Tag Archives: natural language processing

Sentiment Analytics Can Drive Growth

Insurers have a near-constant stream of unstructured data at their disposal that can be used to drive growth by improving policyholder retention and identifying cross-sell and upsell opportunities. One of the challenges for insurers is sorting through this mountain of unstructured data quickly to gain an accurate understanding of the sentiment of their customers in real time.

The last few years have seen sentiment analytics become a critical component of customer feedback strategies for companies of all sizes. Sentiment analytics uses a combination of natural language processing (NLP), machine learning (ML) and deep text analytics to bring out the nuances hidden in the text.

Sentiment is easier to translate and analyze than it is to express. Sentiment analytics, also referred to as opinion mining, is a technique to abstract the underlying sentiment from textual data. Usually, this customer feedback is unstructured data flowing in from multiple channels, such as:

  • Voice messages
  • Claims
  • Adjuster notes
  • Medical records
  • DMV reports
  • Surveys
  • Underwriter notes
  • Email
  • Call center logs
  • Social media posts

The idea is to understand not only the nature of the feedback but also to derive context out of it. However, sentiments are complicated. Analyzing sentiments, even more so.

Domain-Specific Sentiment Analytics

The complexity of spoken language makes it difficult if not impossible to derive sentiment accurately every time. Teaching a machine to understand such things as tonality, cultural lingo and slang, or the ability to discount grammatical errors, and comprehend rhetoric such as irony or sarcasm are all difficult at present. Existing sentiment analytics tools are not equipped to identify the true sentiment of these types of dialogues.

Although sarcasm is a problematic form of language to detect, there are other complex statements that machines are learning to comprehend. Consider the following statement: “Rocketz Insurance Company has always offered me great pricing, but at times I have not been happy with their response time for questions about my policy.” This sentence has a negative as well as a positive connotation, and sentiment analytics come into play. The first part of the sentence can be identified as a positive feeling, and the other half is identified as negative.

See also: 3-Step Approach to Big Data Analytics  

Sentiment analytics specific to industry lines play a key role. The accuracy of identifying the sentiment of data can be increased by training the system (machine learning) on a specific domain, such as the insurance industry. For example, the terms “garaging” and “towing” have a greater meaning in the insurance industry as opposed to, for example, manufacturing. Therefore, if a client makes a comment about either, it would have more meaning for insurance than other industries.

Driving Growth

In the insurance industry, sentiment analytics can be used in a multitude of ways that directly affect business, such as:

  1. Improving retention rates
  2. Identifying cross-sell/upsell opportunities
  3. Identifying trends

Improving Retention Rates

Having the ability to quickly and easily identify the sentiment of policyholders whose auto policy will renew in 60 days or less is a good example. Let’s say we have 1,000 auto policies that are up for renewal by the end of 2019, and the priority for the renewal team is to contact policyholders who are “detractors.” The sentiment of their conversations and interactions, regardless of the channel, has a low score. The challenge for the renewal team is: Who do they contact first? Are all “detractors” equal in their dissatisfaction with their auto policy? And what may be the root cause or causes of their dissatisfaction?

The renewal team can target these policyholders with a strategy to retain these policyholders with insight on why the policyholder may be unhappy before contacting them. Maybe it was a bad claim experience, or they are unhappy with their premium and started shopping elsewhere. It’s not enough to simply understand who a “detractor” is; you need to understand why.

Identifying Cross-sell/Upsell Opportunities

Using the example of our 1,000 auto policies that are up for renewal, what about the policyholders who are “promoters” and happy with their auto coverage? This is an ideal time for the renewal team to contact these policyholders and thank them for their business as a minimum. But is there a cross-sell or upsell opportunity here? For the renewal team focusing on this segment of policyholders, it would be helpful to have some idea why their sentiment is high before contacting them with a potential offer.

