Tag Archives: artificial intelligence

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.

Navigating the Fourth Industrial Revolution

Embracing a growth mindset and understanding how new disruptive technologies could change our industry are among the best strategies to prepare for the opportunities and challenges of the Fourth Industrial Revolution. I highlighted some of the new disruptive technologies in Part 1 and Part 2 of this blog series.

At Gen Re, we advise clients to routinely update their companies’ boards on how artificial intelligence (AI) advancements and collaborative robots are changing their clients’ industries and whether technology is replacing or complementing workplace activities.

What are some critical actions for evaluating AI and developing technologies?

1. Separate the hype from reality. The amount of information can be overwhelming for any CEO or board, so consider getting assistance from trusted advisers in tracking developments.

2. Focus on the core practices, processes, products and people at your customer organizations. Your policyholders’ employees can help you analyze which industries in their portfolio are most vulnerable to automation within the next five years. If a critical assessment reveals that a significant part is susceptible to obsolescence, examine whether product development, market expansion or new partnerships can provide a buffer for anticipated premium or market share loss.

See also: Welcome to the Robot Revolution  

3. Don’t overlook your own underwriting and claim operations. Can you use AI to improve your own underwriting results or identify creeping catastrophic claims? Having a work culture that encourages a growth mindset and embraces new technology is essential.

4. Critically track and examine the legal and regulatory issues that can slow the adoption of AI, robotics and automation. While AI technology continues moving forward, many legal and ethical questions surrounding this technology remain unanswered. Driverless technology provides one pressing example for insurers. As Warren Buffett commented at the 2017 Berkshire Hathaway annual meeting, “If driverless cars became pervasive, it would only be because they were safer,” which would mean that “the overall economic cost of auto-related losses had gone down and that would drive down the premiums” for insurance companies. We do not know when driverless technology will be widely adopted, but we know that now is the time to prepare for its impact on auto, umbrella and workers’ comp portfolios.

5. Don’t wait. It is not too soon to start the journey toward understanding the impact and possibilities of AI, robotics and automation. Ignoring the trend can be costly regardless of what lines of insurance you write.

See also: Succeeding in the Digital Revolution  

Why AI Is Not a Threat to Human Jobs

A lot of the concern about artificial intelligence in the workplace appears to be based on what people have seen in cartoons, read in novels or watched in sci-fi movies, portraying a world overtaken by robots. Now that AI-based systems and applications are gaining ground, people are getting nervous about the role of machines. Will they take over our jobs?

While this seems like a perfectly logical question, it’s actually the wrong question. Instead, we should be asking what we want AI to accomplish. When we do this, it becomes more evident that, in reality, humans and machines will become partners rather than competitors.

AI Is Not About Replacement

There is no doubt that AI will affect jobs. The World Economic Forum projects that 75 million positions will disappear due to automation by 2022. Yet, its report says:

“As has been the case throughout economic history, such augmentation of existing jobs through technology is expected to create wholly new tasks — from app development to piloting drones to remotely monitoring patient health to certified care workers — opening up opportunities for an entirely new range of livelihoods for workers.”

The report goes on to predict automation will create 133 million jobs, or 58 million more jobs than are lost, within the same period. Gartner’s forecast, which focuses specifically on AI, also indicates that AI will be a net positive for employment. Starting in 2020, the scales will begin to tip in job creation’s favor, with 2 million new jobs opening up by 2025.

What this shows is that AI’s function in the workplace is not to swap humans for robots. In my view, it’s about removing the robot from humans.

Many of the tasks AI is charged with completing relate to rote responsibilities. Considerable human capital is wasted on activities that could be automated easily. By spending time on manual processes, people are not using their brains for higher-order skills like problem solving and decision-making.

PwC found that 70% of business executives believe that AI can enable people to focus on more meaningful work, while a Harvard Business Review survey showed that 36% of executives think one of the top benefits of AI is to free workers to be more creative, and 35% cited AI’s ability to help workers make better decisions. If AI-based solutions remove the mind-numbing functions of many jobs, if they can take away the parts of positions that are inherently robotic, it is a huge win.

See also: Untapped Potential of Artificial Intelligence  

The Intelligence Loop

But before AI can accomplish these aims, AI systems must be given a specific purpose. A company doesn’t just proclaim it has AI (hooray!) and, therefore, all its workers sit around thinking and conversing like some utopian society. No — AI systems must be directed to analyze historical data by someone who has created an algorithm to solve a defined problem. AI cannot exist without human guidance.

By the same token, humans get smarter based on the information they learn from machine analysis. People can then apply their higher-order skills to make decisions based on data coupled with their own knowledge base. AI systems subsequently interpret what humans do with the information generated; they in turn get smarter based on these interactions, and systems are refined. This process continues whenever a query is run, new data is added and action is taken.

In this sense, AI systems and the humans who leverage them become co-dependent. Work improves as machines learn more, sparking a continuous loop. This loop maximizes both artificial and human intelligences, producing what PwC dubbed the “man-machine hybrid,” which is “more powerful than either entity on its own.”

