Tag Archives: bots

Race Is on for Bots in Workers’ Comp

The thoroughbred horse Spring Racing Carnival is in full swing in the state of Victoria, Australia, culminating in the running of the Melbourne Cup, “the race that stops a nation.”

Attendance at what is more akin to a “garden party” setting can well exceed 100,000 on Derby Day, Oaks Day and Melbourne Cup Day, and there are plenty of on-course bookmakers in the Betting Ring ready to offer odds to the eager punter.

Bookmakers, like insurance underwriters, make their profit from risk by estimating the probability of an event occurring. This is reflected through the bookmaker’s odds and the rates charged by underwriters. The traditional method of managing a balanced book has been as much an art as a science combining deep research, risk parameters, instinct and psychological management, which is gradually changing by taking advantage of emerging technologies. While bookmakers are forging ahead with the opportunities provided through technology, the insurance industry has lagged, providing an opportunity for others, known as insurtech companies, to revive some dormant approaches to distributing insurance products. Two such companies are Lemonade in the U.S. and Huddle in Australia.

Lemonade and Huddle are not insurance companies per se, but rather underwriting agencies that have taken advantage of emerging technologies, including the use of bots to deliver and service coverages on behalf of an insurance company, the underwriter of risks. For example, Huddle is an underwriting agency for Hollard Insurance, delivering and servicing three of Hollard’s insurance coverages in Australia: private motor vehicle, home and travel insurance. Lemonade Insurance Agency sells and services homeowners and renters insurance coverage insured by Lemonade Insurance Company in the U.S.

See also: 7 Keys for Automated Event Response  

Bots can be equally and effectively applied to tasks that are structurally repetitive such as in workers’ compensation, where, for example, in the vast majority of cases to determine whether an incident is AOE/COE (Arising Out of Employment/Course of Employment) a “yes” or “no” answer is required to nine questions on average. Using bots can reduce this effort to just seconds, and for the 75% of all claims that are either medical only or short-period lost-time claims, processing times can be reduced to mere minutes.

Bots also provide the ideal opportunity for introducing machine learning and artificial intelligence into workers’ compensation claims. For example, bots can be applied to monitoring the progress of an injured worker’s recovery and, if necessary, suggest revised treatment plans. Adversarial behavior is not uncommon in this area, and bots can be used to alert and prevent a likely occurrence. Returning an injured worker to the workforce can be a challenging process. Bots can assist in developing a road map. Also, as fraud is an ever-increasing problem in workers’ compensation, a further benefit to introducing bots is that their visibility in monitoring the processes can act as one of the best deterrents.

While some may say these tasks are better handled by claims personnel, administrative costs can be exorbitant, especially in California. ULAE (unallocated loss adjustment expenses) and ALAE (allocated loss adjustment expenses) can account for around 40% of claims costs, which can be dramatically reduced with bots. Whether current claims administrators adopt this approach remains to be seen, but insurtechs are embracing it and will challenge existing third party administrators for marketshare by providing a better service at a much lower cost. Insurtechs could equally challenge the need for an insurance company’s in-house claims department.

Between now and 2023, it has been suggested, investors will be investing in insurtech startups like eager punters placing a bet on their favorite horse. However, as with betting on a horse race, there is a high probability that a number of insurtechs will be scratched from the race due to poor performance, and, for those remaining, the race conditions may be challenging. Only those insurtechs with agility and stamina will make it to the finish line. The race is on.

Digitization: Bots Take the Reins

Automation and robots are not just revolutionizing work in factories and warehouses but also in offices, including insurance companies. According to a study by the McKinsey Global Institute, in around 60% of all occupations, 30% or more of all tasks could be performed by machines—and even 20% of management tasks could be performed by robot workmates.

How will companies change when office work is automated?

If robots and software programs replace human work, this costs on average around 13% of the wage bill paid for work in a developed country like the U.S. At a stroke, moving this work to low-wage economies becomes less attractive because offshoring on average costs almost 40% of the wage bill in developed countries.