Identifying Trends

In addition to identifying immediate opportunities that can be acted on, sentiment analytics can help insurers understand trends such as:

  1. The sudden demand for a product type
  2. The like or dislike of a specific customer experience

Many homeowners are considering cyber insurance to protect themselves from identity theft and invasion of privacy. Sentiment analytics can provide insurers who currently do not offer cyber insurance a heads-up that maybe they should consider offering cyber insurance as a cross-sell/upsell opportunity.

See also: Predictive Analytics: Now You See It….  

Everyone wants to identify and correct bad customer experiences, especially a bad claims experience. What about good customer experiences? Going back to our auto policy example, something as simple as having the ability to easily obtain an auto insurance card online, or easily reach a customer service representative. can be a positive customer experience that sales and marketing may want to promote.

Conclusion

Sentiment analytics can help insurers sort through a continuous stream of unstructured data to identify opportunities for increasing revenue and identifying trends. Currently, sentiment analytics is not perfect, but focusing on a specific domain such as the insurance industry will increase accuracy.

Sentiment analytics can be a powerful tool if leveraged starting at the earliest touch point, even if it begins with a small set of customers.

Insurtech: Revolution, Evolution or Hype?

Artificial Intelligence (AI), machine learning, the Internet of Things (IoT), blockchain, robotics, quantum computing — the terminology of technology is staggering enough, let alone understanding what it is and how to use it. While some of the advances in technology are more noise than anything useful, many of these developments can be quite valuable for our industry — especially in claims management and risk control.

Fortunately, there are experts with a good understanding of insurtech and how we can make it meaningful for our companies and our injured workers. Three of them helped us break down the latest technological developments and provided insights into how they can benefit the workers’ compensation system during our most recent Out Front Ideas webinar:

  • Guy Fraker, chief innovation officer for Insurance Thought Leadership
  • Jason Landrum, global chief information officer for Sedgwick Claims Management Services
  • Peter Miller, CPCU, president and CEO of The Institutes/Risk and Insurance Knowledge Group.

What Is Insurtech?

Simply put, insurtech is using technology to improve efficiencies and provide a better customer experience in the insurance industry. Many startup companies have entered this space in the last few years, although the majority are not as helpful as they may first appear. Companies come out with apps or, as our speakers said, shiny new toys that drive user experience and seem really cool but do not add value. One speaker said many companies offer solutions to problems that do not exist.

The more mature, robust companies — those with an actual product that covers the life cycle of the value chain or a significant gap in it — are in the minority but have the most potential to make a difference. They offer strategic and comprehensive solutions. There are not too many organizations with this capability, so there are tremendous opportunities.

The right technology, when properly deployed, has the capability of making a significant impact. Two of the most meaningful advancements for our industry are machine learning and AI. So what are these terms and how do they differ?

Machine learning is actually an application of AI. Where AI is basically computer-based logic, machine learning uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. From the data, it defines a formula to predict an outcome, thereby making it meaningful.

See also: Insurtech Ecosystem: Who Will Eat Whom?  

Some TPAs and carriers are using this technology to quickly flag claims that could be in danger of adverse development. The computer takes a claim, runs it against data on other claims and can determine if it is likely to become severe. As soon as the machine learning model detects something different about a claim — something a human would not be able to identify as a potentially huge loss — it alerts the claims manager to intervene and manage it more carefully. The effect is to drive the outcomes of claims in a more positive way.

For injured workers, technology is being leveraged to provide apps that provide easy access to claim information. Injured workers can find out where they are in each step of the process without having to call the adjuster.

In the consumer market, AI and machine learning are being used to apply natural language processing and determine what the person is actually saying or asking a computer. Think Alexa or Google Home. Our speakers predict it will soon become commonplace for humans to interact easily with machines.

Implementing this technology may seem overwhelming to organizations, especially if they try to adopt it on a large-scale basis. Instead, companies should have a narrowly defined plan and seek real solutions.

