Tag Archives: artificial intelligence

Another Reason for Insurers to Embrace AI

Did you know that artificial intelligence (AI) technology first sounded the alarm on COVID-19?

An algorithm developed by BlueDot, a Canadian AI firm, scoured news reports and airline ticketing data to detect the outbreak on Dec. 31, 2019 in China. On the same day, HealthMap, a Boston Children’s Hospital website using AI, spotted a news report of a new type of pneumonia in Wuhan, China, and alerted global health officials. HealthMap was also the first to notify Chinese health officials that COVID-19 was expanding outside of China.

Over the past decade, U.S. tech firms have made significant advancements in AI, and smart robots are making it far easier to automate tasks and functions across industries. AI’s ability to efficiently analyze large, diverse and unstructured data sets is now proving beneficial in the fight against COVID-19.

We examined the myriad ways AI can benefit P&C insurers in a three-part blog series that ran through February. Now we’re picking up where we left off, but with a focus on a timely and important application for workers’ compensation carriers and other P&C carriers. (A more comprehensive article will be published later in the summer.)

AI in the Fight Against COVID-19

AI is improving the speed and manner in which the world identifies, contains and combats infectious disease outbreaks. Its unparalleled ability to rapidly analyze massive amounts of unstructured data has already proven to be an early detection and warning tool for seasonal influenza.

The CDC, recognizing the potential value of AI, holds an annual competition for AI firms and academic institutions. The participants develop AI algorithms to help identify and predict the severity of future influenza outbreaks. Many of these participants are now leveraging their technology and data sets to fight COVID-19.

See also: And the Winner Is…Artificial Intelligence!

AI alerts have played and continue to play a critical role in detecting and controlling future outbreaks.

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In the wake of the global pandemic, AI technologies are offering hope and promise in the fight against COVID-19. MIT’s Watson AI Lab is funding a research project for early detection of sepsis, a deadly complication of COVID-19 affecting at least 10% of COVID-19 patients. The project aims to develop a machine learning system to analyze white blood cells for signs of an activated immune response against sepsis. MIT is also developing an AI tool to help doctors find optimum ventilator settings for COVID-19 patients. Shorter ventilator treatments will limit lung damage and free ventilators for other patients.

U.S. research hospitals are developing AI solutions to improve the speed and accuracy of their COVID-19 diagnoses. Mount Sinai, a leading New York research hospital, was the first in the U.S. to develop an AI solution that could quickly and accurately analyze chest scans of patients and detect early signs of COVID-19 on par with highly trained and experienced radiologists.

The world’s leading tech firms and academic institutions are partnering with governments and hospitals to limit the spread of COVID-19 and to protect healthcare workers. Boston Dynamics and MIT developed Spot, a smart robot, to deliver medicine and monitor vital signals of COVID-19 patients. With the help of its leading tech firms, China created a smart field hospital in Wuhan to relieve and protect overtaxed medical professionals.

AI technology is also accelerating vaccine development in such efforts as the collaborative work between Harvard and the Human Vaccines Project. Given the lengthy time to create, test and approve a COVID-19 vaccine, academic institutions and AI firms are working with scientists to identify FDA-approved drugs for repositioning to treat or contain COVID-19. BeneloventAI, a U.K. tech startup, has already applied its drug discovery platform for this purpose and identified a drug for a COVID-19 clinical trial.

Why It Matters to P/C Insurers

Many AI advances are aimed at protecting the health and safety of medical professionals – doctors, nurses, EMTs and all those employed in hospitals. That protection extends to patients and visitors who do not have COVID-19. As a result, hospitals and healthcare facilities that quickly embrace and implement these new AI technologies should prove to be more attractive risks for workers’ compensation and professional lines specialty carriers.

The adoption of AI and smart robots in healthcare is especially critical given the advent of workers’ compensation COVID-19 presumption statutes and executive orders designed to protect healthcare workers and others on the front lines of the COVID-19 pandemic. Specifically, those legal efforts shift the burden of proof from the employee to the employer and reduce or eliminate the evidentiary requirements to establish a claim. While these developments are well-intended, many workers’ compensation carriers expect to see a rise in claims in states taking this action. If AI can significantly improve the safety of medical professionals, we hope it can offset the rise in claims from the new COVID-19 presumption laws.

