Artificial intelligence, Machine Learning, and Deep Learning are more than futuristic concepts. These technologies are impacting the insurance industry in a significant way right now and this impact is likely to increase in the near future. The idea of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) may fascinate consumers who enjoy talking to their digital while admiring a Nest thermostat. But for the insurance industry, these terms are business-changers that affect products and services offered and interactions with consumers and other industry partners. The definitions of these terms may be a bit confusing to the uninitiated (see sidebar).
Vitality, a UK-based health and life insurer, offers'Vitality points' for consumers who are willing to track and share their daily activities, including walking, running, cycling, swimming or going to the gym. This can be done in a variety of ways, including fitness trackers and health check-ups. An example of this may be someone identified as having a genetic disorder, where no preventative measures could have been taken, being given a large life insurance quote to reflect the uncertainty around the treatment and risk factors. This could reduce profitability for insurers and further undermine the risk pool, pushing up premiums for the higher- risk consumers that remained.
You may have heard the terms analytics, advanced analytics, machine learning and AI. If you're in insurance, here's how to make sense of the terms analytics, advanced analytics, machine learning and AI. AI can learn patterns of driver behavior, improve risk calculation and personalize policy terms in response. According to the McKinsey Global Institute, an insurer may drop risky driver behavior by 53 percent by personalizing risk calculation.
Across Europe, insurers spend billions of dollars every year in just claim processing costs, and they need help changing this, according to Lex Tan, founder of insurtech startup MotionsCloud. Tan is one of more than 20 startup founders discussing the insurance market at the annual Intelligent InsurTECH conference on October 3 in London. The startup utilises image recognition technology and deep learning technology to identify damage through photos. MotionsCloud is one of more than 20 insurtech startups attending Intelligent Insurer's Intelligent InsurTECH conference on October 3 in London.
The insurance disruption space hasn't seen nearly as much activity as fintech, but 2017 has seen the trinity of technological trends - machine learning, AI and Big Data - cross over and fuel the motor of change within InsurTech. As well as the goal of customer retention, the digitisation of customer experience keeps operational costs down and requires little manpower, whilst having digital and cloud based technology makes insurance services better able to cope with an increasingly demanding consumer base who want access to services anywhere and at any time. "More than machine learning", Alberto explains, "we could speak of human learning - both the insurer and SPIXII learn more (and often unexpected) from the behaviours of the customers and apply changes and adjustments in order to increase KPIs". It auto generates an insurance claim, verifies it against its blockchain ledger, and pays its users if the claim is correct.
Perhaps the biggest advantage of exploring these technologies is that insurers now have more touch points with a broader demographic of customers; giving them the data needed to create bespoke packages that justify the cost of service. The second hasn't yet bought into digital disruption, and the focus is on making sure the core set of services is working for the customer. To be able to respond to the concerns being voiced by consumers, and to harness the business agility needed to respond to market trends, insurance businesses from the c-suite down need to make a culture shift. By mirroring this innovation with new internal processes, and by aligning innovation teams with those looking after the core business offerings, the face of insurance will change as we know it.
The cognitive machine learning algorithms have reached a 75 percent accuracy rate for detecting fraudulent insurance claims. With better data, both customers and insurers benefit, she said, because insurers can develop better products based on more accurate assessments, and customers will pay for exactly what they need. The more standard, predictable claims are handled by machine learning algorithms, Breen said, and the human underwriter is essentially fine-tuning the entire process and intervening in cases that need higher-order decision-making. She expects that the number of applications a human underwriter will be required to handle will significantly drop as machine learning makes even more of a foray into the insurance industry.
With the aid of AI workflows, it is also possible to develop applications which enable the early recognition of losses, e.g. Munich Re has already successfully piloted such an application: "Our Early Loss Detection (ELD) platform continuously evaluates over 16,000 news sources and searches for previously defined loss events. The risk management platform M.I.N.D., another successful pilot project already successfully in use on the market, helps insurers, reinsurers and loss adjusters to create transparency on risks together and better appraise these.
Property insurers are preparing to fly dozens of drones over homes and businesses to assess damage in the wake of Tropical Storm Harvey, the first widespread use of unmanned aircraft to size up catastrophe claims. To be sure, insurance adjusters will still be climbing on thousands of roofs to inspect damage in person. State Farm, the largest homeowners' and personal car insurer in Texas, isn't currently using drones in its Harvey claims handling, a spokeswoman said. Private-sector commercial property insurance does often cover flooding, and claims costs for those insurers are expected to total billions of dollars in the Houston area.