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AI in Commercial Underwriting

Today’s underwriters have more variables to contend with, more submissions, more competition and more data of all kinds to deal with than ever before. That’s why more and more insurance firms are deploying AI in commercial underwriting.

Machine learning (ML) and AI are incredibly well suited for helping to deal with the masses of data that underwriters now face. These technologies are changing underwriters’ working lives for the better and delivering huge benefits to businesses and the insurance industry as a whole.

In this article, we’ll explore five key ways you can implement AI and ML in the underwriting process and the results they can achieve. Without further ado, let’s get started.

1.  Processing underwriting submissions

Although efforts have been made to streamline submission processing, many lines of business in the insurance industry still have to deal with large volumes of documents that need to be processed manually. Until now, that’s just been part of the job — and a time-consuming, laborious one.

New applications of AI in commercial underwriting can give great assistance in extracting information from PDFs, printed documents, emails and even handwritten documents, reducing the amount of work underwriters need to do by hand. Optical character recognition and natural language processing are now sophisticated enough to identify the required data in a document, extract it and even perform a degree of evaluation. These advances in text extraction and analysis are opening up efficiencies in underwriting processes, expediting workloads that had previously been a burden to insurance professionals. Time saved on submissions processing is time gained for more rewarding work that makes better use of underwriters’ skills and helps to develop the business.

2. Making risk appetite decisions

As you know, reviewing submissions for viability is another task that can take up a lot of an underwriter’s time. Analyzing the submission and all the related risk data, making the decision whether to underwrite it – it all takes time and effort. And it’s another area where you can deploy AI in commercial underwriting to achieve great results.

Machine learning can now offer underwriters valuable assistance in the decision-making process. Using data on previous applications that have been approved or rejected, these systems build an understanding of which are likely to be viable and which aren’t. The systems can automatically decline certain activities described in the application as free-form text, if deemed too risky or otherwise unviable. Using text classification, these activity descriptions can be automatically mapped onto their corresponding industry codes, based on a given standard. If an application is found to be viable according to the system’s judgment, it can also recommend the most appropriate product according to your historical data. Once again, this valuable assistance can be a real asset for time-pressed underwriters.

3. Submission assignment and triage

Some underwriting submissions, in certain lines of business, require extra attention during processing. They need to be prioritized, but, unlike with other submissions, this can’t be done using simple, blanket rules such as their policy effective date. Underwriters need to look in greater depth to decide their priority.

Using AI in commercial underwriting can help here, too. Optimization and forecasting technologies can assist in assigning these submissions to the most appropriate underwriter. Predictive modeling can also rank submissions according to their estimated closing ratio or some other key performance indicator (KPI). For instance, AI could decide to rank one application highly because you’ve recently been successful at closing business with that broker. These innovations have a tangible impact on how well your business operates and your bottom line: Submissions are allocated more effectively, and your overall closing ratio improves.

See also: ‘3D Underwriting’ in Life Insurance

4. Evaluating risk profiles

To evaluate the risk involved in a submission, underwriters must often invest considerable time in research. They must research and weigh all kinds of information to properly evaluate these risk profiles. Sifting through the wealth of information available, in myriad formats, can be like searching for a needle in a haystack — until now.

Today’s intelligent tools can search through many types of structured (processed and labeled) data as well as raw, unstructured data and aggregate relevant information for underwriters to use. For instance, an underwriter may use this system to search through a database of property inspections, to compare similar cases of structural damage and their results. These systems also make it far easier to retrieve similar past applications to see patterns and learn from earlier experience. Now your business never has to make the same mistake twice.

As we said earlier, AI is the master of dealing with large volumes of complex data, so, when it comes to locating and surfacing valuable items of information like this, AI is in its element. The benefits for underwriters and businesses are huge here: They can be better informed and more confident in their risk evaluations.

5. Coverage recommendations

Toward the end of the underwriting submissions review process, it’s time to make a judgement: what coverages will be recommended? AI-powered systems are capable of assisting end-to-end, so they have much to offer at this point, too.

Recommender systems can help with coverage judgments. By analyzing previous applications, they can get a sense of what the appropriate coverages, with limits and deductibles, might be and offer suggestions the underwriters can use to make their final decision. On a business-wide scale, this means your product and coverage recommendations will be better aligned with clients’ needs and their risk profiles.

