the workplace. The silver lining is the opportunity to develop more efficient, digital operating models by reinventing work and leveraging the power of artificial intelligence and automation.

Artificial intelligence and why it matters

Hype has for some time surrounded AI, but promises first made more than 60 years ago are now finally being delivered. What has been the game changer responsible for putting AI back on the map and on the verge of changing, well, just about everything? The answer is deep learning, an old idea that found an opportunity to mature in the late 1990s and early 2000s. 

Based on learning tasks using artificial neural networks inspired by the biological nervous system, deep learning technology is highly advanced and requires vast volumes of data and computing power only recently made possible. By 2030, AI is estimated to contribute as much as $15 trillion to the world economy, making it the biggest commercial opportunity in today’s fast-changing economy. Indeed, the new realities of the post-COVID-19 world require the accelerated adoption of AI to deliver the efficiencies and augmentations of a highly digitized workplace.

Figure 1: AI’s projected impact on global GDP

For more than 250 years, the fundamental drivers of economic growth have been technological innovations, the most important being general-purpose technologies such as electricity and the steam engine. Now it is AI that stands out as the transformational technology of our digital age, which, as with previous GPTs (general purpose technologies), is expected to trigger waves of complementary innovations and opportunities.

What tangible opportunities does AI offer businesses right now? We are currently witnessing the first wave, usually as a result of companies automating tasks and processes, reducing costs and creating more efficiencies. The work dividends from this first wave are mostly positive. Low-level, tedious, hazardous and boring tasks are taken over by machines, freeing time for the humans to do the higher-level, more productive tasks. 

Significant shifts in computing power and availability of large-scale data advance the development of AI applications that continue to rapidly grow in complexity and autonomy. AI’s autonomous nature and the way it is trained on data – essentially learning from the mistakes made in the past – make the technology both an opportunity and a risk.

See also: 4 Post-COVID-19 Trends for Insurers

AI at work

As organizations deploy technologies that automate work or introduce machine intelligence in the organization, the limiting factor in translating these innovations into real business benefits will be talent. Beyond the designers, developers and data scientists that everyone is battling for today, companies will need to explore what new roles are likely to emerge in digital disruptors.

As with many professions, underwriters have been doing a job one way for decades and now are expected to do things differently. The role is primed for transformation as AI is poised to reconfigure and augment insurance underwriting. Fueled by an explosion of data, low-cost data storage and open source technology, AI has the potential to help underwriters analyze an incredible amount of information, find red flags and help make more accurate decisions. 

While there is no expectation for human underwriters to be replaced, as their judgment will still be needed for complex cases, future underwriters will be expected to work alongside AI systems to ensure all risks are accurately measured and priced. As underwriters increasingly interact with automated AI systems, there will be a need for new skill sets to develop, with some old skills potentially becoming obsolete.

Meanwhile, demand for these new skills far outstrips supply at present, which indicates that the main roadblock to insurers capturing the full value of this new technology is not the science, but the human change management factor. It is a tall order, but starting by having the right people with the right skills in the right roles will far outweigh picking the right technology, algorithm or latest start-up to work with.

More digital, more human

One of the major transformations of the digital age is to see more companies adopting a flat working structure, where career paths are less clear and the turnaround of young talent greater. In this new environment, a next-generation operating model that supports the opportunity to learn skills, to have thought leaders provide mentoring and to involve new staff in meaningful projects will be critical to attract and retain the best digital talent. 

By moving beyond a one-size-fits-all approach to human resources and talent management, digital workforce platforms can help create the conditions in which employees feel energized by their work, valued by their organization and happy in their environment.

Google and Apple are examples of early adopters of digital workforce platforms that built ecosystems allowing them to innovate, take advantage of new technologies to cut costs, improve quality, build value and respond quickly to the fast-changing and rising digital expectations of consumers. How can this model be replicated across other industries?

The answer may depend on the ability of corporate leaders to restabilize the workforce — and to reconceive organizational structures — by using the very same digital technologies that have destabilized it in the first place. The incoming AI revolution should reinforce, not weaken, the uniquely human characteristics that define how we work, particularly in the way that we collaborate, communicate and develop relationships. To fully exploit emerging digital capabilities, most organizations will continue to depend on people, with human skills actually becoming more critical in the digital world, not less. 

See also: Stop Being Scared of Artificial Intelligence

As tasks are automated, they tend to become commoditized; a “cutting edge” technology such as smartphone submission of insurance claims quickly becomes almost ubiquitous. In many contexts, therefore, competitive advantage is likely to depend even more on human capacity, on providing thoughtful advice to an investor saving for retirement or calm guidance to an insurance customer after an accident.

