Tag Archives: ml

‘Explainable AI’ Builds Trust With Customers

Artificial intelligence (AI) holds a lot of promise for the insurance industry, particularly for reducing premium leakage, accelerating claims and making underwriting more accurate. AI can identify patterns and indicators of risk that would otherwise go unnoticed by human eyes. 

Unfortunately, AI has often been a black box: Data goes in, results come out and no one — not even the creators of the AI — has any idea how the AI came to its conclusions. That’s because pure machine learning (ML) analyzes the data in an iterative fashion to develop a model, and that process is simply not available or understandable. 

For example, when DeepMind, an AI developed by a Google subsidiary, became the first artificial intelligence to beat a high-level professional Go player, it made moves that were bewildering to other professional players who observed the game. Move 37 in game two of the match was particularly strange, though, after the fact, it certainly appeared to be strong — after all, DeepMind went on the win. But there was no way to ask DeepMind why it had chosen the move that it did. Professional Go players had to puzzle it out for themselves. 

That’s a problem. Without transparency into the processes AI uses to arrive at its conclusions, insurers leave themselves open to accusations of bias. These concerns of bias are not unfounded. If the data itself is biased, then the model created will reflect it. There are many examples; one of the most infamous is an AI recruiting system that Amazon had been developing. The goal was to have the AI screen resumes to identify the best-qualified candidates, but it became clear that the algorithm had taught itself that men were preferable to women, and rejected candidates on the basis of their gender. Instead of eliminating biases in existing recruiting systems, Amazon’s AI had automated them. The project was canceled.

Insurance is a highly regulated industry, and those regulations are clearly moving toward a world in which carriers will not be allowed to make decisions that affect their customers based on black-box AI. The EU has proposed AI regulations that, among other requirements, would mandate that AI used for high-risk applications be “sufficiently transparent to enable users to understand and control how the high-risk AI system produces its output.” What qualifies as high-risk? Anything that could damage fundamental rights guaranteed in the Charter of Fundamental Rights of the European Union, which includes discrimination on the basis of sex, race, ethnicity and other traits. 

Simply put, insurers will need to demonstrate that the AI they use does not include racial, gender or other biases. 

But beyond the legal requirements for AI transparency, there are also strong market forces pushing insurers in that direction. Insurers need explainable AI to build trust with their customers, who are very wary of its use. For instance, after fast-growing, AI-powered insurer Lemonade tweeted that it had collected 1,600 data points on customers and used nonverbal clues in video to determine how to decide on claims, the public backlash was swift. The company issued an apology and explained that it does not use AI to deny claims, but the brand certainly suffered as a result.

Insurers don’t need to abandon the use of AI or even “black-box” AI. There are forms of AI that are transparent and explainable, such as symbolic AI. Unlike pure ML, symbolic AI is rule-based, with codes describing what the technology has to do. Variables are used to reach conclusions. When the two are used together, it’s called hybrid AI, and it has the advantage of leveraging the strengths of each while remaining explainable. ML can target pieces of a given problem where explainability isn’t necessary.

For instance, let’s say an insurer has a large number of medical claims, and it wants AI to understand the body parts involved in the accident. The first step is to make sure that the system is using up-to-date terminology, because there may be terms used in the claims that are not part of the lexicon the AI needs to understand. ML can automate the detection of concepts to create a map of the sequences used. It doesn’t need to be explainable because there’s a reference point, a dictionary, that can determine whether the output is correct. 

See also: The Intersection of IoT and Ecosystems

The system could then capture the data in claims and normalize it. If the right shoulder is injured in an accident, symbolic AI can detect all synonyms, understand the context and come back with a code of the body part involved. It’s transparent because we can see where it’s coded with a snippet from the original report. There’s a massive efficiency gain, but, ultimately, humans are still making the final decision on the claim.

AI holds a lot of promise for insurers, but no insurer wants to introduce additional risk into the business with a system that produces unexplainable results. Through the appropriate use of hybrid AI, carriers can build trust with their customers and ensure they are compliant with regulations while still enjoying the massive benefits that AI can provide.

What’s Wrong With Commercial Auto?

Commercial auto is one of the biggest, most problematic cost centers in the insurance industry. Its loss ratios are incredibly high — and growing — particularly in comparison with other P&C lines. Carriers struggle to make money on the line, and they frequently incur fairly substantial losses.

