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Top 6 Use Cases of Predictive Analytics in Insurance

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Top 6 Use Cases of Artificial Intelligence and Predictive Analytics in Insurance

But first, some history on the impact of AI and Machine Learning in insurance

Over the past decade, we witnessed a titanic shift in the way insurance businesses operate.

The companies that embraced the “Digital Shift” have thrived, while the companies and business models that ignored it or were slow to adopt an Internet/mobile strategy have sunk.

Hopefully, as the surviving insurers view the floating remains of their fallen competitors, they understand that a new threat has emerged.

And it has a name – Artificial Intelligence.

The use cases and applications of artificial intelligence in insurance are seemingly endless. 

While you shouldn’t expect to see an iron-clad Schwarzenegger approaching in your rearview, the impact of A. and machine learning and the threat it poses on those who ignore it is very real.

The rise of applicable AI has been described as the 4th industrial revolution. Data is the new oil – and AI is the key to unearthing it.

For some perspective, 90% of the world’s data has been created in the past 2 years.

data creation statistics infographic

The growth of data creation is exponential and AI is the key to unlocking it. 

By this time next year, it’s estimated that 1.7MB of data will be created every second for every person on earth.

Don’t bother trying to do the math, I promise you, your calculator is not big enough.

AI and machine learning are the only ways to harness the insights from such an immense amount of information.

By using AI to look at the past, we are able to glean a previously unimaginable look into the future.

Read more about “The Digital Shift”

The State of AI in the Insurance Industry Today

Unlike their digitally native counterparts, traditional brick-and-mortar industries like Insurance have been very slow to adopt newly available technology.

They are lucky – their moats have, for the most part, yet to be breached. But times are changing.

For years, these behemoths have survived based off of minor product enhancements and customer loyalty.

But decades of stagnant physical infrastructure, legacy business partnerships, and technological neglect have made their seemingly impenetrable fortresses a little less daunting.

And the newcomers have taken notice.

Customers, especially millennials, no longer care that their parents used a certain broker, that the retail branch is in their town, or that they “trust” one brand over another.

Today’s buyers find you, you don’t find them.

And with pricing transparency, reviews, blogs, articles, social networks, and industry influencers – there is no shortage of ways to discover everything you need before buying a policy.

While I wouldn’t insult the industry as a whole and deem it fully commoditized, it’s getting pretty dang close.

Turn on a Football game and you will see 6 different insurance companies vying for the same customers…

This one saves me 15% or more, that one has a quacking duck, the other one has Jake in khaki’s, another shows the mayhem in life.

I didn’t even mention the woman running around in the all-white commercials or the ones with Peyton Manning singing a jingle, but surely you get the point

Ignoring the companies with clever commercials and talking animals, a majority of the Insurance industry is still acting as if it is 1997.

Fax this. Snail mail that. Print, sign, scan, return. Or, those dreadful four words, “We do that manually.”

According to a recent PYMNTS case study – just 5.5% of Financial Institutions have adopted AI and only 12.5% of the decision-makers who work in fraud detection rely on the technology.

They instead rely on “more limited – and increasingly outmoded – technologies like business rule management systems (BRMS) and data mining.”

I genuinely fear for companies choosing to keep their head in the sand.

And a lot of the time, it isn’t their fault – their systems are built on severely outdated technology.

This makes it either physically impossible to improve upon or so costly to reconstruct that they choose to stick with the old, “It’s worked for us so far!” mentality.

Ignorance is bliss, as they say.

The digital transformations these companies must undergo to survive likely feels an awful lot like trying to steer the Titanic away from the impending iceberg.

You’ve twisted the steering wheel as far as you can, but the ship only turns so fast.

So, turning our attention to what the future holds, what should these companies do?

While waving the white flag and milking their cash cows until someone inevitably displaces them is certainly an option, it isn’t the one I would recommend.

Embracing the future and implementing an AI strategy could very well mean the difference between life or death for insurers.

What do the future of Artificial Intelligence and Machine Learning applications and use cases look like for Insurance?

To its credit, a majority of the insurance industry has become keenly aware of the technological advances that threaten their incumbent businesses.

Mobile-first business models have stripped away the costs of having a heavy physical presence.

This opened up holes in the canopy for new entrants to grow.

In an effort to stay ahead and fight off companies looking to dis-intermediate traditional insurers, 66% of the legacy players are choosing to invest in and adopt their own AI and technological solutions.

Investments range from car sensors and telematics that monitor driving behavior and AI software that analyzes social media accounts to Drones, IoT device networks and predictive analytics for insurance underwriting.

The amount of data created on a daily basis is incomprehensible for most humans.

Because of that, insurers are looking at new ways of analyzing that data for a competitive advantage.

We have already seen a significant amount of process automation and digital transformation in the last decade.

