Meet Algorithm, Your New Partner
Imagine a future where fancy computers handle your tedious administrative tasks while you do better things, such as delving deeply into your clients’ risk exposures on a daily basis.
This blissful scene is not at all farfetched. In fact, technology can take on the monotonous functions of broking, freeing brokers to provide the value-added services their clients crave.
The need for these technologies is dire. “Brokers are awash in information, taking in and passing out an enormous volume of data,” says David Bassi, an executive director in consulting firm EY’s insurance practice. “Automating this exchange of information liberates brokers from having to manually key in all this structured and unstructured data.”
The value and range of new technologies spreads across a wide swath of a broker’s business—more to come on artificial intelligence and the like—but it seems like right now brokerages are more readily focusing on back-office automation, where the administrative work of the business is carried out. Software known as RPA, or robotic process automation, can carry out simpler, more repetitive human tasks (such as data entry) that don’t necessarily take knowledge or insight to perform. Consider the tedious work performed by company accountants to close the books. Using RPA tools, these data can be easily extracted from a brokerage’s myriad systems and applications to ensure accuracy and a faster close.
This is good news for accountants. “Smart accountants get to do what they yearn to do anyway—study the numbers to learn where the firm is growing business or losing it,” says Therese Tucker, founder and CEO of publicly traded BlackLine, a financial and accounting software firm that provides RPA tools. “This important value-added activity is lost if they’re hunkering down in the trenches tallying up the numbers.”
RPA also can be employed to fulfill a broker’s legal and compliance obligations. “We’re seeing growing interest in the technology to make sure a broker’s various contracts, reports and disclosures are correct and compliant from a regulatory standpoint,” says Dimitris Papageorgiou, a principal in EY’s people advisory services practice. “We know that some brokers are in a pilot stage with the tool.”
The back-office opportunities presented by RPA are both strategic and tactical. As human workflows decrease, people are able to work more efficiently, giving them more time to build relationships with clients.
“When these technologies work very well, they take out about 80% of the effort for 20 people, meaning that these 20 people now have 20% of the work left to do,” says David Kuder, the Robotics & Cognitive Automation leader at Deloitte Consulting. “Extrapolating from this metric, this means you need just four people to do the work that 20 people previously did. The additional 16 people can now apply their intellect to more strategic uses, working more closely with clients to identify and reduce their losses.”
Other insurance and technology experts agree this value is hard to ignore. “RPA allows a broker to really optimize and improve the efficiency of its back office, particularly if the firm is interacting with different carriers’ legacy technology,” says Ted Stuckey, head of the global innovation lab at insurer QBE in Sun Prairie, Wisconsin.
Stuckey describes a recent visit to a midsize insurance brokerage in Los Angeles. “I walked through bullpens of people doing mind-numbing data entry tasks,” he says. “They were working with upwards of 20 different carriers’ legacy systems. From a process automation perspective, in addition to an auditability and integrity standpoint, RPA represents a huge opportunity. You’re able to push people to higher-value work, such as dealing with the more complex transactions where brokers shine.”
These many advantages add up to enrich the relationship with clients. “In our industry, who has the best opportunity to improve the relationship with the end customer? The broker, of course,” Stuckey says. “These technologies present a vital opportunity to improve customer engagement. Right now, the book of business for many midsize and larger brokers is so substantial that it’s extremely hard to stay front and center in the customer’s eyes. These tools give brokers the ability to be there when clients need them most.”
To illustrate the point, Stuckey provided the example of another midsize brokerage partner. “The firm had a team of people whose sole job was to make calls to customers prior to the [policy] renewals,” he says. “Most of the calls went to voicemail. Imagine if they used a chatbot tool using natural language processing to simulate conversations with customers? You’ve just freed up that team of salespeople and account executives to be available every minute for customers’ questions and concerns.”
Brokerages Shift Cautiously into Cognitive Computing
They are limited only by their imagination to help their business.
“The toughest part in deploying new technologies is making the business case for them,” says Stewart Gibson, senior vice president and chief information officer at USI Insurance Services, which recently agreed to purchase Wells Fargo Insurance Services USA. “Early adopters bear the bulk of the cost of a new technology’s implementation. We’re more interested in being a fast follower, tiptoeing into these new solutions as their value becomes clearer.”
Here’s a brief look at some of the new technologies being deployed at a few large brokerages.
“RPA is probably the most prevalent of the new cognitive tools we’ve been using,” Gibson says. “We’ve deployed an RPA tool from Cisco to perceive threats within our network infrastructure.”
