Tag Archives: artificial intelligence

Finally, Fewer Meetings?

As the jury got set to begin deliberations in the trial of Derek Chauvin this week, the judge told them that they would have to rely on their notes from the three-week trial because no transcription of the testimony would be available. Someone tweeted that the lack of a transcript made perfect sense — if the year were 1821.

He has a point. Natural language processing has advanced so much that using artificial intelligence to produce an instant, highly accurate transcription has become trivial, The advancement has broad implications, ranging from the number of meetings that we’re all subjected to, to the interactions that agents and customer service reps have with clients.

Let’s start with meetings. I hate most meetings. I was so disappointed to realize years ago that someone had written a book called “Death by Meeting,” because I considered writing a book with that exact title.

But meetings are hard to stamp out. When a colleague and I facilitated a long-term strategy session for a major medical organization a few years ago, we asked for a list of things that the top team wanted to stop doing. Almost everyone said the organization held far too many meetings — and recommended a long series of meetings to tackle the problem. (The CEO had quite the chuckle.)

Automatic transcriptions provide at least part of a solution. Many meetings are held just to distribute information — even though the orthodoxy is that meetings should focus on making decisions. But transcriptions make distributing information trivial. Those not attending a meeting don’t even have to watch or listen to a recording; they can just skim the transcript in a tiny fraction of the time. If they want to catch all the nuance, or if the transcript seems unclear, they can click on the links that the AI provides to that part of the recording and listen to or watch the actual session. (This article in Wired provides some detail on the advances. My favorite service is Otter.ai.)

No one has to take notes at a meeting any longer. Those wanting to forward details to their staffs can just highlight the important points, or, if some information or interactions are considered to be confidential, can simply cut and paste what they want to share from the transcript.

Many people who now feel their time is sucked away via meetings will be able to avoid them while not missing out on any of the information. The problem won’t entirely go away, because many want to be included in meetings even if they don’t want to actually attend — status depends on being invited to certain meetings. But at least there’s now more flexibility, and smart organizations will find ways to use the advances in transcription to free valuable time for executives and staff.

The capability should be cranked into the deliberations that companies are going through now as they consider how much work-from-home will be part of their future. We’ve all learned how to “attend” virtual meetings without being 100% present — I’ve pretty much lived in gym shorts for 14 months now. Knowing that meetings can be automatically transcribed gives us a backstop so we can be productive during the times when our full attention isn’t needed. Smart companies will note that the definition of a meeting has become fluid and will plan accordingly.

AI-based transcription won’t just reduce the number attending meetings at senior levels of insurers but will also affect how agents and customer reps interact with clients. While it’s long been easy to record conversations, the interactions are now searchable because they can be turned into text. That makes a huge difference.

I remember a colleague at the Wall Street Journal’s nascent TV operation cataloguing video back in the early 1990s, trying to document all the key words and topics that might make the video of use years later. He was a bright fellow who went on to be CEO of a multibillion-dollar communications company, but he soon found that the work took too much time and yielded too little return. Today, he could search transcripts in seconds to see who was interviewed, what the person said, etc.

This searchability will increase accountability for anyone offering advice. That’s not a huge issue at the moment, because it’s largely those giving advice who are recording calls “for training purposes,” but clients will increasingly record calls, too, and will have text that they can easily search and present as evidence if they feel they’ve been ill-used. Look at how seemingly every public interaction is caught on camera these days — and recording audio is even simpler.

At the same time, agents and insurers are increasingly able to search transcripts to see what is puzzling clients, so they can smooth out kinks in the customer experience. Agents and insurers can also see what bigger issues might need to be addressed.

We don’t live in 1821. It’s 2021, and, after a year of COVID, we deserve all the breaks we can get.

I’ll start us off by skipping four meetings this week.

Stay safe.

Paul

P.S. Here are the six articles I’d like to highlight from the past week:

How Insurers Can Step Up on Climate Change

With the coming UN conference on climate change, the insurance industry has a historic opportunity to take a seat at the main table.

Solving Life Insurance Coverage Gap

We are now seeing the fruits of our labors materialized into a genuine straight-through process for term life.

