Laura Favinger, chief administrative officer of the Insurance Information Institute, talks about the III's historic mission and the importance of thought leadership to continue to inform and educate consumers on how technology and innovation can help insurers mitigate new risks.

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Pranav Pasricha, CEO of Intellect SEEC, discusses the company's goal of helping insurers keep the insured top of mind in their innovation efforts, and maintaining that view throughout the evaluation, development and integration of new technologies.

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Zia Zaman, Chief Innovation Officer, Asia for MetLife and CEO of its LumenLab subsidiary, talks about the importance of changing the mindset of incumbent insurance companies for change and innovation to occur.

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Nick Leimer, Principal Insurance Industry Lead of Microsoft's Azure for Insurance, talks about his company's mission to support the insurance industry's innovation efforts with solutions like Azure cloud solutions

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Grace Vandecruze, managing director and founder of Grace Global Capital, talks about insurance industry M&A trends, and how technology of tomorrow will impact the ability of companies to compete and thrive in the future.

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Jamie Yoder, president of Snapsheet, talks about how his career helping advise insurers on how technology and information impacts their strategies has positioned him for a new role in putting that theory into practice to help Snapsheet serve its customers.

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Matt Mosher, Executive Vp and Chief Operating Officer of A.M. Best Co., talks about progress in the company's goal to include an analysis of innovation in its ratings of insurers.

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Mark Chester, CEO and Founder of Ley Line Advisory, discusses the company's goal to help insurance companies be more agile in adoption of new technologies and digital opportunities.

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An unmanned car driven by a search engine company? We’ve seen that movie. It ends with robots harvesting our bodies for energy.

That is a line from a 2011 Chrysler car commercial mocking Google’s self-driving car project. Another Chrysler commercial was even blunter: “Robots can take our food, our clothes and our homes. But, they will never take our cars.”

Chrysler’s early mocking of Google’s efforts exemplifies the fact that few cling to the status quo tighter than the companies that best understand it and have the most stake in preserving it. It is human nature to value what one does well and look askance at innovations that challenge the assumptions underlying current success.

Sprinkle in some predictably irrational wishful thinking and you have the mindset that too quickly dismisses potentially dangerous disruptions. Ironically, seven years later, those Google “robots” are now mostly driving Chrysler Pacifica minivans. Those robots have taken Chrysler's cars and driven more than 10 million miles.

Chrysler benefits by selling cars to Waymo, the spinoff from that Google project, but not nearly as much as it might have from building the robots themselves. Waymo is valued at $175 billion, about five times Chrysler’s market value.

History brims with other examples. When Alexander Graham Bell offered to sell his telephone patents to Western Union, the committee evaluating the deal concluded:

Messrs. Hubbard and Bell want to install one of their 'telephone devices' in every city. The idea is idiotic on the face of it… This device is inherently of no use to us. We do not recommend its purchase.

Ken Olsen, who disrupted IBM’s mainframe dominance with his DEC minicomputers, mocked the usefulness of personal computers in their early days. He declared, “The personal computer will fall flat on its face in business.” Olsen was very wrong, and DEC would eventually be sold to Compaq Computer, a personal computer maker, for a fraction of its peak value.

See also: Why AI IS All It’s Cracked Up to Be 

Steve Ballmer’s initial ridicule of Apple’s iPhone is also legendary, though the words of the then-CEO of Microsoft were mild compared with the disdain on his face when asked to comment on the iPhone launch. Years later, after he retired, Ballmer insisted that he was right about the iPhone in the context of mobile phones at the time. What he missed, he admitted, was that the strict separation of hardware, operating system and applications that drove Microsoft’s success in PCs wasn’t going to reproduce itself on mobile phones. Ballmer also didn’t recognize the power of the business model innovation that allowed the iPhone’s high cost to be built into monthly cell phone bills and to be subsidized by mobile operators. (Jump to the 4:00 mark.) The biggest challenge for successful business executives—like Ballmer, Olsen and those at Western Union—when confronted with potentially disruptive innovations is to think deeply about potential strategic shifts, rather than simply mock innovations for violating current assumptions.

Another perhaps soon-to-be classic example is unfolding at State Farm Insurance. State Farm released a TV ad that is a thinly veiled attack on Lemonade, a well-funded insurtech startup. Lemonade makes wide use of AI-based chatbots for customer service. State Farm, instead, prides itself on its host of human agents. In the ad, a State Farm agent says:

The budget insurance companies are building these cheap, knockoff robots to compete with us… These bots don’t have the compassion of a real State Farm agent.

As I’ve previously written, AI is one of six information technology trends that is reshaping every information-intensive industry, including insurance. In fact, as I recently told a group of insurance executives, I believe insurance will probably change more in the next 10 to 15 years than it has in the last 300.

See also: Lemonade Really Does Have a Big Heart 

That doesn’t mean that Lemonade’s use of chatbots for customer service will destroy State Farm. But, as State Farm should know, customer-service chatbots are only one of numerous innovations that Lemonade is bringing to the game.