Like other large brokerages, USI’s network overflows with millions of transactions daily. Any one of these interactions can cause havoc if the data being transmitted are infected with malware.
“We’d need an army of network engineers watching all the end points inside the network to discern trouble spots,” Gibson says. “The RPA tool provides this vigilance with little manpower. It’s smart enough to identify what might happen or something that is about to happen right now. Network traffic is then automatically rerouted over backup circuits to limit the potential downtime.”
The brokerage also has implemented a matching tool using machine learning to assist salespeople in configuring risk transfer solutions covering customer risks in the middle market. Called Omni, the tool draws insights from USI’s comprehensive database containing risk-based information on more than 100,000 clients over the past 100 years.
“A rules-based engine populated with algorithms, decision trees and linear regressions helps pinpoint the specific insurance coverages and financial limits of protection a client may need,” Gibson says. “It analyzes clients in a similar industry that had chosen to cover their risks in a specific way and then presents the financial outcomes of these decisions—did the insurance fully protect them or just partly protect them? It then configures a suite of customized insurance solutions for the client.”
USI built Omni from scratch using spreadsheets six years ago. The tool has evolved in the intervening years and has recently been turned into a web application. “All it takes is one button to generate a client-ready presentation,” Gibson says. “Since this is a machine-learning solution, each time we bind new coverages for a client, the tool incorporates this new knowledge in future presentations.”
USI is just starting to evaluate artificial intelligence solutions using natural language processing. “We like the idea of processing simple customer requests without having to be on the phone,” Gibson says. “And we also like solutions that pick up inferences from a customer’s words to fix a problem without having a human involved. We may explore a proof-of-concept of these self-service tools down the line but are not yet ready to go mainstream.”
Like other brokerages, Hub International keeps a vigilant eye on developments in the bustling insurtech sector, where hundreds of innovative startups are developing novel cognitive computing solutions for the insurance industry.
“They’re constantly coming up with new ideas that are challenging the status quo in the (insurance) ecosystem, forcing us all to think about doing things in more efficient, cost-effective and customer-centric ways,” explains Carla Moradi, Hub executive vice president of operations and technology.
One way Hub is utilizing machine learning is to compare its insurance policy submissions on behalf of clients with the actual policies that are issued. Brokers typically rely on people to perform such comparisons, which absorbs time and effort that can better be devoted to customer needs. “The process now takes seconds,” Moradi says. “If an error is identified, humans take it from there.”
Hub also is studying the use of RPA to further streamline workflows. “We haven’t deployed anything yet, but we’ve identified several key areas where there are repeatable processes involving the same keystrokes,” Moradi says. “RPA can be used to do these keystrokes, increasing the amount of time our people spend with customers.”
Down the line, Moradi predicts that brokerages will collect and analyze risk-based information generated by their clients’ gradual embrace of the internet of things. “This is yet a great opportunity for us to provide another high-value client service,” she says.
Few lines of insurance are more data intensive than workers compensation, given the voluminous data produced by hospitals, physicians, nurse case managers, pharmacies, physical therapists, insurance carriers, claims adjusters, and other parties engaged in returning an injured employee to work. Using data mining, natural language processing and machine learning, Lockton is accessing and analyzing workers compensation claims to improve the employee’s experience.
“One of the most common cost drivers of workers compensation is a lack of communication with the claimant, which increases their fear and the possibility that they may hire a workers compensation attorney,” says Mark Moitoso, Lockton’s senior vice president and analytics practice leader. “To better understand how a claimant is feeling, we’re relying on text mining and machine-learning technology.”
Much of the data in the workers compensation claims process, such as the notes taken by claims adjusters, is unstructured. By digitizing this information, it can be easily searched using text mining. “We can look for words and phrases indicating a claimant’s apprehension with his or her progress toward the return-to-work objective,” says Moitoso. “Once there appears to be a problem, our people can step in with compassion and empathy to help solve the dilemma.”
Moving Past Automation
Automation is one thing, but it’s just a start. Many large insurers are investing heavily in cognitive computing, building state-of-the-art solutions internally or purchasing them from the growing ranks of insurtech startups. Cognitive computing is more than just automation. It uses machine learning to perform human tasks in an intelligent way, incorporating things such as natural language processing, text mining and image recognition.
Most insurance brokerages are said to be just nibbling at the edges of these types of opportunities. “Many brokers are still in the very early days of assessing the benefits of cognitive computing,” says Anand Rao, global artificial intelligence lead at consulting firm PwC. “They’re using a few of these tools but have not yet made truly substantial investments.”
