Tag Archives: predictive analytics

5 Key Effects From AI and Data Science

In the digital era of innovative products and services, insurtech technologies are bringing great opportunities to the insurance sector and accelerating the industry’s transformation. Advances in AI and data science are leading insurers toward the effective use of machine learning, data modeling and predictive analytics to improve back-end processes and streamlining and automation of the front-end experience for both consumers and insurance companies.

Here are five ways that insurance companies are applying AI and data to the industry:

1. Front-end sales, underwriting and policy service

Customers are acquiring insurance policies much faster and easier with the help of automated processes. These technologies differ depending on the systems that employ them and the people they serve. Integration gateways relying on data and AI are creating new customer experiences.

See also: Seriously? Artificial Intelligence?  

2. Back-end claim services

AI, IoT, predictive analytics and data modeling let insurers refocus claims so that it is easier to file, submit, adjust and reimburse claims. This means customers have their claims settled in an expedited manner. Patterns of fraud are detected, learned from and shared via modeling and the AI that combs them for key information.

3. Business intelligence and big data

Smartphones, telematics and sensors from wearables and connected homes provide a wealth of new data. In a connected world, insurers can generate insights from both external and sensor-based data sources. How this data is collected, stored and used will determine whether insurers will build or lose trust with customers. Take necessary measures to harden networks so that the threat of cybercrime is reduced.

4. Customer experience

Insurance companies need to offer their services in a way that encourages loyalty, customer retention and loss mitigation. This can be made possible by making policy acquisition easier and keeping policyholders engaged. It’s now common for insurers to monitor driving, health and home behavior through mobile apps and wearables. In exchange for the data, carriers offer lower or customized premiums to customers whose score reflects reduced risk.

5. Customized insurance

Carriers offer insurance packages and plans based on a matrix of factors. This requires their agents to possess extensive knowledge about products as well as their new and prospective clients. Through machine learning, millions of data patterns can be analyzed to identify the most appropriate customized plan or product for a particular customer. It can even be offered to them via AI.
Data modeling and artificial intelligence are advancing rapidly. They are laying the foundation of an industry equipped to quickly take clients from prospect to policyholder with minimal touch points and reduced risk.

See also: Motto for Success: ‘Me, Free, Easy’  

Where exactly these technologies will lead us next is anyone’s guess, but carriers have begun to realize the benefits. A historically slow-to-move, conservative industry is now more nimble, innovative and tech-savvy than ever before. Transformation is here!

Predictive Analytics: Now You See It….

I’ve always enjoyed a good magic trick. It gives you a momentary suspension of reality to believe that there is something more. There is also the desire to figure out how they did it, how did they know that your card was the 10 of clubs, how did they walk through a solid wall, how did they violate the laws of physics? But, while the trick is entertaining, you always know that they did not actually saw someone in half and then reverse the process in less than a minute. You know that the rabbit did not dematerialize. You know that the person is not really floating in midair, levitating without strings. You lose yourself in the wonder of it all, but realize that it was just an illusion.

There is a somewhat painful parallel with predictive analytics and insurance. On the one hand, there is great promise to change the entire industry, suspending the well-worn rules of insurance and the doctrines taught since the beginning. We look with wonder at the almost messiah-like prophecies of insurtech and how it will kill multibillion-dollar insurance organizations and entire distribution ecosystems at the touch of an icon.

But then we start to wonder, how will it actually work? How will companies choose which risks are acceptable and at what price? How will they go from losing money on every policy into a sustainable business model that not only generates revenue for today but adequate reserves and reinsurance for tomorrow? How will the buying public intelligently evaluate different policies and coverages when they do not speak insurance-ese?

Just because data may be available, it does not automatically follow that this information is useful, credible and legitimate or can be legally employed. This is the now you see it, now you don’t part of insurance and predictive analytics. But before we delve into it, let’s take a moment to review some fundamentals.

First — insurance is the largest single industry on the planet, accounting for 7% of the global economy. It has been called the grease that lubricates the global economy. It is virtually impossible to think about anything where insurance is not at the heart. Nothing gets built, no commerce moves, no innovation or transaction takes place without insurance playing a major role.