Industry Initiative and Blockchain

One of the exciting potential uses of newer technology in our industry is something called the RiskBlock Alliance. This not-for-profit industry consortium is meant to provide a framework that the industry owns: a standardized way of looking at data. It is based on three technologies:

  1. The Internet of Things
  2. Blockchain
  3. Data analytics

The confluence of these technologies is profound. In a nutshell, the IoT is the network of electronic devices that can digitally capture and exchange data. Blockchain enables the storage of this data along with rule sets. It can execute automated instructions based on the data and the rules applied to it. It also allows for data sharing among organizations in a secure way. A couple of examples demonstrate the significant savings and benefits to the industry:

  1. Proof of coverage. For example, a short-haul trucking company must provide proof of insurance for every load each driver takes; approximately eight hauls per day, per driver. It adds up to about 200,000 times each day that proof of coverage must be executed. There are different insurers involved with each load. It takes the company about 30 minutes to get a proof of insurance for each load. However, using the sharable platform of blockchain means the proof of insurance per load can be available in a matter of milliseconds.
  2. Sharing policy information when subrogation comes into the equation. Say there is an auto accident between two vehicles, each with a different insurance carrier. Initially, both insurers start paying the insureds while they sort through the details to see who is at fault. Once fault is established, payment between the two carriers must be settled. Right now, this is done manually at an estimated annual cost of $300 million to the industry. Using blockchain, the policy information could be housed in a secure environment and the settlement done instantly. Putting policy information in an automated process on an aggregate basis could save tremendous amounts of money and time.

Challenges and Opportunities

While there are some challenges in implementing new technologies, there are also many opportunities. Many of the more rote tasks of handling claims can be done faster by technology, freeing claims managers to provide the human touch that is so necessary in so many workers’ compensation claims. Spending more time with injured workers, showing them concern and empathy, results in better outcomes for them and lower costs for payers.

One challenge is the need for data standardization, something RiskBlock is targeting. This could level the playing field and provide opportunities for smaller insurers to grow more quickly.
Incorporating aspects of insurtech into the daily workflow can be challenging, especially because there are so many innovations and ideas at play. It is important to try to harness that enthusiasm and apply it to a framework that captures the best ideas and develops them into solutions.

Another potential challenge is that our industry is so heavily regulated, and regulated differently in each jurisdiction. That means that some insurtech solutions may work in one area but not another. Caution is required before jumping on something that may not be workable.

One challenge that can be easily overcome is changing the mindset that implementing new technology requires many people. It does not. Moving into the insurtech space is best done in a constrained way, with just two or three people involved. As one speaker said, “It’s not about thinking outside the box. It’s about building the box.” Every game-changing organization like Microsoft and Facebook started with a team of just three or four people.

See also: Key Challenges on AI, Machine Learning  

From a healthcare standpoint, one of the best opportunities from insurtech is the ability to get in front of pain, which can also be referred to as pre-pain or pre-hab. As healthcare technology advances, we will be able to help workers and their families understand what to expect in terms of pain before they undergo surgery, for example. We can help them be better prepared, facilitating better and shorter recoveries.

The Future

With the maturation of insurtech companies, our experts expect the number of startups will slow in the next couple of years. Instead, existing companies will return with innovations.

The tremendous amount of data available in the future will help level the playing field between larger and smaller carriers. This is because the smaller carriers will be able to participate in data sharing initiatives to have access to analytics way beyond what their own data could provide. Data aggregation insurtech companies are going directly to the source for data, such as partnering with auto manufacturers to access data from their onboard computer systems.

Insurtech will also allow pharmacists to match DNA to prescriptions to determine if they are feasible. Also, robotics can be used to handle riskier or repetitive tasks. Rather than replacing workers, the technology allows them to engage in more meaningful responsibilities. Using AI to process routine, medical-only claims may even result in eliminating some steps. We may find straight-through processing can be done quickly and efficiently.