The next ten years, however, will be all about predictive analytics.

Lemonade A.I. insurance underwriting

Lemonade has raised $480 million and is disrupting the insurance industry using AI

Predictive analytics in insurance has stood out as a key area of focus for most of the industry leaders.

And if copying is a form of flattery, I highly suggest you get your copy on.

How is predictive analytics used in insurance?

Simply put, by looking at our past, we are able to better predict our future.

Looking at the past decade, the insights are fairly obvious…

Streamlining online experiences benefitted customers, leading to an increase in conversions, which subsequently raised profits.

Add in operational automation for increased efficiency and you’re looking at millions, maybe billions, of dollars a year in additional revenue and cost savings.

That strategy worked successfully for a while.

However, simply automating repetitive tasks and giving your website a makeover will not be enough to withstand the onslaught of competition.

In order to survive, insurers must integrate artificial intelligencemachine learning, and predictive analytics everywhere they can.

Integrating predictive analytics in insurance software has quickly become the leading initiative on most of the top insurance carriers roadmaps.

What used to be a traditional, rule-based framework is now transforming into a data-driven, automated, highly intelligent and predictive system.

The top 6 ways companies are using predictive analytics today are:

  1. Pricing and Product Optimization
  2. Claims Prediction and Timely Resolution
  3. Behavioral Intelligence and Analytics to Predict New Customer Risk and Fraud
  4. Uncovering Agent Fraud and Policy Manipulation
  5. Optimizing User Experience through Dynamic Engagement
  6. Big Data Analysis

Let’s look at this artificial intelligence in insurance application and use cases one by one…

Pricing and Product Optimization

Armed with more granular data and predictive analytics insurance modeling, actuaries can now build products better suited to dynamic business and market conditions, risk patterns and risk concentrations.

In other words, historical costs, claims, expenses, risk, and profit are projected into the future.

Predictive analytics algorithms give insurers the opportunity to dynamically adjust quoted premiums.

For instance, in property insurance, continual monitoring of variables like claim history in the neighborhood, construction costs, and weather patterns helps to predict risk, price more accurately, but most importantly, leads to new product creation for the evolving needs of customers.

By analyzing customer preferences, behavioral signals, buying patterns, and pricing sensitivity, companies are able to use their predictive algorithms powered by machine learning to constantly optimize and showcase more relevant insurance products.

Up until now, it was difficult to customize policies at the individual level.

However, companies can now use pay-as-you-go and dynamic pricing models based off of customers predicted risk, behavioral signals, and buying preferences.

And many of the digital-first products are a result of millennial influence. 

As Richard Hartley, CEO & Co-Founder of Cytora puts it in Gina Clarke’s “How Your Insurance Quote Is Powered By A.I.” article…

“Millennial consumer behavior is forcing irreversible changes across financial services leading to the emergence of digital-first and app-based services for banking, loans, mortgages, and investment. As the millennial cohort start their own companies and move into decision making roles in business, commercial insurance is beginning to undergo the same revolution.”

Given millennials and Gen Z are quickly making up a majority of the buyers in the insurance market – what should traditional insurers do?

They need to skate to where the puck is going.

By integrating currently available AI and predictive analytics tools, they can avoid a full reboot of their legacy systems.

Not only that, but they’ll be able to thrive in the new age of digital transformation.

Claims Prediction and Resolution

Automating insurance claims processing was a huge step forward as insurers continue their digital transformations.

Given that claims are the part of the insurance lifecycle that has the highest percentage of attempted fraud, it is one of the first places companies are looking to integrate AI.

While legacy insurers are integrating AI software into their legacy claims process, companies like Lemonade are starting with an AI-first approach.

They tout that they can process claims faster and by using a chatbot, they’re able to provide customers with faster payouts.

Lemonade isn’t the only company using chatbots during the claims process.

These chatbots are getting more sophisticated and can review the claim, verify policy details and pass it through a fraud detection algorithm before sending wire instructions to the bank to pay for the claim settlement.

This can help speed up processes and reduce human error.

Other companies like Tractable offer machine vision software to help insurance agencies automate claims.

Insurance agents can upload imagines associated with a claim, such as a damaged car, and an estimate of what they think the appropriate payout is.

The software then compares the image to a database of similar images and allows the agent to make smarter payout decisions.

a.i. insurance software drone

This helps companies avoid overpaying for claims.

Some companies like Cape Analytics offer a service which they claim can help property insurers underwrite more accurately and more cost-effectively using satellite-based machine vision.

They can assess information about the roof, property, treeline, pool, trampolines, etc. saving the company from needing to send a human inspector to the property.

AI is also used to spot anomalies and unknown correlations that would be impossible for the human eye to detect.

Behavioral Intelligence and Analytics to Predict New Customer Risk and Fraud

Insurance fraud has many faces…Stolen identities to obtain a new policy, false payee information, false declarations, computer bots and so on.

