Tag Archives: admiral

Is Big Data a Sort of Voodoo Economics?

Is charging consumers more for their insurance because they use a Hotmail email account or have a “non-English-sounding” name a valid application of predictive modeling, or does it constitute presumptive modeling and unfair discrimination? Does it matter if “big data” is riddled with bad data and bogus information as long as it improves insurer expense ratios? Is this the insurance industry’s version of voodoo economics?

It’s no secret that I’ve written things about the Holy Crusade known as insurtech that are critical or at least suggest caution in climbing aboard the hype and hyperbole bandwagon. Insurtech has been touted as the philosopher’s stone with its ability to turn “lead” data into golden predictions.

One component of this “movement” is big data, the miracle cure for perceived stagnant industry profits known as data analytics and predictive modeling.

There is nothing new about the importance and value of data and its wiser big brother, information. Predictability, in the aggregate, is the cornerstone of industry stability and profitability. It’s the foundation of actuarial science. But, to be of value, the data must be credible, and the models that use it must be predictive by more than mere correlation. And, to be usable, the data and models must meet legal requirements by being risk-based and nondiscriminatory. That’s where one of my concerns lies. Just how valid and relevant is the data, and how is it being used?

What prompted this article was a blurb in Shefi Ben-Hutta’s Coverager newsletter [emphasis added]:

“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.” – Admiral Group in response to research by The Sun that found the insurer could charge users…extra on their car insurance, simply for using a Hotmail email account instead of a Gmail one.

This revelation came just days after The Sun ran an article accusing the U.K. insurer of charging drivers with non-English-sounding names as much as £900 extra for their insurance. I don’t know enough about insurance in the U.K. to opine about the potential discriminatory nature of jacking premiums on people whose names don’t sound “English,” but my guess is that U.S. state insurance departments likely would not look favorably on this as a rating factor.

See also: Strategies to Master Massively Big Data  

Historically in the U.S., P&C insurance rates have been largely based on factors that are easily ascertained and confirmed. For example, the “COPE” acronym (construction, occupancy, protection and exposure) incorporates most of the factors used in determining a property insurance rate. From the standpoint of the fire peril, frame construction is riskier than fire-resistive construction. A woodworker is riskier than an office. Having a fire hydrant 2,000 feet away from a building is riskier than one 200 feet away. It makes sense. It’s understandable. It’s provable.

The risk inherent in these factors is demonstrable. The factors are understandable by consumers and business owners. It’s easy to identify what insureds can do to improve their risk profile and reduce their premiums. Advice can be given on how to construct a building, install protective systems, etc. to reduce risk and insurance costs. Traditional actuarial models are proven commodities, and state insurance regulators have the expertise and ability to evaluate the efficacy of rate changes.

What these factors are not, in many cases, is inexpensive. Confirming this information may require a physical inspection. Some state laws require or compel such inspections. In my state, our valued policy law says that buildings must be inspected within 60 days of policy inception or the law is triggered and a carrier may have to pay policy face value for a total fire loss. Are the insurtech startups selling homeowners insurance even aware of this? It is understandable that insurers want to reduce any unnecessary underwriting expenses if there are acceptable alternatives. Doing so may improve profitability or make them more competitive by enabling premium reductions.

This is where insurtech and technology in general can play a valuable role. Using reliable data on construction and size of buildings, building code inspection reports, satellite mapping for hydrant location and so forth can have an almost immediate impact on the carrier expense side and potentially the loss component. To a large extent, this is actually being done, but the search for something more (or less, if we’re talking about expenses) continues.

Enter “big data” and predictive modeling, along with a horde of people who know absolutely nothing about the insurance industry but a lot about deluding gullible people with hip press releases. They tout the salvation of phone apps, AI bots and “black box” rating algorithms with 600 variables and factors. Factors such as whether someone, according to their Facebook page or other online source, bowls in a Wednesday night mixed league where (speaking from personal experience) the focus is more on beer consumption than bowling and how that might affect the risk of an auto accident.

The $64,000 question is how reliable are these predictive model algorithms and how credible is the data they use? The author of an article titled “How Trustworthy Is Big Data?” claims that there is typically a lot less control and governance built into big data systems compared with traditional architectures:

“Most organizations base their business decision-making on some form of data warehouse that contains carefully curated data. But big data systems typically involve raw, unprocessed data in some form of a data lake, where different data reduction, scrubbing and processing techniques are then applied as needed.”