One of the most exciting uses of new technology is to eliminate losses by removing risks. Insurtech can be used to detect when and how certain actions will likely lead to injuries, allowing humans to set up systems to prevent those conditions. The ability to avoid losses would truly transform our industry.

How to Use AI in Customer Service

How to manage the increase in incoming unstructured information is a key challenge in the insurance industry—we explore how Accenture’s Machine Learning Text Analyzer can achieve this using historical data.

How do you approach customer service and policy administration within your organization? In this blog post, I’ll demonstrate how artificial intelligence (AI) and a raised AIQ can help you get the most out of your data. (For the other articles in this series, click here.) To do this, I’ll discuss how insurers can use machine learning to analyze texts.

How can insurers use AI in customer service and policy administration?

The customer service and policy administration workforce can make their lives easier by using AI to:

  • Understand and act on external emails and requests.
  • Automate call center and webchat services—helping companies get on with more intricate work.
  • Enable self-service queries on policy issuance, endorsements, cancellations and renewals—using virtual assistants, for example.
  • Process unstructured data, which means fewer mistakes and better customer service.

How does AI improve customer services and policy administration?

AI enables more efficient administration processes. Insurance executives plan to invest in seven AI-related technologies in the next three years. They are: 

  • Machine learning; 
  • Deep learning; 
  • Natural language processing; 
  • Video analytics; 
  • Embedded AI solutions; 
  • Robotic process automation; 
  • Computer vision. 

See also: Policy Administration: Ripe for Modernizing  

In addition to increasing the efficiency of administration processes and enhancing analytical insights, AI technologies also benefit customer services through:

As I will show in the use case below, the customer service and policy administration workforce can use machine learning to process information faster and with greater accuracy.

Use case: Machine Learning Text Analyzer (MALTA)

Insurers today must figure out how to manage the exponential increase in incoming unstructured data. Eighty percent of data generated is unstructured, and the volume continues to grow exponentially. Forty percent of business executives complain that they have too much unstructured text data and don’t know how to interpret it.

Insurers face three main challenges:

1. Too much unstructured information

  • A large amount of information comes in through a variety of channels;
  • Incoming data is structured as well as unstructured;
  • Much of the workforce is occupied with processing unstructured information;
  • A large amount of unstructured information exists within the organization.

2. Too many communication channels

Customers use a large variety of channels to communicate with their insurance company, such as e-mail, contact forms, the service desk (e.g. ticketing), letters and applications.

3. The information is not linked to business processes

  • Workers lose a lot of time when they have to identify received information and allocate requests to the right channels;
  • They also lose time owing to inefficient processes caused by breaks in the system;
  • This prolongs the response time to clients;
  • Humans are prone to errors, which creep in at all points.

Solution: Machine Learning Text Analyzer (MALTA)

Now, insurers can automate the analysis and classification of incoming text by applying machine learning and using historical data.

How does MALTA work in customer service and policy administration?

MALTA can analyze any incoming documents, for example when customers send their policy documents via email.

These documents can be analyzed and classified using natural language processing methods and machine learning algorithms. MALTA is also trained with historical data, which enables it to classify, understand and extract information.

In the next step, MALTA links your customer’s policy document to business processes, prompting different functions to take action. Depending on the business and architecture set-up, MALTA or the output of the API triggers a process chain, a robot or an agent so that the necessary processing steps can be executed.

See also: In Age of Disruption, What Is Insurance?  

Benefits of MALTA

MALTA is flexible, customizable, independent, multilingual, state-of-the-art and end-to-end; using Accenture’s machine learning text analyzer, insurers can:

  • Increase classification accuracy and efficiency, and reduce errors.
  • Create individual learning models based on training data.
  • Deploy the solution on-premise, not only in the cloud.
  • Automate repetitive tasks, allowing employees to focus on more complex work.
  • Categorize new requests immediately and send them to the relevant departments.
  • Use state-of-the-art models and tools.
  • Work on a platform-independent web service.
  • Carry out classification outside regular business hours.
  • Cleanse data and extract and evaluate features.
  • Link robotics and process automation tools to classification.
  • Set up and train employees with minimal effort.