With fewer claims than most other lines, why is commercial auto such a problem? Because claims are much larger; carriers may be looking at paying out $200,000 vs. $20,000 on different types.

Let’s examine the issues and see how we can turn commercial auto into a revenue generator instead of a black hole.

Commercial Auto by the Numbers

According to the National Association of Insurance Commissioners, commercial auto represents $39 billion in earned premiums. Yet, it pays out approximately $28 billion in losses, which excludes all of the internal administrative costs. Some folks in the industry thought that losses might go down this year due to fewer cars on the road, given COVID-19, but this has not been the case. One large carrier recently shared with me that they’ve seen a similar level of claims (both in terms of frequency and severity) because many commercial trucking organizations are working nonstop to keep the supply chain going.

If even a pandemic won’t lower the loss ratio, are we doomed? Let’s break this down a bit further.

Of the $28 billion in losses, roughly 60% went to medical costs associated with bodily injury. There is not much that can be done here from a carrier standpoint, short of offering slightly lower premiums for companies that incorporate the strictest safety standards and tools like electronic logging devices. Even this technology can be a double-edged sword given that the more advanced vehicles tend to be more expensive to fix in the event of an accident. While measures can sometimes minimize the severity of injury, accidents still happen.

Carriers have seen an uptick in accident rates involving bodily injury or death over the past 10 years, in part because more commercial vehicles are on the road traveling more miles each year. Then there is the role of drivers themselves. The trucking industry has a remarkably high turnover rate. For example, in the third quarter of 2019, large truckload carriers’ turnover rate increased to an annualized rate of 96%, according to Trucker.com. As companies basically overhaul their entire workforce each year, many don’t invest the resources into in-depth training programs, leading to less experienced, more accident-prone commercial drivers on the road.

Driver issues withstanding, the non-medical costs still leave $11.6 billion in losses. Where does it go? Very simple: Legal costs account for 40% of losses; the system is broken.

Losing Battle

Litigation tends to happen when claims are not resolved quickly or when something is perceived as unfair. While such concern is perhaps understandable, in practice it is not so innocent. A growing body of attorneys are ready to sign on to “help” plaintiffs, initiating cases that should never be filed, and they have become quite proficient at securing huge settlements.

The lawyers who specialize in auto claims know exactly what to look for and can be quite convincing in wooing potential clients and later in threatening the carriers with which they are attempting to negotiate. For example, the lawyers might spot something not related to the specific incident but that could be tied to the company. A savvy attorney would tack on additional charges, such as negligence, on top of bodily injury, pain and suffering. Tactics like these drive settlements higher. Complicating matters, states have very different statutes on bad faith suits filed by attorneys. Some states are more prone to settling cases early, and, as a result, lawyers in these areas increase the number of suits they file — which increases the cost for carriers.

One thing that many insurance firms and self-insured enterprises are just realizing is that plaintiff attorneys are rapidly becoming data-aware and using that awareness in a highly sophisticated and strategic way. Once upon a time, plaintiff attorneys were good at qualifying clients that they had a high degree of confidence could return them a large settlement. While that’s still the case, in the last half decade or so, even moderately sophisticated plaintiff firms have compiled significant datasets on enterprises and insurance companies, in many cases down to the general actions taken at an adjuster level. They use this data to plan their litigation strategies, select the most effective partner and manage each step of the process in an intelligent way. The result is that carriers and self-insured enterprises that do not have similar data-savvy practices are essentially being bled dry because they have nothing to counter this advantage.

See also: The Digital Journey in Commercial Lines

Attorneys are more than willing to try their hand in court, and juries can be quite sympathetic to plaintiffs they feel were wronged by a big company. As a result, there has been a certain degree of social inflation, as jury awards can rise astronomically if for no other reason than a desire to help the little guy fight back against the “evil” corporation — and winning verdicts keep going up. According to Shaub, Ahmuty, Citrin & Spratt, the median of the top 50 single-plaintiff bodily injury verdicts in the U.S. nearly doubled from 2014 to 2018 (moving from $27.7 million to $54.3 million).

As it stands today, there is tremendous variability in jury awards, just as there is with out-of-court settlements. Looking across claims, there might be very little difference in the facts of the case, yet one plaintiff walks away with millions while another receives a much smaller verdict. Carriers are often unwilling to risk the chance of coming out on the wrong side, hence agreeing to a settlement that may be uncalled for.