In other words, there may be little up-front vetting of the information because that takes time and costs money and, when acquired, there is no certainty that the data will ever be used. So, the approach may be to vet the data only when used, and, as the article suggests, that can be problematic.

The article also addresses the ethics of acquiring information on individuals for what may be perceived as nefarious reasons (e.g., price optimization):

“Just because something is now feasible doesn’t mean that it’s a good idea. If your customers would find it creepy to discover just how much you know about their activities, it’s probably a good indication that you shouldn’t be doing it.”

Going back to The Sun’s Admiral reports, what impression would it make on Admiral’s customers if the insurer advertised, “Pay less if you have an English-sounding name!” Would any insurer advertise something they’re allegedly doing behind closed doors? It’s like the ethical decision criteria of, what would your mother think if she knew what you were about to do? The right to do something doesn’t mean that doing it is right. Does black-box algorithmic rating enable and potentially protect this practice?

I mentioned at the outset of this article that the Admiral report prompted the article. What compelled the article was a recent personal experience when I received a $592 auto insurance invoice a little more than two months into my policy. The invoice attachments never really said why the carrier wanted additional premium, but a quick review indicated the reason.

Our son moved out of the house three years ago, and we removed him from our insurance program, including his vehicle. He still uses the same agency (different insurer) that I’ve used since 1973 to insure his auto, condo and personal umbrella. Our insurer learned that his vehicle registration notice is still mailed to our address. With that information, they (i.e., their underwriting model) unilaterally concluded that he still must live here, so they added him back to our insurance program and made him the primary driver of one of our three autos (the most expensive one, of course). I’m not sure what they thought happened to his vehicle. But, of course, no one “thought” about anything. An algorithmic decision tree spit out a boiler-plated invoice.

I’ve been with this carrier now for four years, loss-free, and paid them somewhere in the neighborhood of $20,000 in premiums, yet they could not invest 10 minutes of a clerical person’s time to make a phone call and confirm my son’s residency. Neither we nor our agent received any notice or inquiry prior to the invoice, but my agency CSR (who, I’m happy to report, is still an empathetic human) was able to quickly fix the problem.

I have written about my personal experiences with a prior insurer involving credit scores. My homeowners premium was increased by $1,000 and, by law, I was advised that it was due to credit scoring. As it turned out, the credit reports of a Wilson couple in Colorado were used. Two years later, my homeowners premium was bumped $700 based on three “reason codes,” which I was able to prove were bogus, and the carrier rescinded the invoice. Now I’m being told that my current insurer’s information source tells them that my son has moved back home. I realize that these tales are anecdotal, but three instances in five years? How pervasive is this misinformation?

Is this what “big data” brings to the table? Big, BAD data and voodoo presumptive (not predictive) modeling? Who really benefits from this? Anyone? One of the insurtech buzz words going around is “transparency.” What’s transparent about “black box” underwriting and rating?

At a convention last year, I spoke at length to a data scientist who was formerly with IBM and is now an insurance industry consultant. Without naming names, he characterized some of the predictive models he has examined as “Rube Goldberg” constructs, with the worst ones resembling “a bunch of monkeys heading up the Manhattan Project.”

See also: Big Data? How About Quality Data?  

Another consultant expressed his concern about some data companies. An NAIC presentation he attended listed some parameters relative to data points being used by carriers. The presenter expressed confidence that carriers were disclosing all of their data points. He is convinced, however, that carriers are using 25% to 50% more data points than the NAIC seems to be aware of. He has written about the abuse of data that lacks an actuarial grounding in risk assessment, again, a requirement of some state laws.

Among the many problems with “black box” rating is the fact that no one may be able to explain how a particular premium was derived. No one may be able to tell someone how to reduce their premium. Perhaps most important, regulators may be unable to determine if the methodology results in rates that are unfairly discriminatory or otherwise violate state laws that require that rates be risk-based. Presumably, future rate filings will simply be a giant electronic file stamped “Trust Me.”

“Big data” might be beneficial to insurers from a cost, profitability and competitive standpoint, but it’s not clear how or even if it will affect consumers in a positive way. All the benefits being touted by the data vendors and consultants accrue to insurers, not their customers. In at least one case, if you have a “non-English-sounding” name, the impact is adverse. The counter argument from the apostles of big data is that the majority of people will benefit. Of course, that was arguably the logic used when schools were segregated, but that doesn’t justify the practice.