Second — insurance is the original data-driven industry. Insurance has no converting of raw physical materials into a finished product. At its core, insurance is words, a promise, a contract that lays out rules and responsibilities of the parties. And this is an important lead up to the next fundamental.

Third — insurance is NOT a commodity. Of all the things written and bandied about regarding insurance, this is the single most important misconception being spewed by people who have their insurance feet firmly planted in the air, using both social and traditional media for the uncontrolled spread of this false insurance news. You can take out your ruler to measure if the two pieces of lumber are the same height, width and length. Or if the jar of salsa contains 16 ounces or not. Because insurance is the original data-driven industry, there are no physical dimensions to measure or evaluate. Comparing plan premiums and deductibles only scratches the surface.

Let me paint a word picture that I hope will make this clear. Imagine that I have five plastic tubes in my hand. To the casual observer, they look similar in size, color and caps. I submit that you would not mindlessly grab a tube and put its contents into your mouth without carefully reading the label and ingredients. You would do this because the tubes, while similar in appearance, could actually contain:

  • Toothpaste
  • Anti-bacterial soap
  • Acrylic paint
  • Brass cleaner
  • Hemorrhoid cream

For an actual insurance example, an insurer’s “HO3” provides Coverage A – dwelling coverage. Some Coverage A descriptions say, “This coverage does not apply to any dwelling used in whole or in part for ‘business.” Do you ever conduct business from home? Recent studies suggest that half of Americans work from home at least occasionally. If your policy reads like this, then you probably do not have dwelling coverage. Another example comes from Coverage C – personal property. Some policies say it covers personal property “while it is IN” the “residence premises.” This eliminates any personal property that was not directly in the residence.

See also: 3 Key Steps for Predictive Analytics  

And the worst possible time to discover that your property will not be replaced is when you’ve had a loss.

Something less than the holy trinity

Fourth — sometimes referred to as the something less than holy trinity, Standards, compliance and regulation play important roles within the insurance industry.

  • Standards — with very rare exceptions, there basically are no meaningful standards within the insurance industry when it comes to policy language, pricing or data. Yes, there are forms and rates published by organizations, but carriers are very creative in creating unique coverages, wording, pricing and underwriting selection criteria. We know this to be true for at least three reasons. When an insurance agent moves a policy from carrier A to carrier B, the agent is strongly encouraged to review differences with clients or face a potentially painful and expensive E&O claim for policy differences that were not explained and signed off. Another reason that we know this to be true is that you cannot take data from a policy download from carrier A and upload it to carrier B. And thirdly, if there were meaningful standards, then there would be no need for comparative rating software.
  • Compliance — across the country, there is an unbelievably complex web of compliance requirements. Not only are there state insurance commissioners, but some states split out workers’ compensation from other forms of insurance. Insurance products must comply with individual jurisdictional requirements, or substantial fines can be levied. As an example, check out the press releases on the fines charged to Zenefits for its part in selling insurance without the use of authorized, licensed insurance agents.
  • Regulation — In 12 states, insurance commissioners are elected, providing additional political pressure to lower rates while raising coverage, especially in an election year. On top of the state requirements, some cities have their own insurance requirements which are layered on top of, and sometimes conflict with, the state requirements when it comes to required coverage, price, covered claims and taxes.

Data sources

Because insurance is the original data-driven industry, insurance organizations are a treasure trove of information that they have been collecting since their inception. Perhaps this is why both artificial intelligence and predictive analytics zealots salivate at the prospects of getting access to this information.

There are three potential data sources available to insurance organizations that could feed predictive analytics; traditional, obvious and hidden.

Traditional — these are data sources employed today within the insurance industry. It all starts with information collected on an application. From there it spreads out to include vehicle/property data, employment and payroll data, replacement cost estimators, credit scores, actuarial tables, claims history, location/ZIP codes and beyond.

But even with all this information, there is no uniform application of it throughout the industry, among companies or even within a state. For example, there is ample statistical evidence proving that insurance credit scores are solid predictors of claims severity. Some carriers use insurance credit scores extensively while others ignore it altogether, even in the same competing markets. In personal auto and homeowners, premiums can more than double based solely on the insurance credit score of two identical insureds in the same state. In California and Massachusetts, you are not permitted to use insurance credit scores in either personal auto or homeowners, while in Maryland you can use it in personal auto but not in homeowners.

