Tag Archives: Bernard Marr

5 Obstacles to Automating Operations

Insurers move cautiously when embracing automation and other tech tools, and for good reason. As technology changes the insurance industry, challenges arise that appear in few other verticals.

Nonetheless, business automation is set to change nearly every industry, Deloitte argues, and insurance companies can certainly benefit from the efficiencies that automation will introduce. Data collection, analysis and decision-making, once the sole domain of humans, can now be improved by automation and AI. These tools range from simple time-savers, like auto-completion during data entry, to complex pattern recognition and data mining, which is transforming the way we analyze risk.

Further, automation offers numerous opportunities to improve efficiency, retain customers and reduce errors — but only when automation is enacted thoughtfully, as Corrine Jones notes at Property Casualty 360. Here, we look at five obstacles to automating agency operations, plus ways to overcome them.

1. Departments Aren’t on the Same (Digital) Page

Most insurers’ systems have difficulty talking to one another. Property and casualty insurance companies tend to operate like federations: departments that fall under the same umbrella, but that operate independently most of the time. This means their customer data gets hidden away in silos, and data-driven intel therefore cannot be shared among departments.

Insurers that continue to federalize this way miss key connections that can lead to improved coverage and more satisfied customers, says Dan Reynolds, editor in chief of Risk & Insurance. Breaking down silos can be a daunting task, but the reward can be well worth the effort — and automation can help.

When data can flow across departments, machine-learning algorithms can perform analyses across departmental lines. This lets the technology spot patterns and recommend solutions more easily, says Forbes contributor Bernard Marr. With access to a single cohesive system and its data, machine learning algorithms can handle a wide range of tasks, from spotting potential fraud to providing an interactive FAQ for customers.

2. Current IT Infrastructure Might Not Support an Ambitious Implementation

McKinsey partners Tanguy Catlin, Johannes-Tobias Lorenz, and Shannon Varney stress that one of the big lessons the insurance industry learned in 2017 was that tech-driven strategies aren’t a goal in and of themselves. Rather, executives need to think about what strategies make sense in a tech-driven world.

As such, organizations must ensure the capabilities of their IT teams are keeping pace with plans to implement automation. IT cannot take on a supporting role when implementing automation technology. IT must help lead the implementation, the McKinsey partners argue, and it’s up to the organization to position IT in a leadership role.

See also: How to Solve the Data Problem  

Here is how company leaders can position IT teams to assume that role:

  • Hire tech leaders. IT teams leading changes need project leaders, agility coaches and scrum masters to guide their work.
  • Promote a tech-friendly environment. Demand for tech talent is quickly outstripping supply in many industries, and insurance companies today must establish “an environment that attracts talent, promotes personal growth and offers a desirable and interconnected work environment and flexibility.”

3. Existing Processes Might Not Scale Quickly Enough

Many tech professionals who focus on insurance solutions, like EZLynx project manager Derek Armentrout, caution insurers to “start small” when considering the switch to automation. Starting small can benefit some companies.

But a small start can derail an entire automation project when “small” isn’t combined with “scalable.” Not only must the system be able to grow into the existing insurance company structure, but it must also be able to grow with that structure as the company expands. It must handle not only additional users but also more intensive calculations, recommends Richard Seroter.

Prasad Jogalekar and Murray Woodside in the IEEE Transactions on Parallel and Distributed Systems, provide a definition of scalability that is particularly apt for insurers: “Scalability means not just the ability to operate, but to operate efficiently and with adequate quality of service, over the given range of configurations.” A system that fails customers when overloaded is not scaling adequately to meet either the insurer’s or the customers’ needs.

As Seroter notes, working with a Software as a Service (SaaS) provider is one way to ensure scalability that meets both insurer and customer demand. That means choosing a provider that understands the connection among scalable platforms, automated activities and customer experience to maximize the value of automation in customer retention.