1 Million Digital Life Presentations

The life insurance presentations provide five key takeaways: In sum, millennials demand a more visual, interactive and intuitive approach.

How AI Is Moving Distribution Forward

AI improves risk analysis and fraud detection while providing more sophisticated pricing and faster, more personalized customer services.

Long-Term Disability in the Time of COVID-19

There are many “pandemic headwinds” facing group LTD carriers, and it’s just a matter of time before these trends crystallize.

Sea Changes After a Year of Pandemic

Business as usual is likely to take on a different meaning, a reality compounded by the ever-shortening useful life of technology.

How AI Is Moving Distribution Forward

While artificial intelligence can improve almost all of the insurance value chain, most insurers are still not leveraging AI at its full capacity.

Adopting AI and implementing hyper-automated systems can help insurance distribution, in particular, become more efficient, accurate and secure — a benefit that both companies and the end consumer will see. From improving risk analysis and fraud detection to providing more sophisticated pricing and better customer insights for faster, more personalized customer advice and services, there are many ways in which AI helps move insurance distribution forward.

Improving operational efficiency

With today’s low interest rates, insurers can no longer depend on the financial earnings of their assets and need to find new margins in their operating models. They have more pressure to increase revenues while cutting overall operational costs. 

Deploying AI to everyday back-office processes can reduce the number of manual tasks insurers face, freeing them to spend more time on tasks that support their bottom line. Insurers are able to get more done in less time and often with improved accuracy.

Enhancing insurance distribution

According to McKinsey, 80% of value driven by advanced AI in insurance will come from marketing and sales alone (versus only 10% from better risk management and 3% from gains in operational efficiency). Today, insurers face several distribution challenges — moving from physical to remote and hybrid sales networks, learning how to strike the balance between technology and human sales, adjusting multi-channel and omnichannel sales, etc.

AI can enable a more fluid and personalized experience for customers from initial lead prioritization through needs analysis and advice, to automated underwriting. Additionally, integrated computer vision technology automates the underwriting process while detecting fraudulent documents, which drastically reduces the policy underwriting time for the policyholder and the operation costs for the carrier — a benefit for both parties.

See also: Stop Being Scared of Artificial Intelligence

Providing a better customer experience

By creating consumer-specific predictive models, AI helps policy providers enrich their recommendations to both potential and current policyholders, resulting in better synchronization between needs and offers, and superior service across the entire sales chain. 

For example, speech recognition combined with natural language understanding can interpret essential information from customer inquiries in real time. The AI can then provide contextualized and transparent recommendations for both advisers and agents. The result? Advisers and agents can act faster and more accurately, increasing the number of cross-sell opportunities and the win ratio of quotes.

A look ahead

AI will play into several trends that the industry will start to see unfold in the next five years.

Insurance products and service offerings will become more and more complex

As consumers’ needs continue to develop, so will the products and services required to address them. Five years from now, insurers will offer more complex services and, in turn, will need to be able to better explain these offerings to policyholders. This is where AI technology will be vital. AI will help guide advisers, agents or self-care portals in recommending the most relevant products for each individual. We will also see more embedded insurance offerings, with AI helping to pick and pair the consumer’s best options.

Insurance companies will feel the threat of Big Tech

In five years, the insurance business will be even more intermediated through digital platforms and marketplaces. The list of examples is already growing — Airbnb offers renter insurance, Amazon is offering delivery guarantees, Booking.com proposes travelers’ insurance. 

As insurers continue to compete with Big Tech, they need to match the competition’s standards by offering immediate, simple and adaptive policies with AI. Without full process automation on key distribution activities, traditional insurers will struggle to exist in this tech-focused ecosystem and will be challenged by full-digital players.

The industries where the competition is stronger and the insurers are more primed for innovation include personal lines of insurance such as auto, property and casualty and health insurance. In the future, we will need to see these industries take off with artificial intelligence to stay in the game.

See also: Pressure to Innovate Shifts Priorities

At Zelros, we believe that AI-enabled solutions will empower insurance players to keep up with the rising expectations of their customers. AI will give them the acceleration needed to have the real-time experience that everyone now expects when engaging with a brand.

