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Intersection of AI and Cyber Insurance

Exhibitioners at the Century of Progress International Exposition held in Chicago from 1933-1934 touted washing machines and air conditioners as capable of bringing vast changes to our everyday lives. This optimism for future generations is inherent within the human psyche. As such, we often speak of artificial intelligence (“AI”) as a lofty, almost dream-like reality that awaits us in the not-so-distant future. 

But AI proliferates today and extends beyond the entertainment-based efficiencies embedded within Netflix and TikTok that we read about; attorneys apply AI to document review projects; vehicle manufacturers use AI to control a vehicle’s acceleration, speed and steering; hospitals and doctors are using AI to triage and diagnose patients; and biotech companies increasingly rely on AI to model the potential success of newly developed therapies and vaccines. 

Insurance carriers remain optimistic about the efficiencies to be gained by implementing AI-based applications into their workflows. The same is true for cyber insurance carriers, who over the last eight to 10 years entered the market to meet the needs of customers who seek protection from potential financial and operational ruin due to the rise of ransomware and other malicious activity perpetrated by cyber criminals. And, while AI is sure to benefit society when wielded properly, cyber carriers remain conscious that AI’s proliferation is a double-edged sword. Thus, cyber insurance will have an even greater role to play in an AI-dominated world.

The reasons are twofold:

First, harm from cyber attacks will be more widespread because of the threat posed by more sophisticated AI-based attacks. By using an AI-based attack, malicious actors will be able to operate in ways that are both highly efficient and highly scalable. For example, rather than disguising malware as an email attachment in a message from “your boss,” or hawking magic pills, a sophisticated AI-based attack may be capable of personalizing, instantaneously, the malicious email (or other vehicle) received by each target victim. 

Second, increasingly intelligent cyber attacks are likely to bring greater cost and consequences. Cyber-attacks today inflict financial harm and disrupt the productivity of the victim but generally do not alter people’s livelihood or society at large. We will see that blast radius grow exponentially in the future when malicious actors deploy cyber attacks against those AI-based systems that society increasingly relies on for day-to-day operations.

Look at the recent attack on the Colonial Pipeline and what it’s done to gasoline prices in the eastern U.S. Citizens’ freedom of movement may be jeopardized when a future cyber attack against a vehicle manufacturer not only disrupts assembly line production but also paralyzes entire fleets of autonomous vehicles operating on the vehicle manufacturer’s software. Or, in a more dire situation, if there is malicious disruption of the AI-based systems at the core of a vehicle’s control system. Disrupted AI-based hiring systems could also result in significantly slower access to available low-wage jobs. And patients may suffer or die when a hospital loses its ability to intelligently triage and provide treatment. In sum, the outcomes from a cyber attack could be devastating.

See also: Surging Costs of Cyber Claims

But the future is not entirely bleak. Cybersecurity firms and professionals continue to improve on threat detection and elimination tools by harnessing AI. These types of tools and software are capable of intelligently digesting data points gathered from both past and current attacks across a massive scale. Decreasing response time via the real-time adjustment of threat detection applications is among the myriad ways AI is changing the cybersecurity landscape. 

The adoption of AI by the insurance industry is also bringing about a paradigm shift. The most prominent example is Lemonade, a property and casualty insurer that makes decisions about policy underwriting and claims processing based entirely on AI. Lemonade went public via IPO in summer 2020; it raised $319 million in a single day. Opportunities for innovation abound.

As society absorbs AI into the framework of industry and people’s lives it should expect to reap enormous benefits but also protect those benefits by preparing for and managing attendant risks.

Six Things Newsletter | April 13, 2021

Microsoft Just Raised the Bar

Paul Carroll, Editor-in-Chief of ITL

While insurance has been steadily improving communications with customers through gradual adoption of chatbots, Microsoft just put another big item on the industry’s technology to-do list: speech recognition.

Microsoft’s announcement on Monday that it is buying speech-recognition firm Nuance for $16 billion means that insurers will have to confront the technology — likely sooner than they had expected. Big Tech has already been getting consumers accustomed to having their speech understood by devices, mostly via Siri and Alexa, and the Microsoft purchase of Nuance will push speech recognition into many business transactions. All industries, including insurance, will have to react as Big Tech again raises the bar for what constitutes a reasonable customer experience.

So, it’s worth spending a minute thinking about what speech recognition will — and won’t — change in insurance… continue reading >

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The pace of change has accelerated to hyper-speed, making digital insurance business models more important than ever. Learn what the Leaders are doing.
 

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SIX THINGS

The Future of AI in Insurance
by Karin Golde

Organizations hoping to deploy artificial intelligence have to know what problems they’re solving — no vague questions allowed.