This is a classic example of the “now you see it, now you don’t” aspect of insurance data and predictive analytics. Data is available, but not everyone elects to use it. Also, there are regulatory restrictions on when and where the data can be employed.

It’s important to understand that, unlike traditional credit scores, which are used by lenders such as credit card issuers, consumers don’t have access to their credit-based insurance reports. This helps fuel the fires of mistrust that the American people feel toward insurance. One study concluded that, in most states, “auto insurance premiums are driven in large measure by economic factors that are unrelated to driving safety” — namely, education level, occupation, homeownership status, prior purchase of insurance and marital status. It also found that a substantial majority of Americans believe it is unfair for insurance companies to use economic characteristics – specifically, education level, occupation, not having insurance because of not having a car, homeownership status, marital status, and credit score – in setting auto insurance premiums. For example, good drivers pay 59% more, or $681 annually, on average for auto insurance due to personal characteristics associated with lower economic status.

Obvious — there are a number of new and exciting data sources that have clear and observable usage for insurance and predictive analytics. They may be obvious to the casual observer, but privacy and political concerns may move them into the questionable category.

While telematics has been used by some insurance companies, I don’t think of it as a traditional data source because of the emergence of your vehicle not only as a means of transportation, but also as a data hub. In addition to vehicle and driver performance, data from vehicle hands-free cell phone usage, entertainment and vehicle hotspot connections are available. This allows non-driving data and patterns to be compared and scored, potentially altering premiums and whether you are a desirable insurance customer.

Social media is another obvious data source, but how should it be employed within the insurance marketplace? For example, should your social media posts be used to select whether an insurance company is willing to insure you, and at what price? Also, should social media information be criteria to deny a claim? Recently, a couple applied for personal umbrella liability insurance to be added to their homeowners policy. As part of the application, they had to list the number of dogs they owned and their breeds, which they truthfully answered. They were shocked to find that they were not only rejected for the extra insurance but that their homeowners policy was being canceled as well because the company claimed they had a Rottweiler mix, a dog breed the company considers dangerous. The most intriguing part of the story was that the insurance carrier used pictures from Facebook as proof that the coupled had “lied” on the application. The couple followed up with the insurance company to let it know it was wrong about the dog’s breed. Instead of standing down, the insurance company said it would need a written letter from a veterinarian. This was no problem, because the wife was a veterinarian. The insurance company eventually offered to reinstate the policy, but the couple took their business elsewhere and lamented, “Be careful about what you post on Facebook. It’s sad that you can’t post pictures of your beloved pet on your own Facebook page.”

There are mountains of data available on individuals and corporations that are in the public domain. The federal government collects and publishes reams of data, and there is precious little that you can do about it. Some examples include census data, name and addresses of all licensed pilots, whether your address is on a flood plain. The surveillance, epidemiology and end results (SEER) program of the National Cancer Institute Program provides information on cancer statistics in an effort to reduce the cancer burden among the U.S. population. By keeping this data, SEER details when/where cancers are breaking out. Local governmental data sources include information on physicians and attorneys, and your water bill is also in the public domain. One of the more interesting non-governmental sources is information for all loans issued through the Lending Club, including the current loan status (current, late, fully paid, etc.) and latest payment information. And we won’t even go into amount of free data that is available via Facebook.

The Internet of Things, IoT, is providing unparalleled additional information into the details of our lives. Just about any type of device for your home, office or health is collecting data to learn more about out preferences and habits, which is potentially available to affect your insurance acceptability and pricing. Even iRobot’s Roomba is collecting data about dimensions of a room as well as distances between sofas, tables, lamps and other home furnishings.

Phone companies can track which cell tower your call is pinging. Credit/debit card companies are tracking payment details. Some of this may/may not be available today, but the shifting sands of privacy certainly will alter the status quo.

Hidden — there are a growing number of non-obvious data sources that contain potentially valuable or questionable information about our lives that may be applied to insurance. We will discuss the sources and then whether they should be used within insurance.