Microsoft Just Raised the Bar

While insurance has been steadily improving communications with customers through gradual adoption of chatbots, Microsoft just put another big item on the industry’s technology to-do list: speech recognition.

Microsoft’s announcement on Monday that it is buying speech-recognition firm Nuance for $16 billion means that insurers will have to confront the technology — likely sooner than they had expected. Big Tech has already been getting consumers accustomed to having their speech understood by devices, mostly via Siri and Alexa, and the Microsoft purchase of Nuance will push speech recognition into many business transactions. All industries, including insurance, will have to react as Big Tech again raises the bar for what constitutes a reasonable customer experience.

So, it’s worth spending a minute thinking about what speech recognition will — and won’t — change in insurance.

My bet, having followed the development of a host of fundamental changes in technology for decades now, is that speech recognition mostly will mean the end of the sorts of decision trees that customers now have to go through to get to the right spot in a call center or a corporation.

At the moment, such automated answering systems generally ask callers to respond to a series of options by saying a number or pressing a key. The systems may then ask callers to repeat the process, maybe even multiple times, as a decision tree gradually narrows down the options and determines where to direct the call.

With a system based on speech recognition, customers will simply begin a conversation by saying something like, “I’m calling to check on a payment,” or, “I’d like to check on the status of my claim.” The artificial intelligence may be able to respond immediately, if it can match the caller’s phone number with the appropriate records. If not, the AI can then ask a question or two and respond to simple questions on its own or transfer the call to the right human representative for a more extended conversation.

If a caller wants to speak Spanish, he’ll just start talking in Spanish rather than having to oprima numero dos.

Doing away with these automated menus won’t materially change any caller’s life, but they are enough of an annoyance that insurers and big agencies will need to get rid of them as soon as speech recognition allows. As the world continues to move toward self-service, the industry will need to keep expanding the capabilities of the speech-recognition systems to handle more complex queries and more extended conversations — along the lines of the progression occurring with chatbots.

The change to speech recognition will be a heavy lift. It not only requires mastering the speech recognition technology but tying it into back-end computer systems and integrating voice queries with customer interactions via text message and via the website or app. Training and staffing of agents will need to change, too.

The shift won’t have to happen right away. Nuance (which developed the initial speech-recognition technology for Siri) has a heavy focus on healthcare, so Microsoft won’t immediately be raising customer expectations across all industries. But the change to speech recognition will take long enough and be disruptive enough that insurance companies should develop road maps soon.

Now, I’ve seen some project even more sweeping changes because of speech recognition, but the claims are overwrought. Yes, speaking is often more convenient than typing, but speech has its limitations. If I’m traveling alone and looking for a hotel or a place to eat, I might ask Siri to give me some options, but I’m going to pull off to the side of the road to scroll through them and investigate. And if I’m going to need to read about such relatively simple options, imagine how much more important reading is for all but the simplest queries related to insurance.

Speech won’t become the primary interface for the internet any time soon, despite what some have written and despite great improvement in the technology.

But speech recognition still marks a significant change, and Big Tech is once again setting rules for customer experience that the rest of us will have to abide by.

Stay safe.

Paul

P.S. Here are the six articles I’d like to highlight from the past week:

The Future of AI in Insurance

Organizations hoping to deploy artificial intelligence have to know what problems they’re solving — no vague questions allowed.

10 Ways to Prepare for the Hard Market

In soft markets, differentiation can be challenging. But hard markets present an opportunity for the best insurance professionals to stand apart.

How to Deliver the ROI From AI

A technology has emerged that can harness AI across all departments of a business like never before. It’s called a feature store.

Benchmarks, Analytics Post-COVID

The pandemic introduced several variables that question the validity of actuarial models and benchmarks.

The Key to the Future of Mobility

Telematics can help solve some of the insurance industry’s oldest problems, but, first, insurers must win the client’s trust.

Time to Start Over on Secondary Towing

The current system for secondary towing is excruciating. The only reasonable solution is to start over from scratch.