AI is likely to be one of the biggest game changers in insurance history, offering a wide range of opportunities from faster and more efficient claims management to a greater variety of on-demand insurance services. As organizations transform to thrive in a digital environment, their success will be affected by how well they integrate their workforce into the transformation journey and manage the tension between the constant drive to innovate and improve and the new governance, compliance and regulatory risks created by new AI technologies. Digital transformation requires the overhaul of culture beyond technology updates or process redesign to reap the anticipated benefits.

Optimizing Care with AI in Workers Comp Claims

The expert panel explains how AI can:

  • let you identify the right provider for a case and steer the injured worker to that provider
  • help busy adjusters easily spot potentially troublesome cases and manage them better, from start to finish
  • continuously optimize your network of providers, so you can be sure to have the right provider working with the right worker at the right time.

This panel consists of: Gary Hagmueller, CEO of CLARA analytics; veteran adjuster Nicole Corey; and CLARA analytics Chief Medical Officer Paul Kim. The panel is moderated by Paul Carroll, Editor-in-Chief of Insurance Thought Leadership.

Don’t miss this free on demand panel discussion. Space is limited, so register today!


Presenters:

Gary Hagmueller

CEO, CLARA analytics

Dr. Paul Kim

Chief Medical Officer, CLARA analytics

Nicole Corey

Owner, California Work Comp Advocacy

Paul Carroll

Editor-in-Chief, Insurance Thought Leadership

How to Thrive Using Emerging Tech

Early adopters of artificial intelligence (AI) and machine learning (ML) are able to sift through massive amounts of data and use it to enable various capabilities. These range from making decisions about how to triage a claim using algorithms to improving a customer’s overall claims experience using more data and sources automatically pulled in from AI and ML methodologies. 

But where does the rest of the industry stand with these new capabilities? We released a study around how the top 100 U.S. carriers are benefiting from AI and ML and the challenges and opportunities for an AI-driven future. We found that 75% believe proper implementation of AI can provide carriers with a competitive advantage through better decision-making. 

While only 62% say the carrier they work for is already applying, piloting or planning AI and ML initiatives, these early adopters are already seeing significant AI and ML benefits. In terms of improving the experience for existing customers, insurers are experiencing advantages with faster claims settlements (88%), improved fraud detection (87%) and better risk scoring (85%). On the prospecting side, AI and ML are enabling early adopting insurers more customized and targeted opportunities for cross- and upselling (88%). 

Of the survey respondents representing insurers that are early adopters, most come from the 20 largest U.S. carriers, but adoption across the remaining top 100 U.S. carriers is also rapidly increasing.

While carriers are generally positive about their use of AI and ML, implementation does come with its own set of challenges surrounding staffing, data and compliance. 

The challenges around AI and ML adoption 

Insurance carriers are largely positive about the value of their AI and ML initiatives, but the study identified the challenges they will need to overcome. Staffing challenges are a major concern. According to the study, nearly half of the respondents (49%) said that AI and ML implementation has already affected their staffing plans today. Insurers need people who can understand the inputs and outputs of the applications, and who can explain them to the business. They need knowledge managers who can speak in both technical and non-technical languages and link the dialogue between parties.

See also: Stop Being Scared of Artificial Intelligence

Another major concern is the ability to access high-quality, trustworthy data. The three main issues with data that survey respondents mentioned include their ability to manage the volume and security of the data; linking and normalizing data across different data sources; and ensuring access to the data. Adopters clearly see the value of third-party data, as a majority of the adopters (82%) say their organizations have or will buy external data for their AI and ML initiatives. 

The third concern we found is around compliance and regulatory challenges with insurers’ use of AI and ML. Adopters worry that regulators and legal bodies may not understand AI and ML applications and could possibly block or limit them. Nearly three-quarters (74%) of adopters also have concerns about data privacy, security and ownership issues, anticipating increased regulatory scrutiny as more data sources are accessed and modeled.

Although the COVID-19 pandemic has slowed things, 95% of personal lines insurers are moving forward with their overall technology plans and investments, with only 5% retrenching, according to Strategy Meets Action (SMA). Meanwhile, 75% of commercial lines insurers are moving forward with their overall technology plans and investments, with only 25% retrenching or pausing. 

See also: Step 1 to Your After-COVID Future

Despite these challenges, the early adopters of AI and ML are already seeing benefits. Faster claims settlement, more targeted cross-selling and upselling, improvement in fraud detection and better risk scoring are just a few advantages that insurers are leveraging. As insurance carriers look to implement emerging technology, they should find a technology partner that has a deep understanding of the data, analytics and insurance industry to help them maximize their AI and ML initiatives. In particular, they should look to find a partner with a demonstrated expertise in building models that leverage advanced analytics and that have extensive experience in managing, normalizing and analyzing increasing volumes of data. By this time next year, only those insurance carriers that are fully embracing and implementing AI and ML capabilities now will have that competitive advantage.

For additional insights and data from our study, you can turn to our white paper, The State of Artificial Intelligence and Machine Learning in the Insurance Industry.