See also: 3 Steps to Demystify Artificial Intelligence

To the extent that AI can help reduce illness or its spread, the need for extensive quarantine measures will be reduced, and all sectors of the economy will benefit. Main Street businesses and manufacturing facilities will be able to operate more safely, and that can mean fewer business interruption and premises liability claims during future infectious disease outbreaks.

Insurers do not need a global pandemic to appreciate the economic and health value of AI. Smart robots, AI and automation will continue to significantly improve workplace safety and employee health for all types of businesses even after we have tackled COVID-19. Gen Re continues to monitor these trends and looks forward to helping you understand and navigate the AI landscape.

You can find this article originally published here.

Stop Being Scared of Artificial Intelligence

In a world where messaging tends to overcomplicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value. I’ve found this to be true when it comes to artificial intelligence or, AI.

Generally speaking, the word “artificial” doesn’t call to mind a positive image, does it? Listed meanings include “insincere or affected” and “made by humans as opposed to happening naturally.” 

Artificial intelligence is, in fact, created by humans. The term was coined by John McCarthy, Stanford computer and cognitive scientist, way back in 1955.

AI is not intended to simply be a digital worker, certainly not within financial services and fighting financial crime. Yes, AI can automate various functions. We’re all familiar with the concept of “bots” and virtual assistants. However, those are rudimentary examples of robotic process automation. True AI is human-led and a continuous, instantaneous learning process that drives tangible value. AI is not merely a play to cut costs or replace human capital. Rather, AI enhances the bottom line by keeping compliance staff costs flat in the immediate term and enables our human experts to more appropriately manage their time, by focusing talent on investigations that matter the most.

One of the most valuable aspects of AI, in the context of anti-money laundering and compliance, is the speed by which it can be deployed. We’re talking about time to market and time to value in a matter of weeks. Not months, not multiple quarters — simply weeks. But I don’t mean a generic, black box concept. I’m referring to a highly precise, tailored AI solution that has extensive proof points and, more importantly, far-reaching global regulatory approval.

AI shouldn’t simply be an extension of legacy rules-based routines, nor a way to further automate the process of scoring or risk-weighted alert suppression. That simply dilutes the true value of AI and does not maximize the cost and efficiency benefits.

See also: 3 Steps to Demystify Artificial Intelligence  

The cost of compliance continues to grow at a staggering pace, particularly for financial institutions and insurance companies. Equally of concern, the impact of fines for non-compliance has also skyrocketed in the last decade, to the tune of $8.4 billion last year across North America alone.

What if you could literally solve every single name screen, sanction and transaction alert? What if you could achieve this without sacrificing any aspect of control and security? What if you could increase the throughput, efficiency and accuracy of your compliance operations without adding a single dollar of staff expense to your budget?

Artificial intelligence isn’t scary. It isn’t a black box. And it isn’t the futuristic world of tomorrow. It is the here and now, and it’s battle-tested.

5 Ways AI Helps on Client Service

Artificial intelligence has become a hot topic in the insurance industry as the push to modernize the agency with digital solutions reaches a fevered pitch. Especially now, as our society and business operations adapt to a global pandemic, agencies are scrambling to leverage technology and analytics to make smarter decisions for their clients and their own business.

For many independent agencies, however, AI still feels like a theoretical concept — a capability reserved for and only accessible to big companies with deep pockets. But the reality is, AI has tangible benefits for even the smallest independent agencies when it comes to improving client services and strategic business growth. And, it’s more accessible than many might think. 