How to Deliver the ROI From AI

For insurance companies, there’s a constant influx of data from almost everywhere: customers, marketing teams, sales representatives, underwriting departments, HR and more. These massive amounts of data can be used to make your company better, or so you’ve been told. But harnessing business value from this data isn’t as easy as it might seem. It takes more than collecting data and building models for AI to help a business.

In the last few years, a technology has emerged that can harness AI across all departments of a business like never before, enabling massive, company-wide returns. However, the technology alone isn’t enough; there must be the right combination of technology, people and process.

Feature Stores for Machine Learning

Data scientists love to dive deep into different algorithm alternatives, but the most effective way to get better predictive signals is to get the right data. For example, in media personalization, companies often used the fact that a particular user visited a particular site (like a luxury shoe brand) as an important data point. But this is deceptive. Recency also matters. If a visit to a particular site has been within, say, the last 48 hours, you get significantly better conversion on ads. You have to get the right data points represented to get a model to perform!

Data points that inform models are known as features. These are usually transformed data attributes, which together form the feature vectors that are the input to machine learning algorithms. The process of turning raw data into features is called feature engineering, and is — in my opinion — the critical success factor for practical ML projects that deal with corporate structured data.

Not only is feature engineering essential for model accuracy, it’s also incredibly time-intensive for data scientists. Data preparation takes 80% of data scientists’ time, which means they only have 20% left to actually build, test and implement models. This makes it incredibly difficult and expensive to build models at the volume that would be necessary to provide value for every department of an insurance company.

Technology leaders like Uber, Google and Airbnb have spent years and millions of dollars designing infrastructure that makes it possible to unleash the power of AI throughout a company. The solution they have all converged on is a feature store.

A feature store is a central repository that stores features, data lineage and metadata associated with all the machine learning models in a company. In essence, it is a single source of truth for all of the data science work within one organization. Being able to share and re-use features boosts data science productivity by cutting down duplicate work and making it easy for data engineers, data scientists and ML engineers to collaborate. Each machine learning model becomes cheaper and easier to produce. (If you want to learn more about why that is, there’s a more in-depth resource here.)

See also: 6 Implications of Big Data for Insurance

Integrate Diverse Skill Sets in Data Science Teams

Even though feature stores are incredibly powerful tools, they are ultimately still tools, which means how they’re used will influence how helpful they are. Even with a feature store bridging the gaps inside a company, a “siloed” data science structure makes it hard to truly integrate AI into the enterprise.

Traditionally, the people who can manage large volumes of data and “do the math” of machine learning are sitting in their silos. They are away from the action — where the application interacts with customers, suppliers and employees. They are one step removed from the business. 

But the AI or data science team is not equipped to get the job done independently. They simply do not have enough knowledge about the business or the applications that will deploy the models to lead to production applications that deliver business outcomes. The secret sauce to a successful AI implementation is diversity. Data scientists need to work side by side with people who know the business and the application from inception to completion. 

Culture of ML Experimentation

Machine learning projects need to include more than just subject matter experts and application developers as part of the data science and data engineering teams. To do ML well, you have to create a culture of experimentation within your data science team. 

Markets change, bad actors innovate, the climate changes, the competitors change and so much more. What was the perfect feature vector on go-live might produce noise two months later, or worse — tomorrow. You must realize that an ML project will not thrive with a hands-off approach; it is a process of continuous experimentation and continuous improvement. So the secret is to keep the diverse team intact, frequently evaluating the deployed models, and able to experiment with new features.

See also: Insurance Outlook for 2021

Conclusion

The technologies and organizational silos of the past weren’t made to embed AI into the fabric of organizations, and as a result, companies that aren’t innovating aren’t benefiting from the full power of AI.
To inject AI throughout a company, the goal needs to be the continuous improvement of business outcomes.

You can achieve this by optimizing the two bottlenecks of the operational process:. First, overcome the feature bottleneck of the ML lifecycle with a feature store. Second, overcome the organizational bottleneck of the technology lifecycle by distributing data experts in every department of your company. Your teams will finally be able to demonstrate a significant ROI from your AI.

