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.
The AI consultant, dubbed "Frankie", joined NIB's customer service team in a bid to help provide convenient, timely responses to health cover-related customer enquiries. "The idea behind it is really so we can bring a greater level of choice and a greater level of service to our customers," Mills said, noting that Frankie's responsibilities will be built on as the cognitive learning kicks in. In parallel to that, Mills said NIB is also developing some AI and chatbot technology for its Australian domestic health insurance business and is currently running a pilot based on Amazon Web Services' (AWS) Lex, with the final bot expected to be launched in the near future. "We have made a heavy investment in cloud; a lot of our digital footprint -- our systems of engagement -- are delivered through an AWS platform," Mills explained.
Eyewitness News learned many big name companies like Farmers Insurances rely on the new technology to help process claims. Brent Hazen deployed a drone in Sienna Plantation Thursday afternoon. We currently have seven drones in the Houston area," said Hazen. They do plan to use them to help process claims in the Houston area.
A Chicago startup has raised $2.5 million in hopes of using artificial intelligence and Big Data to help insurance companies make smarter underwriting decisions more quickly. "I wanted to create a Big Data solution to solve a tangible business problem; my friend Harish is an expert in commercial insurance," Malik says. They're trying to disrupt the commercial insurance business by using artificial intelligence and machine learning. But he estimates there are more than 800 commercial insurance companies "who don't have the investment in the space that's required."
In today's day and time while most organizations are busy revamping their Policy administration systems which were long ready to be replaced a decade ago, what will set companies apart will be the organizations that start considering Machine Learning and Artificial intelligence(AI) for their core systems. In every type of insurance product the claims experience influencing the pricing and risk aggregation decision making done by the insurer. If the dots are connected and the data patterns understood and logic applied there are certain decision making aspects that can move away from people to machines and over time evolve to largely autonomous ecosystem. So before we set the drones to fly and change the commercial insurance ecosystem, Machine learning and AI need to be adopted into mainstream core software platforms.