According to the FBI, the annual losses related to insurance fraud are as high as $40 billion, costing the average American family $400-$700 in increased premiums each year.

Predictive analytics insurance software crunches data from behavioral biometrics and behavioral analytics software. 

It then correlates it against past customer records to detect fraudulent activity and suspicious behavior patterns.

The newly created “Behavioral Intelligence” software solutions are leading the charge into a more secure and smarter future. 

Read more about Behavioral Intelligence

Predictive Risk Scoring with Behavior Analytics

An important use case of predictive analytics in insurance is determining policy premiums.

Rewind to the 2000s. When a majority of business interactions were face-to-face – it was exponentially more difficult to get away with risky behavior.

If a customer pulled out a sheet of paper and was copying over their home address, social security number, and the spelling of their middle name – that would likely raise some red flags.

Today, however, as businesses have shifted online, most business interactions are now ‘faceless’.

This means that companies and their agents have lost the ability to read and react to their customer’s body language.

Can you imagine sitting down face-to-face with a customer today, but before they begin filling out the papers you put on a blindfold?

digital body language ai predictive behavioral analytics

Companies are blind to their user’s digital body language. (Image credit: securityintelligence.com)

That would be like a teacher walking out of the room after handing out the test.

formotiv a.i. insurance digital body language

ForMotiv collects over 5,000+ behavioral data points on each unique application.

And on top of that, the teacher didn’t require that you ‘show your work’. Instead, they simply graded you on your final answer.

Luckily, companies like ForMotiv are leading the way into predictive behavioral analytics.

By measuring customer (or agents) “Digital Body Language” – think keystrokes, idle time, mouse movements, copy/paste, corrections, etc. – they are able to use machine learning to correlate certain behaviors to outcomes like risk and fraud.

For example, if an applicant is changing answers on e-med questions, sources of income, or health history.

Companies can now be alerted of this behavior, in real-time, and price their risk accordingly.

This is a far superior solution to what most companies are doing today – waiting until there is a claim in the future and figuring it out then.

It’s the difference between prescriptive medicine and reactive medicine.

Speaking of healthcare…

Health insurance companies are using predictive behavioral analytics and beginning to integrate Internet of Things devices as well.

Wearables such as Fitbit and or Apple Watch can provide ongoing assessments of the individual’s health risk exposure.

Adding predictive behavioral analytics and predictive analytics, in general, helps limit losses for more advanced insurance carriers.

Behavioral Biometrics to Prevent Account Takeover and Fraud

Behavioral Intelligence, not to be confused with behavioral biometrics, is great for assessing new customer risk and comparing it to every other user.

It uses predictive behavioral analytics to measure how unknown user John Smith compares to the millions of other applicants and their outcomes, and predicts what John Smith’s likely outcome is.

This is often confused with Behavior Biometrics, and while they play in the same arena, they’re playing different sports.

Behavior biometrics is all about comparing John Smith to… John Smith.

Behavioral Biometrics helps companies with identity proofing, continuous authentication, account takeover fraud, and vishing scams.

Much like your signature, voice, thumbprint, and face are unique to you – so is the way you interact with a device. Behavioral biometrics measures how John Smith uniquely interacts with a device.

Is he typing the same way? Was he right hand dominant and now left hand dominant? Does he swipe up or down the same?

Using behavioral biometrics, companies can determine if a logged in John Smith is, in fact, John Smith.

As scary as it sounds – it might be easier to fake a signature today than it is to mimic someone’s ‘behavioral’ signature.

Because of this, behavior analytics software can help drastically reduce account takeover and prevent fraud.

Read more about Understanding Digital Body Language

Uncovering Agent Fraud and Application Manipulation

Newly available technology creates a cat-and-mouse game for risk officers and fraudsters. The same technology used to stop fraud is being used to enhance it.

Bots can automatically apply to thousands of financial service companies for thousands of different products. They only need one approval to cause serious harm.

While fraud continues to evolve and affect all types of insurance, the most common in terms of volume and average cost are automobile insurance, workers’ compensation, and health insurance / medical fraud.

The tricky part for insurers, however, is that large percentages of fraud are actually coming from inside their own walls.

Believe it or not, customers are not as savvy when it comes to committing fraud as their agent counterparts.

One very common but hard to prove way insurance agents commit fraud is application manipulation.

ForMotiv recently worked with a Top 10 Life Insurance carrier to identify and solve this exact problem.

The results were astounding.

The original use case was to determine how many questions customers were manipulating on their life insurance applications.

For instance, were they changing their source or amount of income? Or were they trying to game their e-med questions to receive a better rate?

As it turns out, after a month of behavioral data collection we found some phenomenal insights regarding the agents

The data showed the following… 73% of the applications had 2 or more questions corrected by an AGENT after being submitted by an applicant.