Ready to deploy AI in commercial underwriting?

All the use cases we’ve outlined here are available to businesses right now, so if you want to start deploying AI in the underwriting process, you can start obtaining the benefits without delay.

As the industry evolves in the coming years, we’re certain that AI will become an even more useful assistant to underwriters all over the world. And, as new applications of AI in commercial underwriting are developed, we look forward to telling you all about them.

This article was originally published here.

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.

‘3D Underwriting’ in Life Insurance

After 9/11, a revolution happened in airline travel. Airline security tightened far beyond what we had previously known. In addition to new carry-on guidelines, travelers were subjected to more frequent individual screenings. More items had to be removed from our bags and examined. Electronics had to be turned on. Our shoes started coming off. The TSA needed to know us personally before we were allowed to fly.

The revolution, of course, created lines. We had to get up earlier to get to the airport to account for two hours in security. Shorter flights were no longer worth it. “I can drive there in less time.”

So, in 2019, the TSA, doing anything it could to improve the line situation, began using computed tomography (CT) scans in many busy airports. CT scans would change 2D baggage scans into 3D scans, allowing the operator to look at an item in greater detail without tagging a bag to be opened and checked by hand. The ability to “see” the hidden information would shorten lines and streamline the travel experience.

When COVID hit, lines evaporated. Air travel hit rock bottom. Lines are mostly not an issue right now, but public safety is now an even greater issue. In China, for example, large scanners are being used to check human temperatures on anyone traveling. Those with high temperatures are tagged and removed for further screening.

This concept bears a close look for all insurers, and especially for life insurers. In what ways can we use technology to know applicants and policyholders instantly, using that information to protect them and our level of risk in the process? Can we build flexible frameworks for accelerated and fluidless underwriting that will allow us to tackle new issues as they arise and capitalize on new data as it becomes available? Life insurers, group providers and voluntary benefits carriers are entering new regions of opportunity through new doors of data capability.

In Majesco’s latest thought-leadership report, Rethinking Life Insurance: From a Transaction to a Life, Health, Wealth and Wellness Customer Experience, we examine the nature of the purchase experience. Our recent survey across all age groups segmented into two groups – younger (millennial and Gen Z) and older (Gen X and Boomer) generation – painted a picture of a population that is growing in its desire to buy, growing in its goals to stay healthy and wanting the purchase to happen.

Every experience holds data

Today, nearly every aspect of the B2B, B2B2C and B2C customer experience has a level of intelligence that has created a wealth of data about customer activity, behavior and preference. From smart speakers to smart watches, phones, appliances, outlets and more — sensors and signals are everywhere. And, with customers’ permission, sensors are measuring nearly every aspect of their lives. The result is that we now have the data to capture the instantaneous 3D view instead of the 2D view. But insurers must strategically invest in ways to capture and master this data to transform customer experiences in an age of instant digital engagement, delivery and satisfaction. 

The use of data for life insurance is crucially important. Interestingly, the insurance industry has been capturing behavioral insights from customer interactions—offline—for many decades, before technology simplified managing customer relationships. Companies unfortunately didn’t know how to optimize their use of the data before now. That must change if insurers are to survive.

See also: Key Advantage in Property Underwriting

Can data improve the experience?

To meet the needs and expectations of today’s customers, insurers must create a radically different insurance experience, moving from a reactive approach to using real-time data, artificial intelligence (AI), machine learning (ML) and behavioral science to make processes and transactions simple, convenient, transparent and fast, like in other businesses. Encouragingly, our research found that the younger generation is ready and willing to use and share most new data sources for buying and rating life insurance. This willingness will be a key to unlocking sales. 

Adding to this market opportunity, Majesco’s survey data showed that even those Gen Z/Millennials who currently DON’T have life insurance are open to these new data sources being used, nearly the same as their peers who DO have life insurance.

Insurers that are not actively planning and building capabilities to use new sources of data will be rapidly left behind. 

MIB’s February 2020 activity report highlighted that pandemic-related demand for life policies pushed application activity to its highest level for the period since 2015. As noted previously, online, “fluidless” life insurance has dramatically increased during the first three months of 2020.