One insurance company uses the customer’s email domain name in pricing personal auto policies: “Certain domain names are associated with more accidents than others. We use a variety of pieces of information to accurately produce a competitive price for our customers.” This hidden piece of information leads to different pricing for Hotmail email account users instead of a Gmail one. It was also found that this insurance company charged significantly more for insureds with foreign-sounding names.

Intelligent personal assistants, IPAs, are becoming more and more popular each and every day. Organizations from every industry, including insurance companies, are tapping into this easy-to-use technology that is only a spoken word away. Amazon and Google are leading the pack, with Microsoft and Apple in hot pursuit. The good news is that all you have to do is talk, and the IPA is there to grant your every wish. The bad news is that for IPAs to answer your request, they have to be listening. And not just listening but listening all the time. It is unclear how much of the conversation is actually saved and how it is being used. But as we know all too well, once something is saved, it is almost never fully erased; there are digital cookie crumbs to be followed. There was a case where police subpoenaed the recordings made by an IPA in a house where a murder was committed. Should this information be made available to the police? Should it be made available to insurance companies?

You can be certain that everybody, and I mean everybody, including smart phone apps, fitness monitors, retail and grocery stores, rental car companies, cable companies, etc. are collecting lots of data about you, your life choices, lifestyles and buying habits. For many of the obvious and hidden data sources, the public is giving away their data and rights to restrict how it might be used. Why do you think everyone offers free memberships, convenient apps and customer loyalty rewards? It’s not that they enjoy giving things away. While you cannot be certain about a great many things in this life, one thing that you can go to the bank about is that they think it’s worth the time, energy and money to collect this data to monetize it. I know it’s even less exciting than watching paint dry, but stop and try to read through the entire user agreement before mindlessly clicking on the “I Agree” EULA (End-User License Agreement) button. If you read these agreements in detail, you will find that you are providing the right to collect just about everything you can imagine and then some, and then letting the company do with it whatever it thinks best. One car rental user agreement I reviewed said the company had the right not only to the vehicle telematics data, but also to your Facebook information. Now I really cannot connect the dots between a car rental and my Facebook data, but the company apparently think it’s worth it.

My favorite example of hidden information is mapping data. I love how I can enter a destination and see the best route, with/without tolls, and even see changes in the route in case of changing traffic patterns caused by roadwork or accidents, and at no cost. This is great technology that I am thankful for each and every day. However, in case you were not aware, smart phones with mapping tools are also recording your movement as long as the phone is on, even when you are not using the map app. They have my detailed movements on file since December 2012. I can look up, by day, where I went, including the places of business I visited. Do you think an insurance company might be interested if you:

  • Changed your normal travel patterns?
  • Visited a local watering hole after work?
  • Started going to a cancer treatment center?
  • Went to an AIDS clinic?
  • Stopped in a medical or recreational marijuana store?
  • Drove to a psychiatrist three times a week?

As I said, this is great technology, and it doesn’t cost me a thing. Or does it?

A personal example

If you were to look at my available data during the early summer of 2016, you would see a change in my travel patterns, mapping, spending and social media information. You could easily determine that something had changed, triggering a more detailed look in my traditional, obvious and hidden data. You would have found that I started visiting a dermatologist quite frequently, and stayed for long periods. You would have seen pictures and posts on social media about multiple visits and procedures to identify, remove and then remove again more tissue associated with melanoma. While I am thankful that the doctors were able to remove it after multiple surgeries, would an insurance company be interested in this kind of data?

Or perhaps the more daunting question is whether an insurance company should be interested in this kind of data? There is no question that the data is there (now you see it). But the more difficult question is whether or not the insurer should be allowed to employ it (now you don’t).

See also: What Comes After Predictive Analytics  

Bill Hartnett, sometimes called the “godfather” of Microsoft financial services, puts it succinctly; “Predictive analytics has a dark side. Price and claim optimization should have no place in insurance. Determining that you are willing to pay a price higher than your underwriting risk indicates based on past buying behavior, or accept a claim settlement less than the actual cost based on your financial situation is not insurance.”