In the book “Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech,” the author points to an investigation of a correctional facility system that used proprietary algorithms to help decide bail terms, prison sentences and parole eligibility using various factors, some alleged to be discriminatory (e.g., arrest records of neighbors where the person lived). A Wall Street Journal article, “Google Has Chosen an Answer For You – It’s Often Wrong,” demonstrated how searches often indicated a bias or manipulation by whomever constructed the algorithms being used or by how the search parameters were entered by users. Errors in building replacement cost valuations are often blamed on incompetent or untrained data harvesters and users….Even when the data is presumed to be accurate, it can be used incorrectly.

In 2016, I wrote an article for Independent Agent magazine titled, “The Six Worst Things to Happen to Insurance in the Past 50 Years.” No. 3 on my list was the growing obsession with data vs. people. When I write about these things, I know I run the risk of being characterized as the old man on his front porch yelling at the “disrupter” kids to get out of his yard, but I don’t think I’m a Luddite. I love and embrace technology.

I had a PC before IBM did. I still have the first iPod model. My phone is full of nifty apps. My son is a data scientist in the healthcare industry. I get it. But technology is a tool, not a religion. Far too many people treat technological innovation as sacrosanct and infallible, and anyone who questions or challenges its legitimacy and righteousness is committing heresy.

What’s next, a SnapChat invitation from an AI bot that says, “Welcome to the Insurance Matrix, Mr. Anderson”? Not yet, I hope.

How Sharing Economy Can Fuel Growth

In our last blog of our two-part series on the gig and the sharing economy, we looked closely at how the gig or “on-demand” economy will open up new markets to group and commercial insurers. You can read that blog here.

In today’s blog, we look at the sharing half of the gig and sharing economy. How will a radically shifting market, where borrowing and lending are more prevalent, open doors for commercial and specialty insurers? More importantly, how can insurers fuel the growth of this market by making borrowing and lending more palatable?

Is the Sharing Economy real?

The sharing economy is not only real — it has enormous potential for insurers. RVs make a great example. Recreational vehicle owners use their RVs an average of 28 days per year. With 8.9 million U.S. households owning an RV, that’s nearly 2.9 billion unused RV days per year. RVs are remarkably underutilized, making them a prime market for sharing. RV sharing sites, such as Outdoorsy, help owners to profit from their RV, while helping non-owners to gain access to RV use.

Multiply this idea many times over. ATVs are underused. Boats, chainsaws and generators are underused. Cars, homes, cabins, campsites, hunting property, musical instruments, bicycles, hockey rinks and electronics are all commonly underused. The only thing standing in the way of sharing them all is… insurance to cover a new type of risk.

See also: Opportunities in the Sharing Economy  

While some personal lines insurers are making a case for insuring some of these risks by adapting their personal lines products, it is commercial insurers that may have a leg up when it comes to understanding how to price risk for niche risks or groups and how to offer innovative products to these “small – medium” business owners.

Majesco’s forthcoming consumer research report finds that the consumption of ridesharing and home- and room-sharing services has a strong and growing appeal. The year-on-year growth of these activities was between 5% and 15% depending on the activity, highlighting a growing interest and use across all generations due to the digitally enabled capabilities driving ease of use. (For a further look at sharing economy trends, read Changing Insurance for the Digital Age, a collaboration between Majesco and Global Futures & Foresight.)

Experience is a factor

The sharing economy is related to the experience economy. As millennials enter the middle class, they are owning less “stuff” and putting more emphasis on having greater experiences. The sharing economy is fostering their desire to do more by owning less. With less of their income tied up in ownership and maintenance, they have more to spend on borrowing and renting. They are concerned about risk, but they don’t want a confusing transaction to sit in the way between them and the experience. So, insurers must find ways to build simple insurance experiences into the front end of the overall experience, or hide purchase experiences within the usage itself.

Insurance is the answer to some of the sharing economy’s most pressing issues.

Before “sharing economy” was even a term, sharing was at the heart of insurance. So, it would make sense that insurance would fit well into the sharing economy. Insurance, however, has had a product focus, reflected in the organizational silos based on risks and products, by separating personal versus commercial, rather than a customer focus that shifts between different risks (i.e. personal vs. commercial) based on the behaviors or use.

This is why sharing economy insurance incumbents may find themselves disrupted by Slice, Cover Genius and Metromile and similar entities that are popping up everywhere. Optimistically, however, the sharing economy is igniting an insurance renaissance with traditional insurers like Geico, Admiral, AXA and others asking themselves how they can serve people in the on-demand sharing economy … both personally and as a “business.”