Leveraging AI can enable better business strategy for agencies of all sizes, today and in a post-pandemic environment. With AI, agencies can:

  1. Better advise clients. Now more than ever, insureds are looking to their insurance agents for risk management, stability and reassurance. With AI, agents can draw on industry insights to better understand the risks their clients face, provide more relevant, data-driven advice and do so with confidence. With the right tools, agents can look at data about similar individuals, businesses or industries and spot trends early to offer coverage suggestions. For example, the demand for business interruption insurance has risen sharply since the pandemic began. By leveraging AI, agents can see these potential risks coming down the pike and can make sure their clients are protected.
  2. Accurately predict risk. For years, actuarial services have attempted to quantify the economic value of risk to help carriers and agencies arrive at appropriate levels of coverage and premium costs. But today, AI provides a much more insightful and accurate risk assessment. By delving into industry-wide historical data, AI tools can arrive at a more accurate risk value based on real, documented data rather than conjecture. This allows the industry to set premium rates accordingly so that insureds get the coverage they need at a price that’s competitive and makes sense. 
  3. Find business opportunities. Without AI, agencies must rely on hunch, experience and clients to find and address new opportunities. AI technologies let agencies quantitatively analyze client needs, market dynamics and carrier appetite. Based on this insight, agencies can make smarter, faster and more confident business decisions to spur growth. For example, with industry intelligence, agencies can identify valuable opportunities to upsell coverage, identify new clients and expand into new markets based on carrier appetite for certain types of policies in specific geographies. 
  4. Improve agency efficiency. Digitizing processes to eliminate rote, manual tasks not only improves agency productivity and performance, but also client relations. When agents can spend less time pushing paper and more time talking with clients to better understand their needs and provide expert advice, everyone wins. AI can help drive this efficiency with predictive and automated workflows that can make many common insurance processes move faster. 
  5. Enhance client relations. While many agencies fear that AI and other technologies might take away from the personal relationships they’ve built with clients, AI can actually do the opposite. By automating processes and surfacing data-driven insights, AI can give agents more time to spend in meaningful conversations with their clients, providing informed counsel on how best to protect their assets. AI can also improve one of the most frustrating processes for clients — claims processing — to deliver a better experience. For example, we can now automate the submission process by using AI to analyze damage photos and natural language processing of the description of the claim submitted to rapidly assess the probability of fraud. Below a certain threshold, the claim may be automatically and instantaneously paid. This accelerates the process, delivering a more positive experience for the individual or business submitting the claim.

See also: How AI Can Stop Workers’ Comp Fraud  

As digital modernization becomes imperative for agencies, AI is proving to be a crucial ingredient for delivering the level of service that clients expect and for driving agency growth. By scaling AI implementation, agencies can not only keep pace with their peers but also offer innovative solutions that give them a competitive advantage, positioning agents as confident and dependable risk advisers in an increasingly uncertain environment.

How AI Can Stop Workers’ Comp Fraud

Wondering how AI can help detect medical provider scams? Wonder no more.

Artificial intelligence (AI) is redefining work in nearly every industry thanks to the increase in accuracy, efficiency and cost-effectiveness that AI-based applications offer. One of the latest industries to benefit is insurance, where applications are now being deployed to help detect and reduce provider fraud through advanced predictive tools. Claims payers identify fraudulent providers early in the life of a claim and root out bad actors while saving organizations millions of dollars.

The Fraud Problem

Fraud involves deliberately presenting false information to extract a benefit. The most common examples of provider fraud include “phantom billing” (billing for services not rendered), submitting bills for more services than are possible in a provider’s day, providing services unrelated to the injury, using unlicensed or non-credentialed individuals to provide medical services, getting paid kickbacks in exchange for sending patients to third parties and referring patients to entities (such as laboratories or testing facilities) in which the provider has an ownership interest.

While most providers do not engage in fraud, those that do are extremely costly. According to the National Insurance Crime Bureau (NICB), workers’ compensation medical fraud costs approximately $30 billion per year in the U.S. alone.

Fraudulent provider behavior is hard to detect and prove, particularly in workers’ compensation data systems. Advanced data analytics based on AI, however, offers opportunities to overcome the inherent weaknesses in these systems while developing methods to identify and curb provider fraud. Let’s take a look.

Fragmentation of Payers

One of the biggest issues in provider fraud is that no one organization has more than 5% of workers’ compensation market share, so none can see the entire picture of a provider’s claims. This can cause a whole host of issues. For example, if one company has identified a fraudulent provider, other companies may not have this information and continue payments. In states where fraud information is publicly available, providers simply begin practicing in other states, avoiding the state that sanctioned them.