Read More

10 Ways to Prepare for the Hard Market
by Jeff Arnold

In soft markets, differentiation can be challenging. But hard markets present an opportunity for the best insurance professionals to stand apart.

Read More

Digital Revolution Reaches Underwriting
sponsored by Intellect SEEC

The digital revolution in insurance, which began in distribution and then spread to claims, has now reached underwriting in a big way.

Read More

How to Deliver the ROI From AI
by Monte Zweben

A technology has emerged that can harness AI across all departments of a business like never before. It’s called a feature store.

Read More

Benchmarks, Analytics Post-COVID
by Kimberly George and Mark Walls

The pandemic introduced several variables that question the validity of actuarial models and benchmarks.

Read More

The Key to the Future of Mobility
by Bill Powers

Telematics can help solve some of the insurance industry’s oldest problems, but, first, insurers must win the client’s trust.

Read More

Time to Start Over on Secondary Towing
by Rochelle Thielen

The current system for secondary towing is excruciating. The only reasonable solution is to start over from scratch.

Read More

Webinar :
The Alarming Surge in Ransomware Attacks

sponsored by Tokio Marine HCC – Cyber & Professional Lines Group

Join Michael Palotay, Chief Underwriting Officer for Tokio Marine HCC – Cyber & Professional Lines, and Paul Carroll as they continue their discussion on ransomware, cyber attacks, and how businesses can protect themselves.

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MORE FROM ITL

April’s Topic: Agents & Brokers

Mark Twain reportedly once responded to a rumor of a serious illness by saying, “Rumors of my death have been greatly exaggerated.”  Insurance agents and brokers could have said the same thing over the past decade and will likely be parrying those rumors for years to come.

There’s no doubt that agents & brokers inhabit a world going digital and not every agent will migrate easily into the ever-more-digital world, but those who do will find the work more rewarding, both for themselves and for their ever-more-loyal clients.

Take Me There

The Alarming Surge in Ransomware Attacks

Join Michael Palotay, Chief Underwriting Officer for Tokio Marine HCC – Cyber & Professional Lines, and Paul Carroll as they continue their discussion on ransomware, cyber attacks, and how businesses can protect themselves.

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How AI Is Moving Distribution Forward

While artificial intelligence can improve almost all of the insurance value chain, most insurers are still not leveraging AI at its full capacity.

Adopting AI and implementing hyper-automated systems can help insurance distribution, in particular, become more efficient, accurate and secure — a benefit that both companies and the end consumer will see. From improving risk analysis and fraud detection to providing more sophisticated pricing and better customer insights for faster, more personalized customer advice and services, there are many ways in which AI helps move insurance distribution forward.

Improving operational efficiency

With today’s low interest rates, insurers can no longer depend on the financial earnings of their assets and need to find new margins in their operating models. They have more pressure to increase revenues while cutting overall operational costs. 

Deploying AI to everyday back-office processes can reduce the number of manual tasks insurers face, freeing them to spend more time on tasks that support their bottom line. Insurers are able to get more done in less time and often with improved accuracy.

Enhancing insurance distribution

According to McKinsey, 80% of value driven by advanced AI in insurance will come from marketing and sales alone (versus only 10% from better risk management and 3% from gains in operational efficiency). Today, insurers face several distribution challenges — moving from physical to remote and hybrid sales networks, learning how to strike the balance between technology and human sales, adjusting multi-channel and omnichannel sales, etc.

AI can enable a more fluid and personalized experience for customers from initial lead prioritization through needs analysis and advice, to automated underwriting. Additionally, integrated computer vision technology automates the underwriting process while detecting fraudulent documents, which drastically reduces the policy underwriting time for the policyholder and the operation costs for the carrier — a benefit for both parties.

See also: Stop Being Scared of Artificial Intelligence

Providing a better customer experience

By creating consumer-specific predictive models, AI helps policy providers enrich their recommendations to both potential and current policyholders, resulting in better synchronization between needs and offers, and superior service across the entire sales chain. 

For example, speech recognition combined with natural language understanding can interpret essential information from customer inquiries in real time. The AI can then provide contextualized and transparent recommendations for both advisers and agents. The result? Advisers and agents can act faster and more accurately, increasing the number of cross-sell opportunities and the win ratio of quotes.

A look ahead

AI will play into several trends that the industry will start to see unfold in the next five years.

Insurance products and service offerings will become more and more complex

As consumers’ needs continue to develop, so will the products and services required to address them. Five years from now, insurers will offer more complex services and, in turn, will need to be able to better explain these offerings to policyholders. This is where AI technology will be vital. AI will help guide advisers, agents or self-care portals in recommending the most relevant products for each individual. We will also see more embedded insurance offerings, with AI helping to pick and pair the consumer’s best options.