4. Workforce Obsolescence

McKinsey principal Sylvain Johansson and senior expert Ulrike Vogelgesang predict that automation will render 25% of all insurance industry jobs obsolete by 2025. Operations were hardest hit, with a 13% predicted drop in human employees, caused largely by automating everything from report generation to answering customer queries.

That’s neither a negligible amount of job loss nor an unimaginably distant time frame,” Johansson and Vogelgesang wrote. “On the contrary, given the magnitude of these changes and the looming future, it’s important that insurers begin to rethink their priorities right now.”

Among the rethinking steps the McKinsey report recommends are:

  • Retraining existing staff,
  • Identifying imminent skills gaps and hiring to fill them, and
  • Crafting employment value propositions that reflect a tech-heavy world.

Despite McKinsey’s predictions, the insurance industry will need to retain human workers for a number of key positions, Sabah Karimi writes at Great Insurance Jobs. Digital analysts, online marketers and other tech-minded positions will still demand the human touch. McKinsey explains that some insurance jobs are relatively safe from automation for the time being: Actuaries, for instance, are unlikely to see their jobs automated in the near future.

5. Existing Interfaces That Fail to Attract, Inspire and Retain Customers

Customer loyalty to their property and casualty insurer is a unique relationship. Because customers rarely interact with their insurers except in a crisis, building a relationship over time poses particular challenges.

See also: 3 Keys to Success for Automation  

Raising the difficulty level is the fact that today’s customers expect their product and service purchases to be easier than ever before. Web-based business has created an expectation of a seamless omni-channel experience and instantaneous results.

How can automation help?

  • Improving self-service. Increasingly, customers who use the Internet to contact businesses do so with the expectation of self-service, Steve Wiser writes in an article at P&C 360. Automated systems streamline the collection of customer data. When incorporated with machine learning, they can automatically recommend the best additional coverage or next steps for the user.
  • Better analytics. In the age of big data, Wiser notes, insurance companies that don’t gather and analyze customer information are missing an extraordinary opportunity — not only to manage their own risk, but to better connect with customers, as well. A personalized customer experience boosts customer ownership, and it’s a process that can be automated with the right tools.
  • Improved ownership by packaging product lines. When Allstate first tried to switch to a commercial offering, the company found itself stalled by agents who needed to search out information before offering recommendations to customers — and a system that turned this process into a major stall, Kumba Senaar says. An automated system responded to these information requests more quickly, intuited what agents would need next and recommended additional coverages based on available data.

The result? Happier customers, larger purchases and more efficient agents. A win-win(-win) for Allstate.

The insurance industry has a long history of reclassifying “obstacles” as “opportunities.” When insurers partner with SaaS providers, they gain an ally that understands the connections between these major challenges and that can implement systems that address multiple challenges simultaneously.

Big Data in Insurance: A Glimpse Into 2015

Bernard Marr is one of the big voices to pay attention to on the subject of big data. His recent piece “Big Data: The Predictions for 2015” is bold and thought-provoking. As a P&C actuary, I tend to look at everything through my insurance-colored glasses. So, of course, I immediately started thinking about the impact on insurance if Marr’s predictions come to pass this year.

As I share my thoughts below, be aware that the section headers are taken from his article; the rest of the content are my thoughts and interpretations of the impact to the insurance industry.

The value of the big data economy will reach $125 billion

That’s a really big number, Mr. Marr. I think I know how to answer my son the next time he comes to me looking for advice on a college major.

But what does this huge number mean for insurance? There’s a potential time bomb here for commercial lines because this $125 billion means we’re going to see new commerce (and new risks) that are not currently reflected in loss history – and therefore not reflected in rates.

Maybe premiums will go up as exposures increase with the new commerce – but that raises a new question: What’s the right exposure base for aggregating and analyzing big data? Is it revenue? Data observation count? Megaflops? We don’t know the answer to this yet. Unfortunately, it’s not until we start seeing losses that we’ll know for sure.

The Internet of Things will go mainstream

We already have some limited integration of “the Internet of Things” into our insurance world. Witness UBI (usage-based insurance), which can tie auto insurance premiums to not only miles driven, but also driving quality.