Using AI tools, however, organizations can tap into multipayer pools of aggregate information to spot fraudulent patterns quickly and reliably without compromising payer, employer and employee information. It also makes it easier to flag and curb behavior across a multipayer database.

See also: Untapped Potential of Artificial Intelligence

Inaccurate Provider Identification

The constantly changing complexity of provider identification is another major challenge. Data is often tied to names. Fraudulent providers know this system weakness and frequently change their organization names and addresses as well as other identifiers.

Using AI, data scientists can now reliably link multiple bills from the same provider using a National Provider Identifier (NPI) developed by the Centers for Medicare and Medicaid Services (CMS). Almost all providers have an NPI, and some have more than one. When supplemented with taxpayer identification (FEIN) numbers and license numbers, NPIs can reliably identify 95% of medical providers. As a result, machines can overcome the name game, detecting the long-term, multiyear activities of almost all providers and provider organizations.

Long Lag Times

The interval between when an instance of fraud occurs and when it is detected is often several years. For example, a provider may submit a bill on day one for services unrelated to the injury; the bill will be submitted for review 30 days later and will likely be paid in another 30 days. This practice will be repeated dozens of times by the same provider on the same patient over the course of months. If fraud is detected, the provider will have already been paid, and financial recovery is difficult.

To combat this problem, AI can detect the entire course of treatment on the same claim from the first through subsequent billings over multiple years. Software tracks the diagnoses and the number of procedure codes billed by the same provider on the same claim — per day, per month and per year. As a result, claims staff receive real- time alerts and can intervene when a fraudulent provider initiates treatment on a claim.

Complex Provider Supply Chains

The entire fraud supply chain often includes attorneys, medical providers, outpatient and inpatient facilities, interpreters, testing facilities, medical device suppliers, pharmacies, copy services and transportation services. Unless data sets capture all or most of these moving parts, the chance of detecting fraudulent patterns is very difficult.

With AI, it’s getting a lot easier. Data scientists can use aggregated data to track sequences of out-referral and in-referral, exposing links between fraudulent individuals and entities. Sophisticated techniques isolate consistent and repeatable patterns of relationships between multiple providers and third parties. Data scientists then can graphically display suspicious network clustering patterns inherent in fraud networks.

And these are just a few examples of how AI tools can greatly increase the detection of fraud.

See also: Impact of COVID-19 on Workers’ Comp

Defining the Future of Claims

AI differs from more traditional research approaches because it can generate its own rules to detect fraud and look across large data sets nearly instantly. Via machine learning, databases are continually refreshed, becoming smarter and more effective all the time. By incorporating AI-based solutions, insurance payers can defeat fraud at a systemic level and realize significant financial benefits in return.

As first published in The CLM.

The Emergence of AI-as-a-Service

Software-as-a-service (SaaS) has become part of the tech lexicon since emerging as a delivery model, shifting how enterprises purchase and implement technology. A new “_” as a service model is aspiring to become just as widely adopted based on its potential to drive business outcomes with unmatched efficiency: artificial intelligence as a service (AIaaS).

The AIaaS Opportunity

According to recent research, AI-based software revenue is expected to climb from $9.5 billion in 2018 to $118.6 billion in 2025 as companies seek insights into their respective businesses that can give them a competitive edge. Organizations recognize that their systems hold virtual treasure troves of data but don’t know what to do with it or how to harness it. They do understand, however, that machines can complete a level of analysis in seconds that teams of dedicated researchers couldn’t attain even over weeks.

But there is tremendous complexity involved in developing AI and machine learning solutions that meet a business’ actual needs. Developing the right algorithms requires data scientists who know what they are looking for and why, to cull useful information and predictions that deliver on the promise of AI. However, it is not feasible or cost-effective for every organization to arm itself with enough domain knowledge and data scientists to build solutions in-house. 