It is clear that litigation is the most significant hurdle to better loss ratios across the commercial auto line. If we can reduce, standardize or eliminate costs associated with litigation, the industry would be in a much better position.

A Process Evolved

New technologies, artificial intelligence (AI) and machine learning, in particular, can help. For example, you may want to know the likelihood of attorney involvement based on several claim factors, or you want to know which attorney is involved and what kind of settlements he or she negotiates for similar claims. But most importantly, you want to know what actions to take to prevent attorney involvement. AI and machine learning applications are emerging that can identify claims early in their life cycle that need the most attention.

Imagine how powerful it would be if you had an application that inherently understood the intricacies of commercial claims — one that would warn you of claims that were in danger of slipping to an attorney. Solutions are hitting the market that leverage capabilities like natural language processing and deep learning techniques to analyze hundreds of data points hidden within claims. They now can tap into structured data as well as the really interesting unstructured data, like notes or police reports, as well as decoding the sentiment of claimants. This collection of data provides pretty telling clues as to how a claim might progress.

An adjuster could get an alert about aspects of the claim that are troublesome, access to detailed attorney scores and ratings in case the claim escalates and, if need be, the optimal time to settle for a favorable outcome. With this information, the adjuster could take immediate action to head off the problem.

As applications get smarter, they will be able to determine what a claim settlement should look like and why with a much higher degree of certainty based on similar claims. This can be instrumental in the adjuster’s or defense counsel’s ability to negotiate with the claimant’s counsel. Armed with this kind of hard data, the organization could walk into settlement negotiations in a much stronger position and begin to counter the formattable data advantages that plaintiff attorneys have been amassing.

AI and machine learning systems also help organizations close claims faster, and, in doing so, relieve some of claims management teams’ administrative burdens. Additionally, by closing claims quickly and fairly, claimants receive settlements faster, return to their everyday lives sooner and thus generally wind up in a better financial position without ever involving an attorney.

Considering the high loss ratios of commercial auto insurance today and the propensity for them to increase further, emergent AI-based applications are our best hope for improving profit margins and repairing the commercial auto line.

As first published in Claims Journal.

Property Claims: It’s Time for Innovation

The personal and commercial property claims process has traditionally lagged well  behind other segments of P&C insurance in the adoption of technology and innovation. That officially ended in 2020, aided by a global pandemic that changed virtually everything about life and business as we knew it. Understanding the factors behind the historical lack of innovation in property claims provides insights into why and how this segment is suddenly undergoing such rapid transformation.

Auto vs. Property Claims Process Transformation

When compared with the recent impressive rate of change in auto claims, property claims appeared to be a more of a laggard than it really was – but a laggard nonetheless. To put this in perspective, U.S. auto insurance policies, premiums and claims in 2019 were approximately four times larger than property. Further, auto claims are generally more visible and more consequential to the public than property claims. And the auto claims process was broken until about 1990, with the emergence of direct repair programs enabled by internet and database technologies, so the transformation has been that much more obvious and impressive.

Industry Fragmentation

The property claims repair market is characterized by extreme fragmentation, which exceeds that in the auto insurance claims industry. This is due to several factors: 

  • the relatively large number of service providers specializing in distinctly different major damage types, especially managed repair networks, as well as independent contractors, in general
  • the complexity of property claims themselves, which involve the coordination of numerous general and specialty provider types for a given claim 
  • the proliferation of task-specific software solutions, which are generally not integrated with one another
  • the smaller influence of property insurers on the repair process as compared with the influence that auto insurers have (because of less consolidation of property insurers and because they collectively represent only about 33% of repair industry revenue while auto insurers represent almost 90% of collision repair revenue)

A high-level comparison of market fragmentation of third-party auto and property claims repair provider markets provides another important explanation of the emerging transformation in property claims. The collision repair industry has undergone significant consolidation both in terms of the numbers of repair shops and shop ownership – and consolidation continues. Since 1990, the number of U.S. repair locations has fallen roughly 50% to approximately 32,000. Moreover, consolidators have created large multi-location, multi-regional and national MSOs (multi-shop operators) and now control almost 30% of the repair industry revenue. Private equity investments and relatively inexpensive debt have provided the enormous pools of capital required to enable this consolidation.   