In the sharing economy, it’s all about protection for the shorter timeframes and meeting the uncommon, on-demand need … allowing the customer to fluidly switch back and forth from personal to “commercial” needs. And, it’s all about giving owners and businesses incentives to lend property or assets. Insurance can answer these issues.

Insurance will fuel the sharing economy if insurers can build compelling value propositions

Rental companies are familiar with the risk of lending. They understand what is at stake, and they price insurance into their products or create contracts to handle damage. A new round of entrepreneurs is arising, however, who are using technology to match peer-to-peer lending. Websites such as MyTurn are enabling anyone to launch asset-sharing organizations. These types of companies are unfamiliar with how insurance can offer them protection and how coverage should be handled for a broader segment of products and users. This is an area where insurers can fill a growing gap.

The insurance value proposition in the sharing economy is to make both the lender and the borrower comfortable that the transaction can occur without the threat of loss. All that remains for insurers, then, is to determine where sharing is creating insurance gaps and how they can build, sell and service sharing products.

Data is critical

Consider how data is currently used in underwriting most products. For the most part, insurers pull from traditional data sources for underwriting purposes, and, though they may have reduced underwriting time, it is rarely real-time data. The sharing economy is different. It will require hyper-short underwriting loops based on real-time data because many aspects of sharing happen quickly and in a non-uniform pattern. The whole concept of on-demand insurance assumes the flip of a switch between being uninsured and insured.

On-demand insurance products should have the capability to score based on evidence analyzed from many reliable sources. Sharing economy insurers may want at least some scoring related to social profiles and common pastimes and behaviors. These aren’t easy data points to collect, but the further down the road on-demand insurance progresses, the greater the demand will be for every type of character-based data.

Cloud platforms are necessary

In essence, sharing economy insurance requires on-demand micro duration insurance coverage and blurs the boundaries between personal and commercial insurance.  But insurers face challenges including: creating a micro-duration insurance business model; real-time pricing determination based on micro-segmentation and varied factors; mobile-first user experience; low transaction value but high transaction volume; and low-touch, end-to-end operations.

See also: 4 Mandates for Agents in Sharing Economy  

To support these new coverages requires a next-generation core platform that is a complete architecture redesign with an alchemy of data, analytics, digital and processing components; customer-journey-focused solutions; significant reliance on AI for pricing and underwriting; and a light footprint and auto-scale capabilities for high volume support on cloud.  Furthermore, there has to be a strong “find and bind” integration architecture to tap into an ecosystem of innovative services. As we highlighted in our Cloud Business Platform: The Path to Digital Insurance 2.0 thought leadership report, many of the new insurers providing these innovative products have such core platforms in the cloud to allow them agility, speed and innovation in a continuously changing market.

Agility is (no surprise) highly necessary

For insurers to grab the opportunities as they arise, they will need to understand what new technologies can do to facilitate sharing relationships. They will need to use a next-generation core platform that is scalable and allows for real-time data and agile product development. Cloud platforms will lend themselves to many of the necessary features, but expert data integration and “find and bind” ecosystems will also be vital.

For a better look at growth opportunities within the sharing economy, don’t miss Majesco’s report, A New Age of Insurance: Growth Opportunity for Commercial and Specialty Insurance in a Time of Market Disruption.

The Hidden Issue in Facebook Dispute

Headlines can have direct bearing on the world of data and insight —and this has been even more frequent in recent years. This, increasingly, including topics like data monetization.

One such story was the news that Facebook was preventing Admiral Insurance (in the U.K.) from using social-media-activity data as a means of assessing risk. Admiral planned to enable Facebook users to not only log on with their Facebook ID but to also opt in to giving Admiral access to their data in return for potentially lower car insurance premiums.

Given the higher cost of car insurance for younger drivers, the idea had real appeal.

However, it appears that, at the last minute, Facebook announced that it is not willing to allow such data from its users to be shared with Admiral, citing data privacy concerns. (If you missed it, the full BBC news report is here.)

Why should such news matter to customer insight leaders? This dispute gets to the heart of a new battleground for both service providers and those collecting significant amounts of user-provided and user-generated data/content.

See also: 5 Predictions for the IoT in 2017  

The issue at stake

Sadly, this appears to be another example of today’s data barons relying on an old-style command-and-control mindset. To reach the potential for greater data democracy, we need to see a move in corporate culture toward greater collaboration and transparency.