In addition to customer services and policy administration, insurers can use MALTA across other parts of the enterprise, for example:

Are you ready to power up your business with AI? Download the report on How to boost your AIQ for more insight.

Emerging Technology in Personal Lines

Personal lines insurers are investigating emerging technologies and developing strategies and plans related to individual new technologies. Technology is advancing so rapidly that it is even difficult to define what should be considered an emerging technology. For the past several years, SMA has been tracking 13 technologies that many consider to be emerging. These include technologies such as autonomous vehicles, AI, wearables and the Internet of Things. In our recent research, five of these technologies have emerged as “power players” for personal lines insurers, based on the level of insurer activity and the potential for transformation. The specific plans by insurers for these and other technologies are detailed in the SMA report, Emerging Tech in Personal Lines: Broad Implications, Significant Activity.

See also: 2018’s Top Projects in Personal Lines  

Some big themes for emerging tech in personal lines stand out:

  • Artificial Intelligence dominates. AI is often a misunderstood and misused term. However, when specific technologies that are part of the AI family are evaluated, much activity is underway – by insurers, insurtech startups and mature tech vendors. Chatbots, robotic process automation (RPA), machine learning, natural language processing (NLP) and others are the subjects of many strategies, pilots and implementations.
  • The Autonomous Vehicle frenzy is cooling.There is still an acute awareness of the potential of autonomous vehicles to dramatically alter the private passenger auto insurance market. But there is also the realization that, despite the hype, the transition is likely to be a long one, and the big implications for insurers are probably 10 or more years out.
  • The IoT is going mainstream. Discussions continue about the transformational potential of the IoT for all lines of business. But rather than just talking about the possibilities, there is now a great deal of partnering, piloting and live implementation underway. We are still in the early stages of incorporating the IoT into strategies and insurance products and services, but their use is becoming more widespread every day.
  • UI Options are dramatically expanding. The many new ways to interact with prospects, policyholders, agents, claimants and others should now be considered in omni-channel plans. Messaging platforms, voice, chatbots and more are becoming preferred ways to communicate for certain customer segments.

See also: Insurtech and Personal Lines  

Certainly, other trends and much emerging tech activity are happening outside these main themes. Wearables, new payment technologies, drones, blockchain and other technologies are being incorporated into strategies, pilots and investment plans. The next few years promise to be quite exciting as advancing technologies spark more innovation in the industry.

3 Steps to Demystify Artificial Intelligence

Artificial intelligence is the new electricity. We hear it will fundamentally shift the balance of power between labor and capital, mostly by rendering labor obsolete. It will enable and empower transformative technologies that will rearrange the sociopolitical landscape and may lead to humanity’s transcendence (or extinction) within our lifetimes. As it changes the world, it will necessarily rewrite the rules of insurance. That’s the myth, and the nature of the headlines.

Interestingly, insurance is heavy on intellectual property (think of proprietary underwriting models), technology and data. And AI is hungry; hungry for data, of course, but also hungry for systems that can be automated and for proprietary classification problems that can be improved. That places insurance right in the appetite of artificial intelligence and its promise of transformation. If we want to act on artificial intelligence’s transformational potential,  we need to understand what it actually is, separate the technologies from the hype and develop a practical understanding of what is required to implement AI-powered solutions in the insurance sector. This article will highlight these three steps and offers a realistic approach for carriers to take advantage of the opportunities.

Defining Artificial Intelligence

Unfortunately, our first step is also our hardest, as a working definition of artificial intelligence is difficult. The scope of the term AI is broad, and it requires careful consideration to avoid becoming hopelessly confounded with its own hype. It is also challenging to come to a clear definition of natural intelligence, which leaves us struggling for a definition of artificial intelligence because the latter is so often compared to the former.