Insurance companies will feel the threat of Big Tech

In five years, the insurance business will be even more intermediated through digital platforms and marketplaces. The list of examples is already growing — Airbnb offers renter insurance, Amazon is offering delivery guarantees, Booking.com proposes travelers’ insurance. 

As insurers continue to compete with Big Tech, they need to match the competition’s standards by offering immediate, simple and adaptive policies with AI. Without full process automation on key distribution activities, traditional insurers will struggle to exist in this tech-focused ecosystem and will be challenged by full-digital players.

The industries where the competition is stronger and the insurers are more primed for innovation include personal lines of insurance such as auto, property and casualty and health insurance. In the future, we will need to see these industries take off with artificial intelligence to stay in the game.

See also: Pressure to Innovate Shifts Priorities

At Zelros, we believe that AI-enabled solutions will empower insurance players to keep up with the rising expectations of their customers. AI will give them the acceleration needed to have the real-time experience that everyone now expects when engaging with a brand.

Is It Possible to Scale Knowledge?

AI, machine learning, deep learning, natural language understanding, robotic process automation, intelligent process automation: Insurance has a lot of FUN (Frequently Used Neologisms).

The more terms are created and used, the less their meaning is clear, and this is exactly what is happening around AI. 

AI, or, better, machine intelligence, is a set of different technologies and techniques aimed at mimicking human behaviors. Not all are suitable to automate and optimize knowledge-based process such as claims or underwriting, where there are a lot of dense and complicated documents such as medical reports. So, how to scale and automate knowledge-based processes?

To define where to start, please consider:

  • Strategic Plan: Each and every AI initiative must be 100% aligned with the company strategy. All initiatives should be part of an internal ecosystem, to share knowledge and lessons learned and to redefine priorities.
  • Quick Wins: Begin where the use cases are not too complicated. Quick wins will fuel new wins and initiatives.
  • Measure, Measure and Measure: Make sure there is clarity about criteria for success.
  • The Right Skills: Pay attention to the teams and the right balance between outsourcing and internal staff, strengths and weaknesses. This is never a pure build-or-buy journey, but rather build-and-buy, where you can learn a lot from trusted advisers and providers.
  • Do, Learn and Adapt: Agility and flexibility are extremely important when it comes to projects involving technology and innovation. Mitigate the risk by understanding immediately when it’s time to adapt.
  • Organizational Impact: Do not underestimate the impact on the organization and the indirect variables that can influence the outcome of any initiative.
  • Internal and External Visibility: Make sure the initiative is well-represented both internally and externally. Doing so you will help attract the right people.

See also: Crucial Technologies for P&C During COVID

Technology adoption is no longer a choice. It is a must-have. Carriers that will not act immediately will be left behind, victims of the AI divide.

The Future of AI in Insurance

Artificial intelligence (AI) and machine learning have come a long way, both in terms of adoption across the broader technology landscape and in the insurance industry specifically. That said, there is still much more territory to cover, helping integral employees like claims adjusters do their jobs better, faster and easier.

Data science is currently being used to uncover insights that claims representatives wouldn’t have found otherwise, which can be extremely valuable. Data science steps in to identify patterns within massive amounts of data that are too large for humans to comprehend on their own; machines can alert users to relevant, actionable insights that improve claim outcomes and facilitate operational efficiency.

Even at this basic level, organizations have to compile clean, complete datasets, which is easier said than done. They must ask sharp questions — questions formulated by knowing what the organization truly, explicitly wants to accomplish with AI and what users of AI systems are trying to find in existing data to get value. This means organizations have to know what problems they’re solving — no vague questions allowed. Additionally, companies must take a good look at the types of data they have access to, the quality of that data and how an AI system might improve it. Expect this process to continue to be refined as companies attain a greater understanding of AI and what it can do.

AI is already being applied to help modernize and automate many claims-related tasks, which to this point have been done largely on paper or scanned PDFs. Data science will push the insurance industry toward better digitization and improved methods of collecting and maintaining data. Insurtech will continue to mature, opening up numerous possibilities on what can be done with data.

Let’s look at some of the ways AI systems will evolve to move the insurance industry forward.

Models Will Undergo Continuous Monitoring to Eliminate Data Bias

AI will continue to advance as people become more attuned to issues of bias and explainability.

Organizations need to develop the means (or hire the right third-party vendor) to conduct continuous monitoring for bias that could creep into an AI system. When data scientists train a model, it can seem like it’s all going very well, but they might not realize the model is picking up on some bad signals, which later becomes a problem. When the environment inevitably changes, that problem gets laid bare. By putting some form of continuous monitoring in place with an idea of what to expect, a system can catch potential problems before they become an issue for customers.