To accomplish this major experience transformation and bring the decision and the purchase into the same moment, insurers are moving from an underwriter-centric view to a digital, data-driven, accelerated and sometimes fluidless underwriting process. Accelerated underwriting is becoming widespread for term insurance. As shown in Figure 1, the interest in products that use dynamic underwriting and pricing is over two times higher in the younger generations – a significant difference that many insurers are unable to react to today. Once again, our data showed the younger generations who DO NOT currently have life insurance are even more interested in this option than their peers who DO have life insurance (41% vs 35%). Market and growth opportunities await for those who accelerate the move to dynamic underwriting and pricing with new data sources.

Figure 1: Interest in products that use dynamic underwriting and pricing

With the proper use of data, we don’t stop people at the checkout counter

In the traditional underwriting model, we ask people to come to the checkout counter for a price check, then send them home until we verify their ability to buy by gathering lots of medical and personal data. Companies are surprised with their level of not-taken policies. But if you put yourself in the customer’s shoes, why do you want to go through the extra hassle and time, particularly when other options are emerging. Using this picture, we can correctly assume that accelerated underwriting is a modern-day non-negotiable capability that will fit the desire for instant gratification at the point of need.

As a start, some insurers are reducing attending physician statements (APS) and paramedical exams (providing bodily fluids) and using more third-party data and predictive analytics-based models to automate and enhance the underwriting process.

Others are bringing in behavioral data from fitness and wellness programs, social media and new sources with AI and machine learning algorithms to create “smart, automated underwriting” that is continuously learning and adapting. All of this is redefining the process and cycle time and is creating a completely different customer experience.

The right ingredients in the right place at the moment of opportunity

Here is where technology acts as the enabler. For life insurers to fit their products into lifestyles and experiences instead of traditional transactions, several components must be in place.

Cloud Use for Digital Enablement: The point of sale (and marketing) must be well-integrated into current life processes. Often, this means partnerships or channel expansion that will require digital integration using application programming interfaces (APIs) and a cloud-based environment.

Data Integration (and AI / ML) for Accelerated and Fluidless Underwriting: Insurers need to create ways to go fluidless and physician-less so they can automate decisions in real time.

Ecosystem Philosophy: Insurers need to ramp up quickly. They can do this by partnering with ecosystem developers that can give them access to the data sources, channels and technology opportunities that will contribute to quick transformation.

Innovate for the Future: Innovate. Replicate. Reach. Learning the lessons from the past, that good data doesn’t necessarily get used effectively, insurers need to place themselves on a course for optimal data usage across the enterprise. They need to innovate how they use data to get a 3D picture for accelerated underwriting. They need to replicate greenfield and startup methodologies that transform data accessibility into improved experiences. And they need to reach growing and untapped markets with products that sell at the point of life experiences, instead of relying on traditional sales tactics.

See also: Underwriting Wildfire Takes Extra Care

Industry status quo is no longer an option. Your customers, particularly the younger generation who will be your dominant buyers, are expecting all of this and more. They want a customer relationship with a broader value to make their lives better across life, health, wealth and lifestyle needs.

How do your strategies align to what customers want? What plans are you taking to improve your odds of success? 

Are you ready? Your customers are.

For more insights on how you can end transactional thinking and begin capitalizing on customer life experiences, be sure to download, Rethinking Life Insurance: From a Transaction to a Life, Health, Wealth and Wellness Customer Experience.

How to Evaluate AI Solutions

After almost a decade working in a large, global bank, I can speak to the challenges faced by all three lines of defense in trying to combat financial crime. I can also attest to the effect these processes had on our clients. As a front-line corporate relationship manager, I frequently had to navigate the know your customer (KYC), remediation and payment screening process for my clients. 

Not only was this an incredibly time-consuming and frustrating process on an organizational level, but more painful was the deleterious effect it had on our clients and their business: Crucial payments to vendors were delayed unnecessarily; accounts took months to open and required incessant back and forth among multiple parties; and account fundings/transactions always came down to the wire because of basic due diligence, regardless how much work you tried to do ahead of time.

Much of the process that required our intervention seemed mundane, repetitive and inefficient, which compounded everyone’s frustration. 

Sound familiar?