A British insurance broker today automatically processes 3,000 claims a day—all managed by a grand total of four employees. And the subsidiary of a major European energy utility has automated several important processes in administration—from billing and collection of consumption data to consumption management. What was previously handled by 250 employees is now managed by 110 robots, overseen by 11 human supervisors. One of the biggest wireless providers has automated 15 complex administrative processes, which is equivalent to 35% of its work volume: 160 robots process around 500,000 transactions a month. And it doesn’t just save costs. Since the results are more reliable than those produced by human employees, sales staff on the front line have more capacity because they don’t keep having to check back with head office to query an incorrect entry.

See also: Robots and AI—It’s Just the Beginning  

Machines, then, are superior both in terms of cost and quality, with robots and computers producing more accurate results. They rigidly follow their programming—errors are not a factor. And even if production is ramped up, the same quality is achieved with large volumes as with smaller volumes. Robots don’t even need breaks—they can work around the clock if necessary. And something else that’s particularly important in times of increased compliance regulations, machines record their activities in seamless logs, and any activity can be verified later.

However, because it will only be possible to fully automate a handful of jobs in the foreseeable future, work content and processes will need to be redefined. For example, if banks use machines to review loan applications, employees have more time to advise customers, thus producing more applications a day. Financial advisers no longer need to analyze the financial data themselves and can therefore work more on creative investment strategies. Robots can even help develop investment strategies—meaning recommendations that were previously only given to the best customers because they tied up so much adviser capacity, can now be granted to every customer as “robo-advice”.

Automation is even relevant for complex jobs

The opinion still persists that automation is only suitable for the work of poorly qualified and low-paid workers. However, the study by the McKinsey Global Institute comes to a different conclusion: Even around 20% of management tasks can be handled by machines. They can analyze reports and presentations for operational decisions, check status reports for compliance with targets and even prepare HR decisions. In turn, managers have more time for thinking, for communicating and for managing—and the time needs to be used wisely. The more intensive the use of data, the more managers can benefit from automation—for example, in investment management where data volumes can be leveraged and turned into recommendations far more systematically using artificial intelligence and machine learning systems than is possible by a human.

Automation is more than a technological decision

Technology is, of course, a key element on the road to intelligent process automation; however, this is primarily a strategic decision that must be made by top management. The management must assess the extent to which the company is affected by the changes and decide whether to develop a specific strength in the area and to be at the forefront of change, or whether to hold back as a follower and avoid the mistakes of the pioneers. Ultimately, managers must decide how to adjust the operational business model of their company—from the organization and culture to the development of talent and skills. Experience shows that companies that selectively automated their processes and reduced costs quickly and easily in those areas with robotic process automation had to redefine all of their processes on the road to intelligent process automation—entirely in the spirit of the business process re-engineering of the 1990s. The key objective isn’t simply far-reaching automation of all processes, but to improve the overarching business system.

It is still uncertain how soon automation will become widely adopted in offices. On the one hand, the timing depends on the pace of technological developments, and on the other how quickly the technological possibilities will be accepted and implemented in companies. Industries with a strong reliance on pure software solutions lead the way. They quickly achieve significant savings with manageable investment—the finance industry, where processes can be automated at relatively little cost, is a good example. The more hardware that is required, or the more security provisions and legal regulations that have to be met, the longer the switch to automation takes.

See also: A Key Misconception on Digitization  

Management must have a good overview of how their own industry’s parameters are developing, while at the same time developing a feeling for the economics of automation. This specific IQ of company leadership could become the difference between success and failure in the business world of tomorrow.

Adapted with permission of the publisher, Wiley, from DIGITAL@SCALE: The Playbook You Need To
 Transform Your Company
 by Anand Swaminathan and Jürgen Meffert. Copyright (c) 2017 by McKinsey & Company. All rights reserved. This book is available at all bookstores and online booksellers.