As several McKinsey consultants point out, AI-related technologies are driving “seismic tech-driven shifts” in a number of different aspects of insurance. Lemonade has also adopted a mobile-first strategy and is applying behavioral economics to drive other business model innovations. State Farm executives need to get beyond the mocking and think deeply about how emerging innovations might disrupt their strategic assumptions.

One way to do so is being offered at InsuranceThoughtLeadership.com, where ITL editor-in-chief and industry thought leader Paul Carroll has offered a “State Farm Lemonade Throw Down.” Carroll offers to host an online debate between the two firms’ CEOs about how quickly AI technology should be integrated into interactions with customers. Lemonade’s CEO, Daniel Schreiber, has accepted. I hope Michael Tipsord, State Farm’s CEO, will accept, as well.

Better for Mr. Tipsord to face the question now, while there is ample time to still out-innovate Lemonade and other startups, than to be left to reflect on what went wrong years later, as Steve Ballmer had to do with the iPhone.

How do you increase quality in claims assessment, management and administration? We share insights into an end-to-end, AI-powered, claims-automation approach to increase quality, improve processing efficiency and reduce cost.

In this blog series, I’ve spoken about how AI increases process efficiency, reduces costs and helps business solve problems. I also showed how it enables smart business transformation by creating intelligent processes at every step along the value chain and intelligent products and services in the market. In my previous post, I illustrated how insurers can use AI-related technologies in underwriting and service management. Now, I’ll explain how AI helps insurers to manage claims more effectively and efficiently. How can insurers use AI in claims management? AI technologies make information systems more adaptive to humans and improve the interaction between humans and computer systems. By doing this, AI gives insurers an edge on how they manage claims—faster, better and with fewer errors. Insurers can achieve better claims management by using the intelligent technologies in some of the following ways:

• To enable a real-time question-and-answer service for first notice of loss;
• To pre-assess claims and to automate damage evaluation;
• To enable automated claims fraud detection using enriched data analytics;
• To predict claim volume patterns;
• To augment loss analysis

What are the benefits of using AI for claims management? In our  2017 Technology Vision for Insurance , we highlighted how Fukoku Mutual Life Insurance in Japan is using the IBM Watson Explorer AI platform to classify diseases, injuries and surgical procedures as well as to calculate claims payouts. See also: Insurers: Start Boosting Your ‘AIQ’   Our research has shown automated machine classification can be 30% more accurate than manual classification by humans and has the potential to increase productivity by 80%. AI-related technologies can enable higher quality in claims assessment, management and administration. It also supports improving the predictability of reserves and fraud. Smart machines can pre-assess claims and automate damage evaluation. Machine learning enables insurers to classify claims via email in the case of an accident or if medical care is required. It’s fast, accurate, efficient and simple to use. Case Study 1: Cognitive health insurance claims process management We have conducted a pilot with one of our insurance clients on the application of AI to its health insurance claims processes. This insurer’s health claims management process took about 11.5 minutes from receipt of the claim to updating it and closing the record. Scanning the paper documents and uploading them into the portal where they were categorized were the first manual steps. It took roughly five minutes to analyze the data, another five to verify rejection rules and one and a half minutes to accept or reject the claim. With our machine learning solution in place, a fully automated process was enabled and took only three minutes to do the same amount of work. This represents a 74% reduction in the claims settlement time. Furthermore, the machine learning technology applied was able to process health claims with 80% accuracy. The other 20% are incorrectly processed owing to spelling errors or database limitations. However, machine learning technologies are able to store and recall those errors for more accurate claims processing in the future. Case study 2: AI-powered automation of automobile claims processing Accenture was recently part of a major client initiative to identify technologies and partner for an AI-driven automation journey. We proposed and built a solution to automate processes to extract and classify data from commercial automobile claims PDF documents. The client faced many challenges, including having fewer than 400 records to classify 55 unique cases, and these records were mismatched and labeled inconsistently. The client also received scanned images containing text, owing to the redaction process followed to ensure data privacy. We developed an on-premise solution using a combination of IBM offerings and open-source technologies that enabled a detailed analysis of training data. The solution also helped the client to identify quality and sparse/skew data and to test various approaches to maximize performance. In the end, a blind data set of 207 claims documents was processed within a four-hour assessment window, and we were able to process claims PDF documents with scanned images as well as text, including several formats and layouts not part of training data. See also: How to Use AI, Starting With Distribution   We identified several pain points in the current claims management process:

• Error-prone manual data extraction;
• Inconsistent claim classification;
• The need for additional downstream validation;
• Increased time and cost for processing and resolution.

Solution proposed: In the next post, I’ll look at how AI-related technology can be used to improve customer services and policy administration. Get in touch to find out how you can use AI in the entire insurance value chain, or download the How to boost your AIQ report.