See also: Key Advantage in Property Underwriting

Property Claims Ecosystem

In studying the property claims, mitigation and restoration ecosystem, we identified 110 companies with material market share, which we grouped within nine distinct categories:

  • Software applications for:
    • Property estimating
    • Restoration management
    • Claim management platforms
    • Accounting/financial, measurement, documentation, communication and productivity
    • Payment solutions
    • Imaging/aerial inspection
  • Services:
    • Third-party administrators (TPAs)
    • Property claims adjusting and estimating
    • Managed property repair networks

Industry Consolidation

When we researched corporate ownership profiles for these 110 firms, we discovered that 45 – or 39% of them – are funded or controlled by private equity, venture capital or a few strategic investors. While there is some such investor activity in every one of the nine segments, it is most pronounced in managed property repair networks, claims management platforms and imaging/aerial inspection verticals.

These investors are fueling consolidation in these segments in much the same way as they are in the auto claims ecosystem, and will spur greater adoption of cost-effective and process innovation technologies. This is already evidenced by the emergence and adoption of artificial intelligence, computer vision, augmented, virtual and extended reality, machine learning and natural language processing across property claims.


Emerging Property Repair Market Opportunity

The property repair industry is 40% to 50% mature, while we estimate the auto claims industry is approximately 80% mature. This is partially illustrated by direct repair claims penetration of the collision repair industry, which is at or over 50% for carriers with higher market share (and more for some auto carriers) versus less than 10% on average for property repair.

Homeowners property insurance claims and ecosystem software and technologies market, viewed holistically, represent a significant and mostly unaddressed market opportunity. The situation closely parallels the auto insurance claims process and collision repair markets of 1990, which saw technology and economics drive vendor consolidation and carrier adoption of managed national repair programs, which were enabled by automated estimating software development, digital communications, imaging and end-to-end claims workflow tools.

Property Claims Solution Platforms

Property insurance carriers increasingly will be seeking technology-driven end-to-end property claims management solutions featuring;

  • connectivity between all parties from report of loss to remediation to payment and closure
  • hybrid insourced/outsourced carrier claims and repair network management capabilities, including  universal, standardized contractor onboarding, performance metrics, automated skills/needs matching, user reviews and vendor rankings.
  • integration with Guidewire’s claims platform or similar partner ecosystems

Property Claims Technologies

Artificial intelligence (AI), machine learning (ML), robotic process automation (RPA),computer vision (CV), natural language processing (NLP), aerial imagery including drones and digital payments are being aggressively adopted across the P&C insurance claims process, and specifically property.

  • Smart home technology adoption will mitigate and in some cases eliminate claims and losses; Bain Capital predicts that in just five years there will be 50 billion connected devices and a trillion by 2030. According to Statista Market Forecast, the global smart home market was valued at $55.65 billion in 2016 and is projected to reach $174.24 billion by 2025, growing at an annual rate of nearly 14%. While 32% of homes currently have a smart device, that number is expected to reach 52% by 2025.
  • The impact of these technologies to the property claims and restoration industries is already — and will become even more — significant
  • As residential policyholders become more comfortable with self-administered smartphone photo and video inspections of property damage reported directly, insurers will gain more control over the restoration assignment process, which will promote the use of national repair networks (and the claims management software that can manage the end-to-end process)
    • It is estimated that the use of photo inspection services can reduce field claims cost from an average $550 down to between $60 and $90 and the cost of technical inspections from $550 to $300
    • Technical inspections or VAIP (virtual adjusting and inspection programs) will fuse services, including the use of a licensed adjuster. Claims will offer faster cycle times and savings of 35%.
    • Providers of satellite and aerial images, including drones, are gaining in importance in the residential property damage identification, validation, damage assessment and repair estimation process.
    • Satellite and aerial imagery are increasingly being used by the property insurance industry for catastrophe planning and response, including damage evaluation and estimation.

Property insurance carriers now seek to avoid the effort and responsibility of managing restoration contractor selection or oversight but require a complete end-to-end workflow management platform to achieve their goal.

See also: How to Pursue Innovation in a Crisis

The property insurance claims and repair industries continue to move through a multi-segment structural transformation caused by prevailing market conditions, including industry fragmentation, consolidation, investments, revenue and geographic scale, end-to-end technology and software integration, emerging technology adoption and claims process improvement. Companies and investors that recognize the numerous opportunities presented by this transformation and solve for these dynamics are likely to be the future industry leaders.

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.