Robots and AI—It’s Just the Beginning

You’ve probably had regular help from a virtual assistant by phone or online, assisting you with basic tasks such as directing your call or giving you your bank balance. Helpful, right? The companies that employ the virtual assistants think so, too, and are applying these AI/robotic processes to more and more of their everyday business operations.

Often called out for being slow to change, the insurance industry is beginning to catch up quickly. It’s making sweeping changes across organizations and core systems because of the disruptive emergence of insurtech. Carriers like Celina and USAA are using AI in their daily operations and reaping the benefits.

As a result, insurers are now either delivering — or are in the process of delivering — a great digital experience to consumers. Once complete, this transformation will entail an entirely new way of doing business and servicing customers.

See also: Strategist’s Guide to Artificial Intelligence

There are four main technologies to keep in mind:


Robotics is the branch of technology that deals with the design, construction, operation and application of robots, virtual or physical. They are autonomous or semi-autonomous machines or systems that can act independently.

Artificial Intelligence

AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation. AI is software that learns and improves. Some robots can use AI to improve their capability by learning, but that is optional.

Cognitive Computing

Cognitive computing technologies are a subset of AI. Cognitive computing “refers to computing that is focused on reasoning and understanding at a higher level, often in a manner that is analogous to human cognition,” writes Lynne Parker, director of the division of information and intelligent systems for the National Science Foundation, in Computerworld. “This is a subset of AI that deals with cognitive behaviors we associate with ‘thinking’ as opposed to perception and motor control.”

Robotic Process Automation

Insurtech consultant Celent defines robotic process automation (RPA) as a set of technologies that can automate processes that currently require human involvement. Robots replicate human behavior to conduct the tasks as a human would; robots also optimize the tasks. RPA can yield benefits when applied to the right roles. It does well supporting repetitive tasks in various environments where there is little change, often back-office support roles and tasks.

Accenture found that cost savings after deploying RPA can reach as high as 80% and time saved on tasks as high as 90%. Automating repetitive processes means tasks are completed quickly with fewer errors, opening up new opportunities for employees to focus on more customer-centric tasks.

But RPA is not the answer to everything. It does not think, reason or predict. It completes simple, repetitive tasks quickly, but it does not learn or self-improve. Developing an enterprise-wide strategy to determine where RPA provides the most value and to anticipate the organizational change that may result is the prudent approach.

The Future Is Here

IBM’s Watson and Amazon’s Alexa are early examples. Insurers already have joined the revolution. Celina Insurance Group uses an analytics-based agency prospecting tool to appoint agents in high-potential underserved areas. USAA’s “Nina” is an AI virtual assistant that chats with customers on the USAA website. It’s designed to respond to 120 questions, from reporting stolen payment cards to changing a PIN.

See also: The Big Lesson From Amazon-Whole Foods  

There will inevitably be lessons to learn from successes and failures of this first wave of robotics and AI. However, early adopters of these technologies also risk success. Investing in innovation is what will allow insurers to stay ahead of disruption and, in some cases, create it.

As robots evolve, their capabilities and applications will no doubt be vast. Just as we could not have predicted how the internet — and now the Internet of Things — would evolve, robotics and artificial intelligence will likely follow the same course.

Why 2017 Is the Year of the Bot

In the 2013 movie “Her,” Theodore Twombly, a lonely writer, falls in love with a digital assistant designed to meet his every need.  She sorts emails, helps get a book published, provides personal advice and ultimately becomes his girlfriend. The assistant, Samantha, is A.I. software capable of learning at an astonishing pace.

Samantha will remain in the realm of science fiction for at least another decade, but less functional digital assistants, called bots, are already here. These will be the most amazing technology advances we see in our homes in 2017.

Among the bestsellers of the holiday season were Amazon.com’s Echo and Google Home. These bots talk to their users through speakers, and their built-in microphones hear from across a room. When Echo hears the name “Alexa,” its LED ring lights up in the direction of the user to acknowledge that it is listening. It answers questions, plays music, orders Amazon products and tells jokes. Google’s Home can also manage Google accounts, read and write emails and keep track of calendars and notes.