Bill Wilson, CPCU, ARM, AIM, AAM, founder and CEO of InsuranceCommentary.com, puts it more bluntly, “All insurance regulators who have considered the issue of price optimization have concluded that it has no place in underwriting and pricing insurance. Most state insurance laws expressly require that rates and premiums be risk-based and not unfairly discriminatory. The insurance industry is one founded, like few others, on the overriding principle of utmost good faith. Price optimization is about as far as you can get from good faith.”


These are some formidable questions, requiring both discussion and research. Intel, IBM, Workday and the Washington, D.C.-based Information Technology Industry Council—whose members include Facebook, Apple and Google—all issued principles on the ethical use of artificial intelligence. Microsoft put out an entire book on “Artificial Intelligence and its Role in Society.” Some of the biggest tech companies founded an ethics-setting organization called the Partnership on Artificial Intelligence to Benefit People and Society, based in San Francisco. There are many others including Open AI, the AI Now Institute, doteveryone and the Center for Democracy and Technology.

It would seem reasonable that because insurance is the single largest industry on the planet, and consumes nothing but data, representatives from the insurance industry participate in these cross-industry forums.

So, where do we go from here? I’d like to suggest two ways to move forward, but their source and inspiration are miles apart from insurance.

First — predictive analytics and insurance is a journey, not a destination. The inspiration for this is Heraclitus of Ephesus (535 BC – 475 BC.) Now I admit that insurance, technology and ancient Greek philosophers don’t have a whole lot in common, at least on the surface. Heraclitus wrote, “Everything changes, and nothing remains still. You cannot step twice into the same stream.” He was saying that if you step into a stream, step out and then right back in, you are stepping into a different stream. The water around your feet during the first step will be different than the second. The air around you is different. The birds in the air are no longer in the same place. And so it is with predictive analytics and insurance. What was commonplace and known last year is way out of date. The assumptions and tools are better and different now than just months ago. This requires continuous investment, evaluation and adjustment of your predictive analytics plans and application. There needs to be a continuing collection and evaluation of data sources, quality and efficacy.

Second — predictive analytics and insurance requires that we treat people well. The works of Robert Neelly Bellah (Feb. 23, 1927 – July 30, 2013) are the inspiration for this application. He was an American sociologist, and the Elliott Professor of Sociology at the University of California, Berkeley. Robert wrote about something he called “expressive individualism,” where each person has a unique core of feeling and intuition that should unfold or be expressed if individuality is to be realized. Each person has the need to express needs, wants and desires. Throughout the predictive analytics implementation process within insurance, we need to treat people individually and always lead with respect. Today’s insurance marketplace needs to move way beyond the current mania of mail-merge marketing memos. Another thing to avoid is acting creepy, using so much information that you make the customer feel uncomfortable.

Winners within the insurance marketplace will be those organizations that aggressively and systemically leverage data and predictive analytics technology while providing personalized products and services. The road to successful employment of predictive analytics within the insurance marketplace will, by necessity, require many starts and adjustments along the way. Members of the “something less than the holy trinity” will create unique challenges felt in no other industry. But great potential awaits those who start and continually move forward.

Note — this article is based on a presentation for the Global Predictive Analytics Conference, April 2-4, 2018, Santa Clara, CA.

Why Don’t Most ERM Systems Work?

So why don’t most Enterprise Risk Management system work?  Simply, they don’t “manage” risk, they just record it.  Manage is a verb not a noun. It is activity not an item.  Making a list might be adequate for those who want to check off regulatory compliance, but it’s does not produce a ROI.

They don’t manage threats

To manage threats you need to actively monitor risk drivers and influences thru lead and lag KRIs in real time.  Reporting systems aren’t much use if they’re telling you after the event. By the time it shows up on a heat map it’s not a risk, it’s an incident.  Simply moving your risk management from spreadsheets to a cloud risk register does nothing to pursue an active defence against threats.

To create a workable system, you need to take your risk registers, work out what causes those risks to worsen (drivers and influences), and what lead/lag KRI to use to monitor the movement of those drivers and influences.  You then need to set up a real-time system for collecting those KRIs and alerting the appropriate people who can act on the threats immediately.