AI tends to be discussed in two flavors. The first is general artificial intelligence (also, artificial general intelligence and strong AI). GAI is machinery capable of human-level cognition, including a general problem-solving capability that is potentially self-directed and broadly applicable to many kinds of problems. GAI references are accessible through fictional works, such as C-3PO in Star Wars or Disney’s eponymous WALL-E. The most important feature of GAI is that it does not currently exist, and there is deep debate about its potential to ever exist.

The second is usually referred to as narrow AI. Narrow AI is task-specific and non-generalizable. Examples include facial recognition on Apple’s iPhone X and speech-to-text transliteration by Amazon’s Alexa. Narrow  AI looks and feels a lot like software or, perhaps, predictive models. Narrow AI can be described as a class of modeling techniques that fall under the category of machine learning.

See also: Seriously? Artificial Intelligence?  

What is machine learning? Imagine a set of input data; this data has one or more potential features of interest. Machine learning is a technique for mapping the features of input data to a useful output. It is characterized by statistical inference, as advanced techniques often underlie machine learning predictive models. Through statistical modeling, software can infer a likely output given a set of input features. The predictive accuracy of machine learning methods increase as their training data sets increase in size. As the machine ingests more data, it is said to learn from that data. Hence, machine learning.

Perhaps most important of all, machine learning (as an implementation of narrow AI) is real and here today; for the remainder of our discussion when we say “AI,” we mean narrow AI or machine learning.

Beyond the Hype

The hype around AI and its potential is extensive. Silicon Valley billionaires opine on the potential implications of the technology, including comparing its power to nuclear weapons. Articles endlessly debate if and how quickly AI will structurally unemploy vast swaths of white collar workers. MIT’s Technology Review provides a nice summary of the literature, stating that up to half of all jobs worldwide could be eliminated in the next few decades.

AI may well have this kind of impact. And the social, political and economic implications of that impact, especially around questions of potential large-scale unemployment, deserve careful long-term consideration. However, executives and business owners need to evaluate technology investments today to improve their current competitive position. From that perspective, we find it more practical to focus on examining which existing tasks could be automated by AI today.

Enter Pigeons

In 2012, researchers trained pigeons to recognize people based only on their faces as part of a study on cognition. Suppose you had millions of face-recognizing pigeons; this force of labor could be deployed in a comprehensive facial recognition system — a system remarkably similar in function to the facial recognition AI of devices like modern smart phones. It turns out pigeons have also been trained to recognize voicesspot cancers on X-rays and count, among a host of other tasks related to headline-grabbing AI achievements.

The metaphor is admittedly silly. Instead of pigeons, imagine an army of virtual robots capable of classifying information from the real world to produce a machine-readable data set. In machine learning language, these robots take unstructured data and make it structured. Said robots resemble the automation machinery of a factory; like spot welders tirelessly joining steel members to form automobile frames, our virtual robots tirelessly recognize if a face is featured in a photograph. In contemplating the question, what could be automated with AI, a useful starting place is the army of robots (or pigeons!). For example:

  • What existing analyses could be improved or optimized? Could pricing or underwriting be improved using better classifiers or non-linear modeling approaches?
  • What data currently exist at the firm that could be made available for new types of analysis? Claims adjusters’ notes can be processed by natural language algorithms and cross-referenced with photos of physical damage or prior inspections.
  • What data would you analyze if it could be made available? What if you could listen to all the policyholder calls received by your customer service department and annotate which questions stumped the customer service representatives? Or which responses lead to irritation in the policyholders’ voices?

Bringing AI to Insurance

What is an insurer to do? Start by not fretting. We propose two considerations to facilitate a sleep-at-night perspective. First, insurers are already good at AI or its precursor technologies. The applicability of AI in the present and near future is entirely based on narrow AI technologies. For example, natural language processing and image recognition are both machine learning implementations with working business applications right now. Both use predictive models to achieve results. The software may be artificial neural networks trained on vast data sets, but they are nonetheless conceptually compatible with things insurance carriers have used for years, like actuarial pricing models. The point is that the application of AI is an incremental step forward in the types of models and data already applied in the business.