Google and Amazon have both opened up their devices to third-party developers — who in turn have added the abilities to order pizza, book tickets, turn on lights and make phone calls. We will soon see these bots connected to health and fitness devices so that they can help people devise better exercise regimens and remember to take their medicine. And they will control the dishwasher and the microwave, track what is left in the refrigerator and order an ambulance in case of emergency.

See also: What Do Bots Mean for Insurance?  

Long ago, our home appliances became electrified. Soon, they will be “cognified”: integrated into artificially intelligent systems that are accessed through voice commands. We will be able to talk to our machines in a way that seems natural. Microsoft has developed a voice-recognition technology that can transcribe speech as well as a human and translate it into multiple languages. Google has demonstrated a voice-synthesis capability that is hard to differentiate from human. Our bots will tell our ovens how we want our food to be cooked and ask us questions on its behalf.

This has become possible because of advances in artificial intelligence, or A.I. In particular, a field called deep learning allows machines to learn through neural networks — in which information is processed in layers and the connections between these layers are strengthened based on experience. In short, they learn much like a human brain. As a child learns to recognize objects such as its parents, toys and animals, neural networks learn by looking at examples and forming associations. Google’s A.I. software learned to recognize a cat, a furry blob with two eyes and whiskers, after looking at 10 million examples of cats.

It is all about data and example; that is how machines — and humans — learn. This is why the tech industry is rushing to get its bots into the marketplace and are pricing them at a meager $150 or less: The more devices that are in use, the more they will learn collectively, and the smarter the technology gets.  Every time you search YouTube for a cute cat video and pick one to watch, Google learns what you consider to be cute. Every time you ask Alexa a question and accept the answer, it learns what your interests are and the best way of responding to your questions.

By listening to everything that is happening in your house, as these bots do, they learn how we think, live, work and play. They are gathering massive amounts of data about us. And that raises a dark side of this technology: the privacy risks and possible misuse by technology companies. Neither Amazon nor Google is forthcoming about what it is doing with all of the data it gathers and how it will protect us from hackers who exploit weaknesses in the infrastructure leading to its servers.

Of even greater concern is the dependency we are building on these technologies: We are beginning to depend on them for knowledge and advice and even emotional support.

The relationship between Theodore Twombly and Samantha doesn’t turn out very well. She outgrows him in intelligence and maturity. And she confesses to having relationships with thousands of others before she abandons Twombly for a superior, digital life form.

We surely don’t need to worry yet about our bots becoming smarter than we are. But we already have cause for worry over one-sided relationships. For years, people have been confessing to having feelings for their Roomba vacuum cleaners — which don’t create even an illusion of conversation. A 2007 study documented that some people had formed a bond with their Roombas that “manifested itself through happiness experienced with cleaning, ascriptions of human properties to it and engagement with it in promotion and protection.” And according to a recent report in New Scientist, hundreds of thousands of people say “Good morning” to Alexa every day, half a million people have professed their love for it, and more than 250,000 have proposed marriage to it.

See also: Top 10 Insurtech Trends for 2017  

I expect that we are all going to be suckers for our digital friends. Don’t you feel obliged to thank Siri on your iPhone after it answers your questions? I do, and have done so.

What Do Bots Mean for Insurance?

As customers increasingly demand a better experience when they interact with companies, including insurers, help is coming from a counterintuitive source. It turns out that one of the best ways to be more personal is through… robots.

More precisely, the answer is turning out to be chat robots, or “chatbots.”

People don’t like having to phone call centers and wade through that phone tree — “Para continuar en espanol, oprima uno… For billing, press 2; for….” Many, especially younger people, just want to be able to text a question and get it answered. That’s how they handle everything else. So, many companies are realizing they need to have customer service reps that respond to texts, and they’re seeing an opening to use chatbots.

