Recently, my application for insurance for a classic car I'd bought was refused. It was a first for me and when I inquired why, I was told that the insurance company was concerned that I associate with'high-value individuals'. I don't, but even if I did, how could this possibly impact my access to insurance? The broker kindly investigated on my behalf and discovered that a robot -- or more accurately an'automated decision-making machine' -- used by the insurance company had scoured the internet and discovered that in the distant past I'd been the motoring editor of a national newspaper. I was no wiser as to why this might suddenly have made me a liability.
Earlier this year, 60,000 technology experts from 170 countries descended on Lisbon, Portugal, to take part in Web Summit, the world's largest tech conference. As part of Web Summit, I attended MoneyConf, an insurtech and fintech conference, where the world's leading insurance companies, banks, tech firms and disruptive startups met. Here, I spoke with industry leaders about how they were changing insurance's long-standing image problem and improving user experience (UX) to better serve customers. Customers are unlikely to do business with any company that doesn't give them a good experience. "Customers want simplicity, more clarity.
MetLife processes over 260,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult because the applications include many free-form text fields. The application contains questions that can be answered by structured data fields (yes-no or pick lists) as well as questions that require free-form textual answers. Currently, MetLife's Individual Business Personal Insurance unit employs over 120 underwriters and processes in excess of 260,000 life insurance applications a year.
American Airlines (Fort Worth, Tex.) has utilized speech-recognition technology to enhance its automated flight information system, AI Magazine Volume 20 Number 1 (1999) ( AAAI) consortium representing 60 percent of all newspapers circulated in the United Kingdom, has developed an intelligent agent-based classified advertising web site. The ADHunter system collects and indexes as many as 1 million automobile, help wanted, and property classified ads. Heritage Mutual Insurance (Sheboygan, Wis.) is using intelligent software to improve its underwriting process. The company's rule-based paperless personal lines processing application automates the procedure for evaluating and issuing a variety of insurance products. David Blanchard is the editor of Intelligent Systems Report (Cuyahoga Falls, Ohio; www.isreport.com),
Which works better for modeling credit risk: traditional scorecards or artificial intelligence and machine learning? Given the excitement around AI today, this question is inevitable. It's also a bit silly. While some new market entrants may have a vested interest in pushing AI solutions, the fact is that traditional scorecard methods and AI bring different advantages to credit risk modeling -- if you know how to use them together. Take, for example, our new credit decisioning solution, FICO Origination Manager Essentials – Small Business.
So, how do companies find ways to address the ever-increasing customer needs? While the adoption of new technologies might have been slower than desired initially, the Indian insurance sector is certainly awakening to its benefits now. Several insurers are now deploying these processes to understand their customers better and for product innovation. Of all these news processes, perhaps artificial intelligence and machine learning are proving to be the most potent! Information overload New data sources like third-party databases, social media activity, internet of things, and more are providing a steady stream of information.
Insurance companies normally place artificial intelligence, the Internet of Things and big data into individual buckets. Yet, chief information officers should invest in all three at once, according to new research from Novarica, as they are equally dependent on each other for best results. In an executive brief, "Big data, IoT and AI Maturity levels," the consulting firm argues that while AI is the next stage of advanced analytics decision making, big data and IoT--in the form of sensors, drones and wearables--feed machine learning platforms the information needed to help insurers make smarter claims and underwriting decisions. "All three of these emerging technologies are tightly intertwined. In fact, it's difficult to recognize the future value of one without the others," said Jeff Goldberg, SVP of research and consulting at Novarica, the study's author.
Founded in Silicon Valley in 2009, Health 2.0 is a international organization dedicated to health innovation and made up of 90 chapters. The Berlin chapter has over 1000 members from healthcare, insurance companies, the pharmaceutical industry, startups, and eHealth enthusiasts. On October 25th, 120 interested people gathered at Spielfeld in Kreuzberg, Berlin to hear a forecast of the German healthcare system over the next 3 years. The Berlin Health 2.0 chapter event panel represented a wide spectrum of players in German healthcare: Yvonne Gründler from a totally new digital private health insurance company; Jonas Pendzialek, a consultant for digital health transformation of public insurance; Markus Dahlem who cofounded an app for headache treatment; and pharmaceutical veteran, Dr. Hardy Kietzman. Together they painted a picture that was both encouraging and honest.
In the fall of 2016, Oliver Buechse, a Green Bay-based strategy consultant, attended a conference in Silicon Valley with a focus on disruption in the financial industry. Interacting with the artificial intelligence and fintech community, Buechse noticed something different about the discussions there. Concepts like artificial intelligence and machine learning weren't theoretical, far-off possibilities, but rather present realities. AI, clearly, had already arrived on the West Coast. "All of California was abuzz about AI," Buechse said.
This application of population health AI data will occur only if the EHR companies can profit from the function by charging the physicians for the tabulated population data analysis. Without concomitant software to overcome prior authorization rationing of prescriptions by insurance companies and Pharmacy Benefit Managers or built-in EHR software to override diagnostic and treatment rationing by insurance bureaucrats, the benefits of AI clinically for the patient or physician will never be applied at the bedside. This function of automated overriding of prior authorization rationing of Artificial Intelligence (or NAI) suggestions could be easily delivered to physicians simply by cross-linking insurance company drug formularies with patients insurance plans using several prescription tracking companies already contracted with EMR companies and used daily in most pharmacies. I'm betting, the low earnings and low profitability potential of prior authorization API overriding software for the EHR industry combined with data (price and formulary) blocking by Pharmaceutical Industry Benefit Managers (PBM's) and the insurance companies will prevent implementation or this most desired clinical function.