These types of repetitive, mundane tasks are ideally suited to be outsourced to artificial intelligence, which the industry seems to now realize. 

Artificial intelligence can be an incredibly valuable tool, in that it can offload mundane tasks, provide insight into customer and employee behavior, create more standardization and help reduce or manage costs.

But as technology becomes increasingly sophisticated, there are many factors to weigh in the decision-making process. 

After countless conversations with stakeholders and decision makers in the industry, I have learned that there are five main concerns when implementing regulatory technology, especially AI technology, in the financial sector: 

  1. How transparent is the AI? 
  2. What if the AI learns the wrong behaviors, such as bias?  
  3. Does it have more than one purpose? What is the road map?
  4. Is it better than what I have now? More accurate, faster, more standardized, more cost effective? Can “better” be tested quantifiably?
  5. What are the redundancies? How will this technology affect my operational resiliency?

Let’s look at each point in order.

1. How transparent is the AI? 

While this seems like a straightforward question, “transparent” really encompasses three separate factors:  

  • Will my team and our stakeholders be able to understand how it works? 
  • Will I be able to easily demonstrate to audit, the board and regulators that it’s doing what it’s supposed to do?
  • Can I get a snapshot of what is happening at any given moment? 

See also: Stop Being Scared of Artificial Intelligence

All of the major regulators have stipulated that artificial intelligence solutions be explainable and demonstrable. Both of these concepts are rather self-explanatory but still worth exploring.

Explainability 

It’s not sufficient for your compliance team to understand how the AI makes decisions. They also need to be comfortable explaining the process to key stakeholders, whether they are board members, the internal model committee, audit or the regulators. 

If your technical team can’t understand the technology or how decisions are made, or if the vendor claims confidentiality to protect its IP, this is a cause for concern.

Demonstrability

Like transparency, demonstrability captures a few components – it means you have to be able to demonstrate:

  • What decisions the AI has made; 
  • What changes you’ve made to how the AI makes decisions; and
  • Who made the changes.

This is where an audit trail comes into play. First of all, is there one? If so, is it immutable, and does it capture all actions in the AI or just some of them? Is it exportable in report format, and, if so, is the report readable and can it be easily understood?

Compliance is a data-driven world, and the risk associated with being deemed non-compliant is substantial. Being able to capture and export changes to, and decisions made within, your AI is crucial to your relationships with your stakeholders.

As personal liability expands in the corporate world, board members and committees increasingly require an understanding of not only how compliance risk is being mitigated, but also clear evidence that it’s being done, how and by whom.

2. What if the AI learns the wrong behaviors, such as bias?

The underlying questions here, without detracting from the very serious concern about embedding existing unconscious bias into your AI, are:

  • If the AI is wrong, or my requirements change, can I fix it? How easily? 
  • What impact will tweaking the AI have on everything it’s already learned?

An industry journalist recently asked me if I thought bias was a problem with AI. My answer to her, and to all of you, is that AI simply learns what’s already happening within your organization. As a result, unconscious bias is one of the things that AI can learn, but it doesn’t have to be a problem. 

While you can’t really prevent AI from learning from past decisions (that’s kind of the point), good technology should enable you to identify when it’s learned something wrong, and to tweak it easily to prevent bad decision-making from becoming embedded into your AI’s decision making.

This ties in to the need for transparency and reporting. It’s not only necessary to see how decisions are made, you also need to be able to prevent poor decisions or bias from being part of the AI’s education. And all of these things need to be documented. 

When testing new vendors, once the AI engine has been trained initially for your proof of concept, you should be able to clearly understand the findings and be able to make changes at that time (and thereafter). You will very likely be surprised by some of the ways decisions are currently being made within your organization. 

For example, at Silent Eight, our technology investigates and solves name and transaction alerts for banks. This work is typically done by teams of analysts, who investigate these alerts, and close them as either a true positive (there is risk here) or false positive (there is no risk). True positive alerts require substantially more time to investigate and close than alerts deemed to be false positives. 

Analysts typically have KPIs around the number of alerts they’re expected to investigate and close each week. 

By late Friday, the analysts are doing everything they can to make sure they meet this quota. As a result, it’s not unusual during the AI training process that the AI learns that 4pm on Fridays is a great reason to close out pending alerts as false positives. 