Right now, people are just doing basic QA, but it won’t be long before we see them harness sophisticated tools that let them do more on an end-to-end development cycle. These tools will help data scientists look for bias in models when they’re first developing them, making models more accurate and therefore more valuable over time.

Domain Expertise Will Matter Even More

In creating these monitoring systems, they can become sensitive to disproportionate results. Therefore, organizations must introduce some kind of domain knowledge of what is expected to determine if results are valid based on real experience. A machine is never going to be able to do everything on its own. Organizations will have to say, for example, “We don’t expect many claims to head to litigation based on this type of injury in a particular demographic.” Yes, AI can drill down to that level of specificity. Data scientists will have to be ready to look for cases where things start to go askew. To do that, systems — and even the best off-the-shelf toolkits — have to be adapted to a domain problem.

Data scientists are generally aware of what technology options are available to them. They may not be aware of the myriad factors that go into a claim, however. So, at most companies, the issue becomes: Can the data scientists understand whether the technologies they know and have access to are appropriate for the specific problems they’re trying to solve? Generally, the challenge that organizations face when implementing data science solutions is the difference between what the technology offers and what the organization needs to learn.

Statistical methods, on which all of this is based, have their limitations. That’s why domain knowledge must be applied. I watched a conference presentation recently that perfectly illustrated this issue. The speaker said that if you train a deep learning system on a bunch of text and then you ask it the question, “What color are sheep?” it will tell you that sheep are black. The reason is that, even though we know as humans that most sheep are white, it’s not something we talk about. It is implicit in our knowledge. So, we can’t extract that kind of implicit knowledge from text, at least not without a lot of sophistication. There’s always going to have to be a human in the loop to correct these kinds of life biases to close that gap between what you can learn from data and what we actually know about the world. This happens by inviting domain expertise into the data science creation process.

We’re getting better and better at democratizing access to AI systems, but there will always be an art to implementing them — where the data scientists have to be close to the subject matter experts to understand the underlying data issues, what the outcome is supposed to be and what the motivations are for those outcomes.

Unstructured Data Will Become More Important

There is so much data at insurance companies’ disposal, but we have only tapped into a small percentage — and we’ve yet to cultivate some of the most significant assets. The integration and analysis of unstructured data will enable this to happen as it becomes more accessible.

Case in point: Natural language processing continues to mature. This means that, instead of pulling information from structured fields, like a yes/no surgery flag that could be interpreted pretty quickly by reading claim notes, adjusters could receive a more holistic view of the claim, going beyond the structured data and finding more and more signals that would have otherwise escaped the adjuster’s attention.

Images also provide all types of exciting and insightful unstructured data. The interpretation of scanned documents is a necessary part of claims. Advanced AI systems that can handle unstructured data would be able to read them and incorporate relevant data into outputs for evaluation. Theoretically, even further in the future, adjusters could look at pictures from car accidents to ascertain the next steps and cost estimates.

See also: Despite COVID, Tech Investment Continues

Systems that can interpret unstructured data also will be able to extract information in terms of drugs, treatments and comorbidities from medical records. In claim notes, sentiment analysis will seek out patterns from across many claims to identify the ones that yield the most negative interactions with claimants so that early interventions can occur to influence claim outcomes. We are just scratching the surface on unstructured data, but it won’t be long before it makes a profound impact on insurtech.

Feedback Loops Will Improve

Ideally, good machine learning systems involve feedback loops. Human interaction with the machine should always improve the machine’s performance in some way. New situations will perpetually arise, requiring a smooth and unobtrusive way for humans to interact with machines.

For example, claims adjusters may review data outputs and determine that possibly this sentiment wasn’t actually negative, or they might learn that they missed extracting a drug. By letting the machine know what happens on the “real world” side of things, machines learn and improve — and so do claims adjusters! To reach this level and to be able to continually improve data analysis and its applications, undergoing a continuous improvement loop, is where AI will ultimately shine. It empowers adjusters with rich, accurate knowledge, and, with each interaction, the adjuster can inject a bit more “humanness” into the machine for even better results the next time.

Companies are putting systems in place to do that today, but it will still take a while to achieve results in a meaningful way. Not a lot of organizations have reached this level of improvement at scale — except for perhaps the Googles of the world — but progress in the insurance industry is being made each day. AI systems, with increasing human input, are becoming more integral all the time. Within the next five to 10 years, expect AI to transform how claims are settled. It’s a fascinating time, and I for one look forward to this data-rich future!

As first published in Data Science Central.