How AI Will Define Insurance Workforce

Prior to COVID-19, the U.S. boasted historically low unemployment and a roaring economy. Nearly every industry was expected to face a severe talent shortage within the next 10 to 20 years. But then March hit, and the world turned upside-down.

Since then, the pendulum has swung in the other direction. The current unemployment figures are reporting as many as 10.7 million people are out of work, and, despite this sudden abundance of available workers, staffing issues remain — they’ve just become more complex.

To navigate this wildly fluctuating environment, companies will rely on data for decision-making about hiring, training and countless other matters that affect the bottom line. This will require tools, like artificial intelligence (AI), to make sense of data and to adjust quickly amid uncertainty.

The best way to examine AI’s value in today’s uncertain world is to look at how it can work within a specific industry. Doing so makes it possible to show practical applications from which lessons can then be applied to other industries.

Commercial Insurance: A Case Study

Like other industries, commercial insurance faced a significant hiring crisis pre-COVID-19. The average claims adjuster remained in the industry for just four years — about the time it takes to gain full expertise — and those workers who stuck with claims have inched closer to retirement. So, this multibillion-dollar market is at risk of losing much of the human brain trust that enables current systems to run, as new workers cannot be hired, trained and retained fast enough to balance the scales.

Fast forward to today. Commercial insurance looks markedly different. The types and volumes of claims are changing. For example, claims related to COVID-19 contact or work-from-home circumstances are rising quickly, as are post-termination claims, while traditional claims have dropped.

At the same time, access to traditional healthcare has been in flux. To combat the limitations on available providers, telehealth solutions have exploded, opening up a whole new set of providers that claims reps need to become somewhat familiar with to facilitate claims accordingly — claims that bear a greater potential for fraud and litigation, which cost companies millions of dollars each year.

In short, almost everything about claims operations has changed — and, like many other industries that have been traditionally slow to adapt to new challenges, commercial insurance faces real hurdles.

The Importance of Data and Intelligence

Data is the key to overcoming dramatic changes within a relatively static industry. Maintaining a pulse on what’s happening across a business, or with a specific claim, and how it relates to things experienced previously is important; spotting trends early is vital. Organizations require data to determine if their plans and practices are working — and, if they are not, data should be used to drive intervention and adaptation.

But thousands to millions of data points alone won’t save the day if an organization doesn’t have the capability to understand what the data is telling them. What is the context? How are points connected? If a trend continues, what will be the effects six months or two years from now?

AI systems unlock the meaning of data to make it useful, pinpointing where organizations need to make adjustments. In commercial insurance, AI could allow for expanding provider networks to offer better, faster access to care. To actually expand networks using quality providers, systems need to tap into more data to learn which providers have achieved the best outcomes on which types of cases.

What is particularly exciting about implementing AI in this rapidly changing environment is that interpretations of data are not fixed. Machine learning capabilities are constantly refining and updating insights so that organizations — and their people — can respond accordingly.

See also: How AI Transforms Risk Engineering

Designing the Future Workforce

So, if data analytics and AI become staples in modern business, how do they solve the human resource problem? What do they mean for the future workforce? The answer is threefold.

Data determines what your hiring needs actually are: In a world that is changing so quickly, your business might not need as many people specialized in a certain area, whereas new opportunities or divisions may emerge. Your business may be forced to alter its offerings to match customer needs. Data is the guide; it lets you home in on exactly what skills are required.

AI guides training: Because AI is able to analyze so much data so quickly, new hires are able to access the information and prompts they need to do their jobs well as soon as they need it. There is not as much feeling around or dependency on senior colleagues. This is not to discount the value of experience, but it means that workers can reach a competent level much faster; what they lack in experience and intuition is replaced by data-driven insights and standardized practices.

AI augments jobs: AI solutions take care of many of the rote tasks workers are routinely bogged down with today. As a result, employees can focus on making more efficient, informed decisions; they can actually use their brains more. AI flags potential errors or problems so that they can be addressed before they escalate. Reps can focus on delivering compassion at a time when people need it most.

While COVID-19 has fundamentally altered the future workforce, tools like AI help get it back on track. In leveraging it effectively, organizations will become nimbler and more responsive to conditions while employees are more knowledgeable and effective.