Practically Speaking

The best way to understand how this all plays out and the impact AI can have is to view it in a practical setting. AI and machine learning currently are being used in claims operations to instantly find the right providers for injured workers, formulate Medicare Set-Asides (MSAs) based on years’ worth of data in a fraction of the time, intervene in claims that could be headed to a lawyer’s office before problems escalate and much more. AI certainly furthers these tasks in their own right, but this is where we see the sum is greater than each of the parts.

As claims reps charge AI systems with looking for and synthesizing specific data points, the way claims reps and claimants interact fundamentally changes.

On one hand, claims reps are fully equipped with the right information to answer claimants’ questions and engage with them because they have access to mountains of data that an AI system can interpret. On the other hand, by not having to manually seek out all of the facets that matter in the 90-plus cases sitting on their respective desks, claims reps gain the opportunity to get a real-life picture of the people and cases to which they are assigned. They are liberated to provide care and compassion for claimants on a scale they’ve never achieved in the past. This not only alters how claims are handled but also influences the types of workers designated to handle them.

Reskill, Not Replace

Positions are evolving across nearly every industry. While most paper-pushing days are gone and replaced with electronic communication, more personal, customized processes and the customer experience will become more front and center in the new AI-driven world.

Positions will open up that embrace new skill sets. Employees who bridge the gap between domain expertise and technology will be essential, and those who can navigate between business, analytics and customer service will be in the highest demand. As workers become smarter and more dependent on machine learning, they become even more valuable to their organizations, bringing fresh, creative ideas into the workplace with unprecedented efficiency.

See also: And the Winner Is…Artificial Intelligence!  

So, machines will not take over our jobs, but they likely will remake them in wonderful and surprising ways.

As first published in Dataversity.

3 Ways AI, Telematics Revolutionize Claims

The automotive claims process has long been strenuous, time-consuming and costly both for insurers and consumers. The moment an incident occurs, a driver is placed in a world of stress. In addition to managing the emotional strain that is a car crash, the driver now has to deal with several different parties to repair the damage. Traditionally, it takes one to three days after filing a claim to initiate contact with an insurance adjuster (it takes even more time if the adjuster needs to inspect the damage).

There is suddenly an unexpected burden consuming time and money and requiring paperwork. But advancements in artificial intelligence and telematics (such as our new Claims Studio) can revolutionize the claims system by validating claims, processing them much faster and placing safety at the forefront for drivers. 

Here are three ways the insurance industry can adapt to improve the claims process: 

Validating Claims

Automotive claims have historically been a manual process, where drivers retell their side of the story following a collision. These details are then shared with insurance companies, adjusters and, at times, even courts, to resolve claims and disputes. This process leaves room for ambiguity and human error, because, as we all know, there are two sides to each story. We also have to take into consideration the shock that results from a car crash – a driver might not remember or realize immediately the need to take photos of the damages or call the insurance company to begin the claims process.

Insurers can help drivers mitigate this complicated and stressful process by implementing advanced technologies, now available, that provide accurate, unbiased crash storylines. These narratives detail key findings such as the severity of a crash, where the vehicle was hit, the driver’s speed (before, during and after a collision), the weather and more. A claims adjuster needs this information to do his or her job. When this information is incomplete or inaccurate, the process takes longer, and costs increase for the driver.

Accelerating the Claims Process

In addition to enabling insurers to settle claims more seamlessly and accurately (preventing potential fraud), these technologies aid in settling claims earlier, paving the way for better customer experiences. For example, our solution automatically populates crash insights and reporting into a web portal or directly into an insurer’s claims management system, providing insurers with many details needed to quickly process a claim. By offering claims adjusters this information within 10 minutes of an accident, insurers are empowering them to help drivers quickly resolve their issues.

Placing Safety at the Forefront

The use of artificial intelligence and telematics has brought significant benefits to insurers and consumers. Several auto insurers are already using mobile telematics to assess risk and promote safer driving behavior, but the benefits don’t start and end there. In fact, one of the most important – and life-saving – aspects of the technology is the ability to detect crashes within moments of their occurring. Technology provides real-time notifications of a vehicle crash to quickly send roadside assistance to drivers when they need it most. By providing critical details like GPS location, time and driver identification, new crash detection solutions enable insurers to save valuable time in emergency situations, offering an added level of peace of mind. 

See also: Untapped Potential of Artificial Intelligence  

In some instances, the new technologies could also save a life. One instance is Discovery Insure, a South Africa-based insurer that uses our Crash Detector to send immediate roadside assistance and paramedics to customers following collisions and life-threatening crashes. One customer, Evelyn Sadler, received immediate attention after a taxi swerved into her vehicle, causing it to go airborne. As the distracted driving epidemic increases, causing 1.25 million people to die in road crashes each year, insurers can offer drivers technologies and solutions that can keep safety at the forefront and prevent many deaths. 

The future of the automotive claims system is already here, with several insurers realizing the impact this technology has on their bottom line. I’m excited to continue to watch this space grow – and hope that additional insurance organizations will quickly follow suit.