Other consultants agree brokerages are comfortable sitting on the fence for now, waiting to see how the market shakes out. “Unlike large global banks and insurers, many insurance brokers are in a proof-of-concept stage with these technologies,” Kuder says. “There’s been a lot of talk and a lot of hype, and a few brokers will say they’re doing this and that. But only 10 to 15% of brokers are doing much of anything.
“No firm wants to be the first headline replacing a ton of labor with robotics or cognitive automation. But everyone is absolutely experimenting with these technologies or has it on their radar screens.”
Time will tell if this experimentation leads to fuller deployment. For now, the pace is slow. According to a recent survey by consulting firm Accenture, 37% of insurance executives say their companies plan to “extensively” invest in machine learning over the next three years, and another 44% predict “moderate” investment. In another study by the IBM Institute for Business Value, 90% of insurance respondents predict cognitive computing will “strongly impact” their revenue models.
“Both insurers and brokers are closely examining artificial intelligence and machine learning, realizing the significant opportunities they present,” says Ashish Umre, a partner on insurer XL Catlin’s Accelerate disruption and innovation team. “The key for brokers is to identify those strategic areas where the technology will make the most difference in adding value for their clients.”
It’s Better Risk Management
While the importance of a strategic, patient approach cannot be overstated, some maintain brokers need to pay attention to what carriers are doing so the playing field does not change around them while they sit on the fence.
“What do all customers large and small want from their broker? They want their risks managed more efficiently and coherently,” says Michael Maicher, head of global broker management at Allianz. “They want convenience, transparency, trust and the knowledge that they are gaining value for the money they’re spending. These technologies help do just that.”
According to Lori Sherer, partner and insurance leader in Bain & Company’s advanced data and analytics practice, “As the carriers collect client risk data in a better format than brokers currently do, they’ll be able to help them better understand these risks, resulting in more accurate and affordable coverages. Brokers are getting paid beaucoup dollars to place the risk today. The more digital this becomes, the less relevant they will be unless they’re investing in the same tools.” Bain & Company’s insurance company clients all have innovation labs and corporate venture arms investing in insurtech startups and have presented the firm with written proposals to use a greater variety of cognitive computing solutions in the future, she says.
Maicher predicts the simple placement of the risk will become less valuable and cheaper. “Consequently,” he says, “brokers need to improve their knowledge of clients’ risks and preferences, accessing relevant data to achieve deeper insights. Clients will pay for this more sophisticated risk advice.”
Insurers are just as vulnerable to these technological forces as brokerages. “Several reinsurers are investing heavily in cognitive computing to do more risk management and loss control with the idea of working closely with the brokers to essentially displace the carriers,” Rao says. “There’s a lot of friction in the marketplace.”
This friction is good for corporate clients, as it ultimately will produce less expensive insurance products with coverages customized to actual needs.
“With lower transactional costs, risk transfer will become more attractive to clients,” says Maicher. “This will result in a higher demand for a greater range of insurance products, increasing the overall size of the market.”
New products also will emerge from the industry, as brokerages and carriers develop a better understanding of client risks. “As more companies leverage the IoT and put their data in the cloud, the related risks are sure to grow,” Sherer says. “The industry is only beginning to understand how to effectively transfer these risks. The opportunities are huge.”
The Labor Question
If brokerages continue to gradually employ more cognitive computing technologies, their actions are unlikely to result in the mass displacement of labor. The tools are intended to free employees from rote tasks so they can provide more personalized services to clients, meaning little labor displacement, if any, for the time being. Even better, the use of these technologies creates a need for new skills in a brokerage’s workforce.
“Since people will be working more directly with technology on a daily basis, the workforce needs to reflect these skills, either through recruitment or training,” Kuder says. “Different people will be doing different things in the future, repurposed to provide value-added activities that lead to better client services.”
These workforce changes are already occurring. “We’re definitely seeing movement in the market for hiring or reskilling individuals in the automation space,” Papageorgiou says. “This demand for talent actually exceeds the supply, which may be a factor in why some brokers are slow to adopt cognitive computing.”
Thanks to cognitive computing technologies, the services provided by the brokers of the future will become more important as the brokers shift toward more sophisticated advice. In this progression, mergers and acquisitions are likely, both among brokerages and with specific insurtech startups.
“These are exciting times for brokers to innovate and experiment,” Kuder says. “There’s a lot more degrees of freedom to choose where and how you want to play.”
Banham is a financial journalist and author. Russ@RussBanham.com