They don’t tell you HOW it will affect Objectives

The common practice of recording what objectives might be affected by a risk does nothing to assist in achieving or optimizing those objectives.  The real purpose of risk management is to navigate the myriad of influences on the objective’s outcome as they occur, i.e. it is an interactive real-time activity.

Risk Management’s primary purpose in the strategic and tactical planning phase is to identify the best course to market and thereby optimize resources (time and capital).  This requires specifying HOW risks and actions interrelate and compound effect on one another.  This highlights two things.  For ERM to work it must integrate both risk and actions, and it must know HOW variations in either compound effect.

Once these are in place they can easily be used to monitor progress in achieving objectives. Workflows and Issue reporting become inputs to risk drivers and influences which in turn automatically update risks. With a real-time aggregation of risks (roll-up), alerts can be sent to interested parties when the risk threshold of any objective is threatened.

See also: The Current State of Risk Management  

They don’t improve the quality of decision making

By definition complex systems (the business world) are chaotic (see Chaos Theory), where small variations alter outcomes, like the weather and the winner of the Melbourne Cup.  But risk management was never about predicting the future. It’s about providing advice on the effects of possible decision outcomes and being prepare for any adverse effects.

But here’s the real rub.  For ERM to be useful it has to employ Predictive Analytics and machine intelligence.  In my defence, Predictive Analytics doesn’t actually predict the future, it just highlights obscure facts. It provides true decision making collateral on possible opportunities and threats in any scenario, from which “informed decisions” can be made, instead of “gut feel” guesses.  It helps mitigate decision bias and raise ramifications sometimes overlooked in the heat of a problem.

Obviously many ERM systems have numerous other failing, such as a single hierarchy for aggregating or “rolling-up” risks (wouldn’t it be nice if the world was that simple), and not including Incident Management in ERM to create a closed feedback loop, which drives evolution and effectiveness.  But the single most important thing is to use your risk collateral as part of the day-to-day operational decision making and not to just let it stagnate in risk registers being reviewed annually.

3 Ways to Measure Models’ Effectiveness

Most insurers are using some form of predictive modeling, but it can be difficult to know if it will remain effective over time. Evaluating a predictive model can be tricky because, while there are many ways data can be measured, there is no accepted standard. With the considerable investment that’s involved in predictive analytics, the C-suite understandably wants to hold certain yardsticks to the models and see if they are performing well, and to make sure every stakeholder is using it correctly. Having a forward-looking evaluation can make all the difference when making key decisions, especially if there is trust in the measuring mechanism.

Below are three new ways that insurers can evaluate the impact of predictive models, based on a model currently in production for a regional workers’ compensation insurer. The graphs below provide real-time insights that can help predictive modeling avoid becoming a black box, meaning that you can only see the output of the predictive model, not the input or how that output came to exist. The first two graphs separate out 10 equal portions of either premium or policy count, with each portion referred to as a “bin.”

1. Monitoring that a model is still current and accurate

You need to be able to regularly check if the model you have in production is still up-to-date and providing accurate scores. This graph illustrates the overall model lift on the book for a regional workers’ comp insurer in 2015 and 2016. The insurer’s model is generating a low score on business that’s running very profitably — the lower-risk bins 1, 2, 3 are approximately 30% better than average. Policies getting a score in the higher-risk bins 8, 9, 10 are all running at twice the average loss ratio. This provides a clear indication of what to target and what to avoid.

Bottom line: This model is still current and accurate.

See also: Top 6 Myths About Predictive Modeling  

2. Tracking the impact of a model on decision-making

To realize the benefits of analytics, your staff needs to leverage the insights to make more informed decisions that create improved results. This is a graph of “decision data” from Valen’s InsureRight Manage application. Orange represents policies that were declined, red is quoted and lost, green is quoted and bound and yellow represents non-renewals. It’s evident that declinations are low on the good business — less than 10% — and high on the other end, approaching 50% for bin 10. The insurer is not renewing policies in bins 9 and 10 and, most importantly, retaining more than 50% of business in bins 1, 2, 3.

Bottom line: Underwriters at this insurer are using the model to make more profitable risk selection and pricing decisions.