Obviously this is a good example of AI learning the wrong behavior and needing to be tweaked. It’s also a good example of mistaking correlation for causation, which is a topic worthy of its own examination on another day.

Today, as regulations are introduced and amended, you’re continually updating your policies to reflect these changes. It’s no different with artificial intelligence. It’s imperative that your AI engine is correspondingly easy to tweak, and that, when you tweak it, you don’t lose everything it has already learned. 

Thoughtful, well-designed technology should be built in a manner that makes it easy to update or amend part of the AI engine, without affecting the rest of the learnings. This is something you should both ask about, and test.

3. Does the AI have more than one purpose? What is the road map?

Many financial institutions have the dueling mandates to be both innovative and transform digitally, but also to rationalize vendors. So, when considering artificial intelligence solutions, which are often niche, it’s worthwhile finding out:

  • How the vendors decide to build out features;
  • Whether they are willing to customize their offering for you;
  • How reliably they’ve delivered on features in the past; and
  • Whether what’s on their road map adds value for you.

This way you can ensure that the decision you’re making is one that is future-proofed and set up for longevity.

See also: 3 Steps to Demystify Artificial Intelligence

4. Is it better than what you have now? 

Better can mean different things to different organizations and individuals. It’s typically tied into the problems you’re experiencing now, and what your organization’s strategic focus and priorities are.  When I ask clients and prospects what they mean by ‘better’ the answers I hear most commonly are:

  • Is it more accurate?
  • Is it faster?
  • Will it give me greater standardization?
  • Will it enable me to identify more risk?
  • Will it enable me to federate by jurisdiction?
  • Will it lead to greater efficiencies?
  • Is it more cost-effective?
  • Does it increase my visibility? I.e., is it transparent?

Once you’ve defined what “better” means to you and your organization, you need to find out from your prospective vendors if and how “better” can be tested quantifiably. 

5. What are the third-party dependencies? How will this technology affect my operational resiliency?

Operational resiliency and third-party due diligence have become a significant focus in the industry and can be a barrier to doing business.  Many regulators, including the EBA and the FCA, have issued guidelines on the topic, and continue to revisit it.

It’s vital to understand if a vendor is reliant on any other vendors in its tech stack, if it’s using open source code, what the deployment is (on premise, in the cloud, in a private cloud) and what security standards the vendor adheres to.

Take back the things you can control 

Right now, the financial services industry is beset by many challenges that are outside of its control, including low interest rates, working remotely, bad debt provisions and the increased new accounts and suspicious activity resulting from COVID. 

Your compliance costs and processes are a piece of the puzzle you can control. Good artificial intelligence technology will enable you to offload some of your mundane, repetitive tasks, freeing you and your team to focus on more complex risks and higher value projects. 

I recognize that artificial intelligence can be a bit daunting, and that it has a mixed reputation in the industry. However, if you’re armed with a dose of skepticism, have the right questions to ask and approach it with an open mind, you’ll be amazed by what it can do.

How ‘Explainable AI’ Changes the Game

Artificial intelligence (AI) drives a growing share of decisions that touch every aspect of our lives, from where to take a vacation to healthcare recommendations that could affect our life expectancy. As AI’s influence grows, market research firm IDC expects spending on it to reach $98 billion in 2023, up from $38 billion in 2019. But in most applications, AI performs its magic with very little explanation for how it reached its recommendations. It’s like a student who displays an answer to a school math problem, but, when asked to show the work, simply shrugs.

This “black box” approach is one thing on fifth-grade math homework but quite another when it comes to the high-impact world of commercial insurance claims, where adjusters are often making weighty decisions affecting millions of dollars in claims each year. The stakes involved make it critical for adjusters and the carriers they work for to see AI’s reasoning both before big decisions are made and afterward so they can audit their performance and optimize business operations.

Concerns over increasingly complex AI models have fired up interest in “explainable AI” (sometimes referred to as XAI,) a growing field of AI that asks for AI to show its work. There are a lot of definitions of explainable AI, and it’s a rapidly growing niche — and a frequent subject of conversation with our clients. 