We may never know the rights and wrongs of Admiral’s negotiations — like whether or not it was naive in failing to contractually lock down access to the required APIs. However, Facebook’s behavior still appears to be heavy-handed and conveys an arrogance in regard to data ownership that is disappointing. But then, perhaps, the leopard, which thought it was fair to experiment on users without permission, has not really changed its spots.

It is an interesting object lesson for other firms aiming to create value from social data and data sharing between businesses.

Customers should own their own data

The core of my concern, however, comes from a customer perspective. As more and more firms — from Admiral to TripAdviser — are looking at data-monetization plans, firms should remember whose data it is. Hiding behind fine words about protecting privacy does not mask how consumers are being denied decision-making power about their own data.

It is my hope that truly customer-centric organizations can learn from this bad example. People deserve to be educated about the reality of data monetization in our changing world. Many applications, with permission, will have the potential to make peoples’ lives easier or to save them money (for the price of their data).

Infantilizing customers by deciding what is to be allowed is “Nanny State” thinking. What our industry and our society needs, instead, is clear communication that gives people the opportunities and choices of what should happen with their data. I suspect many young drivers would have chosen to share their Facebook data with Admiral in return for cheaper premiums.

Changing mindsets, thinking in terms of customers as more active data owners, also happens to be the best mindset to adopt in preparing for GDPR.

A controversial issue

It has been interesting to see how this Facebook-Admiral item has divided opinion.

The active hub My Customer promptly ran an opinion piece (to which I contributed toward the end of the article). As you can see, there are strong views on both sides.

See also: How to Turbocharge a Marketing Budget  

I see the need to raise awareness about social media data being used by other companies. Too many conferences laud the potential of big data without an equal emphasis on data protection and permission-based marketing.

But I still come down on the side of giving the customer the choice.

Closing reflections

Let’s just reflect on the fact that this might be an example where a U.S. tech giant is not embracing the free market and the U.K. insurer could be the customer’s champion.

Strange times, indeed…

Let us know if there are other news stories that have grabbed your attention or on which you’d like to know the Customer Insight Leader view.

Telematics: Moving Out of the Dark Ages?

While the number of usage-based insurance (UBI) policies reached 14 million at the end of September 2016, most insurance companies are still overwhelmed by the challenge of using collected data to rate their customers’ driving habits.

This conclusion is based on analyzing the world’s 27 largest UBI programs, including those of Admiral, Allianz, Allstate, AXA, Generali, Desjardins, Direct Line, State Farm, the Hartford, Unipol, Uniqa and Zurich.

See also: Why Exactly Does Big Data Matter?  

Progressive, the No. 1 telematics insurer globally, still uses a temporary device and does not collect GPS data. Unipol, the No. 2 player, still only collects mileage data from its customers.

We believe, however, that the prehistoric age of connected insurance analytics is ending. The era was based on the premise that all policyholders are reluctant to be “tracked.” But with most of us giving daily credit card, fingerprint, driving speed or location details to companies such as Apple, BMW or Vodafone, how to make sense of the self-censorship that insurers apply to their programs?

The truth is that more data benefits insurance companies… and the careful drivers! At the center of this change is advanced data analytics – the ability to extract insights from real-time data sources and discover risk-predictive patterns.

Our analysis, detailed in the Connected Insurance Analytics report, shows that the glaciation period’s ice is melting and that all the key insurers are now moving.

See also: Data Science: Methods Matter (Part 3)  

Progressive started a vast recruitment plan to attract data scientists. Generali also made a strong move by acquiring MyDrive, an analytics provider with early footsteps in smartphone UBI. Allstate just created Arity, which will collect data on drivers and sell analytics products to third parties. Simultaneously, Unipol created Alpha, a self-standing analytics and telematics operation.

The bulk of insurance companies is yet to act. To help them adapt to this new climate, Ptolemus published the Connected Insurance Analytics (CIA) report as a step-by-step guide to advanced analytics. It describes, analyzes and illustrates the process by which advanced analytics companies take raw driving data and transform it into real-time, individual risk profiles.

Screen Shot 2016-12-06 at 9.42.20 PM

The investigation shows that acceleration, braking and mileage are the most used — unsurprisingly — but also that the range of factors is much wider and illustrates the complexity involved in selecting the correct criteria.

To offer a predictive driving score, the report demonstrates that insurers must gain a deep understanding of driving conditions. Adding contextual data, such as road type or relative speed, is a necessary step to price customers fairly.

The full article from which this is extracted is available here.