3. Measuring if the overall risk quality of a portfolio is improving with a model in production.

If you’ve established that your model is accurate and your people are using it, the next question is what kind of impact it’s making to the quality of your portfolio. Are we lowering the risk of our book of business? This view shows the insurer’s risk-selection trends, with an overview of how risk-selection decisions have been influenced by a model and the resulting change to the portfolio. The blue bars represent premium volume by month, and the orange line represents average risk score (i.e., loss ratio prediction) by month. Though there is some variability from month to month, the overall downward trend indicates improvement over the course of the year. There is a small uptick in December 2016, which provides an indication that further analysis is needed.

Bottom line: The risk quality of this portfolio is improving, though still requires careful monitoring.

See also: Survey: Predictive Modeling Lifts Profits  

Not only is it crucial to measure before an implementation takes place, it’s vital to do so both during and after, as well. Predictive modeling only works well if it is aligned with stated business goals, and knowing how to measure that is key to an insurer’s bottom line. With these three new ways to measure, insurers now will have different yardsticks to see whether it is successful and if they are using the actionable insights.

Proof of Value for Medical Management

Everyone knows the bulk of workers’ comp costs now are medical. Claims reps and nurse case managers handle injured workers and their medical costs with utmost care. Anecdotes show that their work saves time and money. The problem is that concrete evidence of their value has been elusive—until now.

How can costs avoided and time saved be measured? The measurements are like rabbits pulled from a magician’s hat. What really happened?

Quantifying what did not happen is usually impossible. However, quantifying and measuring savings is completely feasible through a different approach, using predictive analytics.

The workers’ comp industry does not readily embrace change or innovation. That is changing as pressure increases to become more efficient to sustain profitability as resources shrink. The best approach to meeting this challenge is incorporating advanced technical strategies such as predictive analytics that are designed to support and streamline the business process and make workers smarter. The collateral benefit is being able to objectively measure and report savings.

The solution is to extensively analyze the organization’s historic data using predictive analytics and deliver the insights in the form of actionable information to all the stakeholders, including claims reps, medical managers and other decision-makers. Just a few steps are needed, including data analysis, data monitoring, informing and integrating the efforts of stakeholders and measuring the savings.

The first and most critical initiative is analyzing an organization’s historic data using predictive analytics methodologies — because each organization has unique internal and culture processes regarding claims handling and medical management, using others’ data, regardless of how large the database, can mislead.

See also: 2017 Issues to Watch in Workers’ Comp

Situations and conditions found in the past are likely to recur. Once the risks are identified in historic data, they can be searched programmatically in current data through continuous data monitoring. When problematic situations occur in the data, appropriate responses and interventions are mobilized immediately. The insights are delivered to medical management stakeholders, including claims reps, medical case managers, senior management and others as appropriate. The knowledge delivered is structured to assist them in decision support and coordinating efforts.

Risk information in claims is delivered concurrently to stakeholders so they can make early and sound decisions, then initiate appropriate action. Importantly, all medical management participants receive similar information so initiatives are coordinated and integrated, thereby implementing strong, multi-disciplinary approaches.

When risk conditions in claims are identified in this manner, reserves in that claim need attention, as well.  When events and conditions in claims change, indicating a need for more intense medical management, reserving should also be addressed. Based on predictive analytics, the probable ultimate medical costs are projected and portrayed for claims reps, thereby providing key knowledge to support appropriate action.

Data monitoring identifies claims with risk conditions concurrently and informs the stakeholders immediately. Intervention efforts are coordinated among claims reps, medical case managers and others, providing broad-based, integrated initiatives leading to improved results. Savings are gained through proactive, coordinated intervention by professionals who are offered key information for decision support making them accurate, efficient, and effective.

See also: On-Demand Workers: the Implications

When claims are closed, objective savings are measured by comparing projected performance based on predictive analytics with what was accomplished through active, integrated initiatives across all medical management participants. The calculations are quantifiable and objective.

The simplest and most rewarding approach is to outsource this process to a knowledgeable medical analytics company. Internal processes need not change, but professionals and business processes are made more accurate and efficient—a win for the organization, its employees and its clients.

Technology is far less expensive than people. When it is designed to assist professional workers by making them more accurate and efficient, the return on investment is profound.