At a basic level, explainable AI describes how the algorithm arrived at the recommendation, often in the form of a list of factors that it considered and percentages that describe the degree that each factor contributed to the decision. The user can then evaluate the inputs that drive the output and decide on the degree to which it trusts the output.

Transparency and Accountability

This “show your work” approach has three basic benefits. For starters, it creates accountability for those managing the model. Transparency encourages the model’s creators to consider how users will react to its recommendation, think more deeply about them and prepare for eventual feedback. The result is often a better model.

Greater Follow-Through

The second benefit is that the AI recommendation is acted on more often. Explained results tend to give the user confidence to follow through on the model’s recommendation. Greater follow-through drives higher impact, which can lead to increased investment in new models.

Encourages Human Input

The third positive outcome is that explainable AI welcomes human engagement. Operators who understand the factors leading to the recommendation can contribute their own expertise to the final decision — for example, upweighting a factor that their own experience indicates is critical in the particular case.

How Explainable AI Works in Workers’ Comp Claims

Now let’s take a look at how explainable AI can dramatically change the game in workers’ compensation claims.

Workers comp injuries and the resulting medical, legal and administrative expenses cost insurers over $70 billion each year and employers well over $100 billion — and affect the lives of millions of workers who file claims. Yet a dedicated crew of fewer than 40,000 adjusters across the industry is handling upward of 3 million workers’ comp claims in the U.S., often armed with surprisingly basic workflow software.

Enter AI, which can take the growing sea of data in workers’ comp claims and generate increasingly accurate predictions about things such as the likely cost of the claim, the effectiveness of providers treating the injury and the likelihood of litigation.

See also: Stop Being Scared of Artificial Intelligence

Critical to the application of AI to any claim is that the adjuster managing the claim see it, believe it and act on it — and do so early enough in the claim to have an impact on its trajectory.

Adjusters can now monitor claim dashboards that show them the projected cost and medical severity of a claim, and the weighted factors that drive those predictions, based on:

  • the attributes of the claimant,
  • the injury, and
  • the path of similar claims in the past

Adjusters can also see the likelihood of whether the claimant will engage an attorney — an event that can increase the cost of the claim by 4x or more in catastrophic claims.

Let’s say a claimant injured a knee but also suffers from rheumatoid arthritis, which merits a specific regimen of medication and physical therapy.

If adjusters viewed an overall cost estimate that took the arthritis into account but didn’t call it out specifically, they may think the score is too high and simply discount it or spend time generating their own estimates.

But by looking at the score components, they can now see this complicating factor clearly, know to focus more time on this case and potentially engage a trained nurse to advise them. Adjusters can also use AI to help locate a specific healthcare provider with expertise in rheumatoid arthritis, where the claimant can get more targeted treatment for a condition.

The result is likely to be:

  • more effective care,
  • a faster recovery time, and
  • cost savings for the insurer, the claimant and the employer

Explainable AI can also show what might be missing from a prediction. One score may indicate that the risk of attorney involvement is low. Based on the listed factors, including location, age and injury type, this could be a reasonable conclusion.

But the adjuster might see something missing. They adjuster might have picked up a concern from the claimant that he may be let go at work. Knowing that fear of termination can lead to attorney engagement, the adjuster can know to invest more time with this particular claimant, allay some concerns and thus lower the risk the claimant will engage an attorney.

Driving Outcomes Across the Company

Beyond enhancing outcomes on a specific case, these examples show how explainable AI can help the organization optimize outcomes across all claims. Risk managers, for example, can evaluate how the team generally follows up on cases where risk of attorney engagement is high and put in place new practices and training to address the risk more effectively. Care network managers can ensure they bring in new providers that help address emerging trends in care.

By monitoring follow-up actions and enabling adjusters to provide feedback on specific scores and recommendations, companies can create a cycle of improvement that leads to better models, more feedback and still more fine-tuning — creating a conversation between AI and adjusters that ultimately transforms workers’ compensation.

See also: The Future Isn’t Just for Insurtech

Workers’ comp, though, is just one area poised to benefit from explainable AI. Models that show their work are being adopted across finance, health, technology sectors and beyond.

Explainable AI can be the next step that increases user confidence, accelerates adoption and helps turn the vision of AI into real breakthroughs for businesses, consumers and society.

As first published in Techopedia.