Second, sorting through the hype requires a staple of good business decision making: the risk-cost-benefit analysis. Determining which technologies are worth investment is within scope for decision makers that otherwise know how to make selective investments in growing the capabilities of their firm. The problems faced by a carrier are much bigger than sorting out AI if management lacks the basic skillset for making business investments.

Providing an inventory of every application of AI is beyond the scope of this article. DeepIndex provides a list of 405 at deepindex.org, from playing the Atari 2600 to spotting forged artworks. Instead, suppose that AI, like electricity, will be broadly applicable across industries and functions, including the components of the insurance value chain from distribution to pricing and underwriting to claims. The goal is to identify and implement the AI-empowered solutions that will further a competitive advantage. Our view is that carriers’ success with AI requires three key ingredients: data, infrastructure and talent.

Data: AI might be considered the key that unlocks the door of big data. Many of the modeling techniques that fall under the AI umbrella are classification algorithms that are data hungry. Unlocking the power of these methods requires sufficient volume of training data. Data takes several forms. First, there are third party data sources that are considered external to the insurance industry. Aerial imagery (and the processing thereof) to determine building characteristics or estimate post-catastrophe claims potential are easy examples. Same with the vast quantities of behavioral data built on the interactions of users with digital platforms like social media and web search. Closer to home, insurance has long been an industry of data, and carriers are presumed to have meaningful datasets in claims, applications and marketing, among others.

Infrastructure: Accessing the data to feed the AI requires a working infrastructure. How successfully can you ingest external data sources? How disparate and unstructured can those sources be? Cloud computing is not necessarily a prerequisite to successful AI, but access to vast, scalable infrastructure is enabling. Are your information systems equipped, including security vetting, to do modeling in the cloud? Can you extract your internal data into forms that are ready to be processed using advanced modeling techniques? Or are you running siloed legacy systems that prevent using your proprietary data in novel ways?

Talent: Add data science to the list of AI-related buzzwords. We claimed earlier that many of the advancements attributed to narrow AI are predictive models conceptually like modeling techniques already used in the insurance industry. However, the fact that your pricing actuary conceptually appreciates an artificial neural net built for fraud detection using behavioral data does not mean you have the in-house expertise to build such a model. Investments in recruiting, training and retaining the right talent will provide two clear benefits. The first benefit is being better equipped to do the risk-cost-benefit analysis of which data and methods to explore. The second is having the ability to test and, ultimately, implement.

See also: 4 Ways Connectivity Is Revolutionary  

In Aon’s 2017 Global Insurance Market Outlook we explored the idea of the third wave of innovation as propounded by Steve Case, founder of AOL, in his book, “The Third Wave: An Entrepreneur’s Vision of the Future.” The upshot of the third wave for insurers was that partnership with technology innovators, rather than disruption by them, would be the norm. This approach applies now more than ever as technological innovators continue to unlock the potential of AI. If you don’t have the data, or the infrastructure, or the talent to bring the newest technologies to bear, you can partner with someone that does. Artificial intelligence is real. While the definitions are somewhat vague – is it software, predictive models, neural nets or machine learning – and the hype can be difficult to look past, the impacts are already being felt in the form of chatbots, image processing and behavioral prediction algorithms, among many others. The carriers that can best take advantage of the opportunities will be those that have a pragmatic ability to evaluate tangible AI solutions that are incremental to existing parts of their value chain.

If you don’t have an AI strategy, you are going to die in the world that’s coming.” Devin Wenig CEO, eBay

Maybe true, but that does not make it daunting. The core of insurance is this: Hire the right people, give them the infrastructure they need to evaluate risk better than the competition and curate the necessary data to feed the classification models they build. AI hasn’t, and won’t, change that.