The Insurer had this to say… “You helped us find the agents who represent themselves better than their employer and customer.”

Yes, we were able to identify a significant amount of customer manipulation as well. But what we did not expect to see was how often and aggressively agents were gaming the application.

Changing a few key answers to receive a better rate helps them convert more customers. More customers = more commissions. Simple formula. 

We’ve heard this from a few customers and prospects… “Oh, no, our agents would never do that.”

Well, I hate to be the one to break it to you, yes they would.

To think there is absolutely zero suspect or blatantly fraudulent activity going on is like thinking your kid didn’t have their first beer until they were 21.

Or that professional athletes don’t use substances to get an edge.

Using ForMotiv’s “Forensics” tool, customers are able to clearly determine not just WHAT answer is being provided, but HOW.

This level of insight was previously impossible to extract.

Today, it is being used by 4 of the Top 10 life insurance carriers. Gathering behavioral intelligence can protect carriers with claim contest-ability and special investigations.

Not to mention, it can save companies millions of dollars.

Companies need to be aware of the fact that internal or distributed agents often act in their own best interest. And their self-service ignores the ramifications for their clients and companies.

Dynamic engagement and optimization of user experience

According to ITL and their prediction of InsurTech trends, the main focus is on a digital-first customer-centric approach.

A KPMG report also stresses how customer satisfaction and retention is becoming a more important KPI than operational efficiency.

As products are commoditized, loyalty becomes a thing of the past.

So what do you do now that maximizing customer satisfaction has become the name of the game?

I believe predictive analytics holds the key to achieving optimal CX and customer loyalty.

Using behavioral AI tools, companies are able to uncover behavioral insights at the form field level.

For instance, ForMotiv gives its customers behavioral intelligence on how their users and agents are actually interacting with the forms and applications, in ranked order, and provides explanation-based A/B testing recommendations. a/b testing

This insight allows marketing and customer experience teams to remove bottlenecks, troublesome questions, and chokepoints and optimize their form fields for increased conversion and great customer & agent satisfaction.

In addition, companies can use innovative predictive behavioral models to measure user intent, in real-time, and can uncover insights into the actual intent of the users.

Does this look like a profitable customer? A fraudster? Someone having trouble with the application?

By reading a customer’s digital body language, companies can use predictive behavioral analytics to create dynamic experiences for customers.

This helps to reduce friction for ‘good’ customers and add friction for seemingly ‘bad’ customers.

Do they seem confused or stuck on a question? Offer contextual help, a chatbot, live chat, and more.

Are they behaving in a risky manner or acting like a bot? Dynamically add friction, such as an “Upload a government-issued ID” process question.

Using these same tools, companies can predict application abandonment with near almost pinpoint accuracy.

This allows them to dynamically engage a user who seems likely to abandon the application.

Big Data Analysis

As we mentioned before, the amount of data created every second is virtually incomprehensible.

For a little context- the difference between a million seconds versus a billion seconds is 11.5 days versus 31.75 years.

So comparing a million IoT devices to a few billion?

It’s mind-numbing when you consider the data created by these devices.

In 2020, it is estimated that there will be 20.4 billion IoT devices. That is ~130 new devices connected to the Internet every second.

IoT devices AI

Cisco expects the total data generated to exceed 800 zettabytes, with a single zettabyte equal to about a trillion gigabytes.

Using the above time example, a trillion seconds equals about 31,710 years.

With about 90% of the data being unstructured, companies will be forced to embrace machine learning and predictive analytics more than ever to keep up with the demands of IoT.

Telematics (in-vehicle telecommunication devices), drones, wearables, smart speakers… Refrigerators, washing machines, toasters…

By adding Internet access to every device imaginable, predictive analytics for insurers will be crucial for survival.

For instance, the behavioral data of applicants is computed when underwriting premium rates for vehicle insurance.

Does the driver slam on the brakes? Do they peel around corners? Do they park their car often in deserted locations? Are the roads conditions good where they drive? 

By applying predictive analytics, insurers can assess the likelihood of the insured in being involved in an accident. 

Or even the odds of having their car stolen by matching behavioral data with external factors like safe neighborhoods.

Ultimately, this helps tailor policies and premiums that protect the insurer as well as the insured.

Bottom line

Never before has it been more important to implement an AI solution for insurers.

Using predictive analytics is the best way for insurers to fend off competition and thrive in a competitive market.

As the digital shift continues to impact the industry as a whole, transforming user data into actionable intelligence is imperative.

Companies that integrate predictive analytics will undoubtedly increase their market share.

They will also boost customer loyalty and can significantly grow their revenue while reducing their costs.

 

Are you ready to implement predictive analytics and Behavioral Intelligence in your insurance processes? Let’s chat.

 



 

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