Google’s Nest thermostat keeps track of when you’re home and away, whether you’re heating or cooling, and communicates this information back to a data store. Could that data be used in more accurate pricing of homeowners insurance? If so, it would be like UBI for the house.

The Internet of Things can extend to healthcare and medical insurance, as well. We already have health plans offering a discount for attending the gym 12 times a month. We all have “a friend” who sometimes checks in at the gym to meet the quota and get the discount. With the proliferation of worn biometric devices (FitBit, Nike Fuel and so on), it would be trivial for the carrier to offer a UBI discount based on the quantity and quality of the workout. Of course, the insurer would need to get the policyholder’s permission to use that data, but, if the discount is big enough, we’ll buy it.

Machines will get better at making decisions

As I talk with carriers about predictive analytics, this concept is one of the most disruptive to underwriters and actuaries. There is a fundamental worry that the model is going to replace them.

Machines are getting better at making decisions, but within most of insurance, and certainly within commercial lines, the machines should be seen as an enabling technology that helps the underwriter to make better decisions, or the actuary to make more accurate rates. Expert systems can do well on risks that fit neatly into a standard underwriting box, but anything outside of that box is going to need some human intervention.

Textual analysis will become more widely used

A recurring theme I hear in talking to carriers is a desire to do claims analysis, fraud detection or claims triage using analysis of text in the claims adjusters’ files. There are early adopters in the industry doing this, and there have emerged several consultants and vendors offering bespoke solutions. I think that 2015 could be the year that we see some standardized, off-the-shelf solutions emerge that offer predictive analytics using textual analysis.

Data visualization tools will dominate the market

This is spot-on in insurance, too. Data visualization and exploration tools are emerging quickly in the insurance space. The lines between “reporting tool” and “data analysis tool” are blurring. Companies are realizing that they can combine key performance indicators (KPIs) and metrics from multiple data streams into single dashboard views. This leads to insights that were never before possible using single-dimension, standard reporting.

There is so much data present in so many dimensions that it no longer makes sense to look at a fixed set of static exhibits when managing insurance operations. Good performance metrics don’t necessarily lead to answers, but instead to better questions – and answering these new questions demands a dynamic data visualization environment.

Matt Mosher, senior vice president of rating services at A.M. Best, will be talking to this point in March at the Valen Analytics Summit and exploring how companies embracing analytics are finding ways to leverage their data-driven approach across the entire enterprise. This ultimately leads to significant benefits for these firms, both in portfolio profitability and in overall financial strength.

There will be a big scare over privacy

Here we are back in the realm of new risks again. P&C underwriters have long been aware of “cyber” risks and control these through specialized forms and policy exclusions.

With big data, however, comes new levels of risk. What happens, for example, when the insurance company knows something about the policyholder that the policyholder hasn’t revealed? (As a thought experiment, imagine what Google knows of your political affiliations or marital status, even though you’ve probably never formally given Google this information.) If the insurance company uses that information in underwriting or pricing, does this raise privacy issues?

Companies and organizations will struggle to find data talent

If this is a huge issue for big data, in general, then it’s a really, really big deal for insurance.

I can understand that college freshmen aren’t necessarily dreaming of a career as a “data analyst” when they graduate. So now put “insurance data analyst” up as a career choice, and we’re even lower on the list. If we’re going to attract the right data talent in the coming decade, the insurance industry has to do something to make this stuff look sexy, starting right now.

Big data will provide the key to the mysteries of the universe

Now, it seems, Mr. Marr has the upper hand. For the life of me, I can’t figure out how to spin prognostication about the Large Hadron Collider into an insurance angle. Well played.

Those of us in the insurance industry have long joked that this industry is one of the last to adopt new methods and technology. I feel we’ve continued the trend with big data and predictive analytics – at least, we certainly weren’t the first to the party. However, there was a tremendous amount of movement in 2013, and again in 2014. Insurance is ready for big data. And just in time, because I agree with Mr. Marr – 2015 is going to be a big year.