Using so-called natural language processing to understand a text message and then drawing on artificial intelligence to both find the answer and generate a reply, chatbots can handle perhaps 70% to 80% of queries. They can hand a conversation off to a human when necessary and can take the conversation over again, without the customer’s ever realizing that a bot has been involved or that a handoff occurred. In fact, the bots can wind up sounding a lot less robotic than the standard call center rep who is only allowed to read off a script.

The bots are so efficient about finding answers that they actually have to be slowed down, so the customer doesn’t think, “No one could type that fast,” and wonder if a computer is involved. (A certain percentage of typos can also be programmed to appear, as can emojis or lots of exclamation points, to make the bots seem more human. You can actually program the bots to have different personalities.)

See also: Want to Enhance Your Customer Experience?

With so many mundane tasks handled by bots, the call-center reps get to deal with more interesting issues and can spend more time with customers, giving everyone a better experience.

Although they haven’t shown up widely in insurance yet, they are in use in numerous other industries, with great success.

Why now?

Chatbots have been around for more than 20 years. Why should companies pay attention to chatbots now?

For starters, these days just about everyone is carrying a super-computer around in her pocket. In 1991, 1GB of flash memory would have cost around $45,000. Now, most phones have at least 32GB of memory. Processors are more than 1,000 times as fast as they were in 1991. So, the technology for chatbots is lightyears ahead of where it was.

Companies have also placed an increased focus on messaging, including with bots. Facebook Messenger uses more than 11,000 chatbots to respond to messages. The chat app Kik recently said that more than 20,000 bots have been made specifically for its platform.

Perhaps even more importantly, the pendulum in the customer-business relationship has swung heavily in favor of the customer. Companies no longer control the message/brand; it’s all out there in the ether, and companies need to guard their reputations by caring for customers. Some companies, such as Zappos, have pretty much built their businesses on the customer experience, while others, including cable companies (Comcast, most notoriously), are vilified.

Insurance companies can see what’s happening in other industries and see what they need to do. Net Promoter Scores (NPS) are the insurance industry’s most consistent measurement of customer loyalty, and, despite some pockets of brilliance by individuals, most of the time the insurance industry stinks. Chatbots can help.

Chatbots create a conversational web and conversational commerce. They can even be programmed to wish the customer a happy birthday or happy anniversary or make some comment about how long the customer had been with the company.

Chatbots make a company’s behavior more consistent across the board, especially in terms of elegance and simplicity. On top of that, it’s easy to keep the bots on-message, and they only need to be trained once.

See also: ‘Age of the Customer’ Demands Change

In 2013, it was estimated that it takes five screens to get a user to where he wants to go. In 2015, that number has increased to seven. Bots get the information almost instantly, even if that means going to a deep link in an app or on a site or in a corporate data center.

Bots aren’t “one size fits all.” They’re “one size fits one.” So work has to go into customizing them for a company. But simple bots such as for frequently asked questions can go live in a day, and an ecosystem of bots can be developed over time. Once a bot exceeds its ability to answer a question, it might initially pass the question to a human, but, in time, the handoff could go to another bot that has been developed.

Bots will also be able to take on more tasks, including outreach to customers. Bots could automatically alert customers of an impending hurricane and begin a dialogue with them about what steps to take to prepare, who to call if they need immediate assistance, how to file claims, etc.

In insurance, there’s nothing like the Domino’s pizza tracker, which allows a customer to follow along with an order every step of the way, from the order to the oven to the front door. But there will be, and imagine how helpful that will be with claims. Many customer calls are about where their claims are in the process, so streamlining it and providing a bot with the capabilities to respond to the customer would make the process easier, eliminate a lot of calls — and make the customer much happier.

Of course, an insurance bot isn’t going to answer “what is the meaning of life?” or “how much wood could a woodchuck chuck if a woodchuck could chuck wood?” like Apple’s Siri can. But a bot can be tailored and trained to answer many questions, filling a gaping hole in the insurance world.