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.

ML for Commercial Property Insurers

For years, the preparation and management of data have exposed themselves as two costly and critical challenges for commercial property insurers. These challenges are hampering production and efficiency and inhibiting growth and profitability. The flow of submissions and the preparation of statement of values are laborious and time-consuming to agents, brokers, insurers and anyone else in between. Without a solution to meet the changing market needs to manage these complex data sets, commercial property insurers’ ability to quickly respond to markets and aggressively price business is hindered.

The inability to address these issues has obstructed the process, making it prone to error and hard to scale, especially in today’s market. In turn, this obstruction limits the speed and accuracy of commercial insurers’ decision making and debilitates businesses’ potential to grow. The gap between data preparation, screening, prioritization, analysis and pricing steepens, and companies find themselves stagnant and looking for answers. There is yet to be a commercially viable solution focused exclusively on automating the operational preparation and processing component of commercial property insurance data so companies can better meet the growing need of customers and markets and handle the substantial work that is required.

A company’s inability to respond quickly can affect the relationship with the producer, leading to a higher chance of being selected against. These types of companies are more likely to take on more complex characteristics, along with riskier business as the expectation of long processing times is already set.

But we’re starting to implement machine learning into problem-solving tools to address these challenges. These tools enable commercial re-insurers to take their raw data sources and harmonize them with next-gen technology that analyzes, reviews and writes business submissions to provide companies with the competitive edge that’s been sought after for years.

Making the most of your data 

On average, commercial property insurers can only process a portion of the submissions they receive. Typically, managing and preparing results in inconsistencies surrounding labeling, coding and more, which create downstream issues with pricing, modeling and aggregation. Critical amounts of data are lost through the process, and information is not consistently accessible, hindering the ability to make crucial decisions. The only way to solve this and manage business expectations is by hiring additional skilled labor, but this increases the acquisition costs, hurts profits and isolates information among the skilled experts.

Using machine learning, data integration and analysis offer the ability to make data mapping suggestions based on learning algorithms. Manual adjustments are then fed back into the decision-making model, transforming complex, big data into actionable insights that are accessible, in real time, to the entire organization. This allows teams to spend their time on business-generating activities and acting on insights from data, instead of the constant back and forth editing spreadsheets.

See also: How Machine Learning and AI Reduce Risk  

Potential opportunities to grow the business are lost today because of the acquisition costs for new business, but machine learning allows insurers to get from point A to point B by enabling them to screen and prioritize submissions. Today, submissions can be prepared one at a time, but, with machine learning, employees are able to triage multiple submissions at once, including new submissions, enabling the underwriter to focus on the key deals and negotiating terms.

A solution for the enterprise

Giving users the ability to gain access to all commercial property data gives them a wider, more detailed view of the market as well as an understanding of the risk profiles that producers are sending. By providing an automated process to ingest and prepare data, insurers are afforded a more efficient and flexible way of consolidation that essentially helps eliminate errors, cuts costs and promotes growth as companies can now allocate resources to address other areas of the business. Ultimately, automation and machine learning provide insurers with the ability to process submissions at a much higher rate of around 80%.

While giving data access to individuals within the company is beneficial, expanding that access in the form of outsourcing can create a number of different security concerns. Many insurers are operating and sharing data globally, making security and compliance with regulations like GDPR an absolute necessity. Outsourcing is nearly impossible under GDPR due to the heightened risks in sending and having external sources manage large amounts of customer data. Insurers need to show due diligence in not only securing their own data but their customers’, as well. In place of outsourcing, we are now seeing data management and storage platforms incorporating heightened security and data integrity into the design, ensuring these tools meet security standards such as ISAE 3402, SSAE 16, AES at rest and SSL/TLS in transit and ISO 27001. Meeting the standards not only helps to prove compliance with regulatory requirements, it also shows customers that insurers are taking their data privacy demands seriously.

Looking ahead

For the commercial property insurer, it is of the utmost importance to have the ability to prepare and manage complex data sets with an easy, quantifiable solution. Emerging solutions across the industry will enable insurers to make fast, appropriate decisions required to address the always-changing market and expand the business.

See also: How Machine Learning Transforms Insurance  

With the introduction of technology such as artificial intelligence and blockchain, combined with machine learning, the realm for new directions provides the insurance industry an unprecedented opportunity to collaborate. While these changes will continue to bring us new and improved methods to get things done faster and more efficiently, one thing is certain, ambitious commercial property insurers are already discovering collaborative initiatives to establish concept cases.

Three Key Takeaways

  • The current processes are putting insurers behind their competitors in the commercial property market because they typically process 20% to 30% of the submissions received.
  • Machine learning is allowing insurers to triage, screen, prioritize and score submissions much faster and with a higher output rate.
  • The result is more completed submissions, which leads to the ability to be first to market.