AIaaS is gaining momentum precisely because AI-based solutions can be economically used as a service by many companies for many purposes. Those companies that deliver AI-based solutions targeting specific needs understand vertical industries and build sophisticated models to find actionable information with remarkable efficiency. Thanks to the cloud, providers can deliver AI solutions as a service that can be accessed, refined and expanded in ways that were unfathomable in the past.

One of the biggest signals of the AIaaS trend is the recent spike in funding for AI startups. Q2 fundraising numbers show that AI startups collected $7.4 billion — the single highest funding total ever seen in a quarter. The number of deals also grew to the second-highest quarter on record. Perhaps what is most impressive, however, is the percentage increase in funding for AI technologies — 592% growth in only four years. As these companies continue to grow and mature, expect to see AIaaS surge, particularly as vertical markets become more comfortable with the AI value proposition.

See also: Predictions for AI Adoption in 2020  

Vertical Adoption

Organizations that operate within vertical markets are often the last to adopt new technologies. AI, in particular, fosters a heightened degree of apprehension. Fears of machines overtaking workers’ jobs, a loss of control (i.e., how do we know if the findings are “right”?) and concerns over compliance with industry regulations can slow adoption. Another key factor is where organizations are in their digitization journey. For example, McKinsey found that 67% of the most digitized companies have embedded AI into standard business processes, compared with 43% at all other companies. These digitized companies are also the most likely to integrate machine learning, with 39% indicating it is embedded in their processes. Machine learning adoption is only at 16% elsewhere.

These numbers will likely balance out once verticals realize the areas in which AI and machine learning technologies can practically influence their business and day-to-day operations. Three key ways are:

Empowering Data

Data that can be most useful within organizations is often difficult to spot. There is simply too much for humans to handle. The data becomes overwhelming and thus incapacitating, leaving powerful insights lurking in plain sight. Most companies don’t have the tools in their arsenal to leverage data effectively, which is where AIaaS comes into play.

An AIaaS provider with knowledge of a specific vertical understands how to leverage the data to get to those meaningful insights, making data far more manageable for people like claims adjusters, case managers or financial advisers. A claims adjuster, for example, could use an AI-based solution to run a query to predict claim costs or perform text mining on the vast amount of claim notes.

Layering Insights for Better Outcomes

Machine learning technologies, when integrated into systems in ways that match an organization’s needs, can reveal progressively insightful information. A claims adjuster, for example, could use AIaaS for much more than predictive analysis. The adjuster might need to determine the right provider to send a claimant to based not only on traditional provider scores but also on categories that assess for things like fraudulent claims or network optimization that can affect the cost and duration of a claim. With AIaaS, that information is at the adjuster’s fingertips in seconds. 

In the case of text mining, an adjuster could leverage machine learning to constantly monitor unstructured data, using natural language processing to, for example, conduct sentiment analysis. Machine learning models would look for signals of a claimant’s dissatisfaction — an early indicator of potential attorney involvement. Once a claim is flagged, the adjuster could take immediate action, as guided by an AI system, to intervene and prevent the claim from heading off the rails. While these examples are specific to insurance claims, it’s not hard to see how AIaaS could be tailored to meet other verticals’ needs by applying specific information to solve for a defined need.

Assisting Humans at a Moment’s Notice

Data is power, but it takes a human a tremendous amount of manual processing to effectively use it. By efficiently delivering multilayer insights, AIaaS provides people the capability to obtain panoramic views in an instant. Particularly in insurance, adjusters, managers and executives get access to a panoramic view of one or more claims, the whole claim life cycle, the trend, etc. derived from many data resources, essentially by a click of a button.

See also: How to Use AI in Customer Service  

The Place for AIaaS

AIaaS models will be essential for AI adoption. By delivering analytical behavior persistently learned and refined by a machine, AIaaS significantly improves business processes. Knowledge gleaned from specifically designed algorithms helps companies operate in increasingly efficient ways based on deeply granular insights produced in real time. Thanks to the cloud, these insights are delivered, updated and expanded upon without resource drain.

AIaaS is how AI’s potential will be fulfilled and how industries transform for the better. What was once a pipe dream has arrived. It is time to embrace it.

As first published in The Next Web.