A McKinsey survey looking at the banking, auto insurance, retail energy, health insurance, and mobile communications sectors found that the quality and availability of digital interactions have a significant impact on customer satisfaction. Adding digital offerings is crucial to what consumer-facing companies must do to remain competitive in the face of increased customer expectations. However, some organisations still only offer basic digital services, and not all have created integrated, omnichannel experiences. Companies that use technology to transform customer experience have increased customer satisfaction by 15 to 20%, reducing cost to serve by 20 to 40%, and boosting conversion rates and growth by 20%. As consumers have come to expect the same experience of their financial services providers that they have elsewhere in their lives, traditional financial institutions (FIs) are increasingly looking for ways to improve customer service and deepen engagement.
On June 30, the National Association of Insurance Commissioners' (NAIC) Artificial Intelligence Working Group approved principles that call on insurers and others using artificial intelligence (AI) to take proactive steps to avoid proxy discrimination against protected classes. In the run-up to the vote, Commissioner Jon Godfread (Chair – ND) delivered a passionate statement indicating that the nation's insurance regulators are committed to addressing discriminatory practices while preserving the industry's ability to engage in appropriate underwriting. After extensive discussion, 15 of the states participating in the call voted in favor of the principles, and one abstained. The AI principles will be considered by the NAIC's Innovation and Technology Task Force on July 23 and, if approved, by the full NAIC membership during the virtual summer national meeting. While the principles will not have the effect of law, they are expected to serve as a road map for future regulatory workstreams.
Pricing: Through predictive models (with algorithms such as random forest, linear regression, xgboost, etc.), we can provide insurance premiums in a more dynamic and precise way. More specifically, they can be personalized according to driving habits, geographic area and commute distance. To the traditional price-setting variables, a new set of variables are added to improve the profitability of the portfolio. These variables depend on the company's own needs/capacities and can range from competitors' prices to the policyholder's traffic record, driver's license age, credit score, as well as external data systems and sources. The interesting thing here is the dynamism in determining the price; the models change based on data inputted over time, then recognize patterns and adjust the rate autonomously.
Smart IT systems are now calculating claims costs and attributing fault for accidents without any human involvement, speeding up the resolution of claims. Technology is set to transform motor insurance in the next five to 10 years, revolutionising both the claims process and repair. Artificial intelligence (AI) is enabling insurers to evaluate vehicle damage at the scene of a collision, without the need for a claims handler or loss adjustor. By analysing millions of photos of vehicle damage and cross-referencing them with actual repairs, programmers have been able to create algorithms that can assess the scale of the damage and create a full estimate including recommended repair, paint, parts costs and labour hours. The system can determine, for example, whether body panels can be repaired or need replacing, and in worse case scenarios it ensures that no total losses are sent to bodyshops.
Brokerages who use artificial intelligence could find opportunities to upsell based on changes in a client's lifestyle, according to a software vendor executive. The more data you feed a machine learning model and the more you train it, the better it gets, said Kevin Deveau, managing director of FICO Canada, part of San Jose, Calif.-based Fair Isaac Corp., in a recent interview. Artificial intelligence (AI) is when technology mimics human cognition such as learning from experience, identifying patterns and deriving insights, said Mark Breading, a partner with Boston-based Strategy Meets Action. Machine learning is a type of AI in which computers act without being explicitly programmed, SAS Institute Inc. notes. Bigger brokerages with enough money to invest in AI and machine learning could use those technologies to build a "360-degree view" of a customer, said Deveau, in the context of how the COVID-19 pandemic is forcing companies to change the way they operate.
Kaia Health, a digital therapeutics startup which uses computer vision technology for real-time posture tracking via the smartphone camera to deliver human-hands-free physiotherapy, has closed a $26 million Series B funding round. The funding was led by Optum Ventures, Idinvest and capital300 with participation from existing investors Balderton Capital and Heartcore Capital, in addition to Symphony Ventures -- the latter in an "investment partnership" with world famous golfer, Rory McIlroy, who knows a thing or two about chronic pain. Back in January 2019, when Kaia announced a $10M Series A, its business ratio was split 80:20 Europe to US. Now, says co-founder and CEO Konstantin Mehl -- speaking to TechCrunch by Zoom chat from New York where he's recently relocated -- it's flipped the other way. Part of the new funding will thus go on building out its commercial team in the US -- now its main market.
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. MetLife's intelligent text analyzer (MITA) uses the information-extraction technique of natural language processing to structure the extensive textual fields on a life insurance application. Knowledge engineering, with the help of underwriters as domain experts, was performed to elicit significant concepts for both medical and occupational textual fields.
Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on artificial intelligence. As there are many startups working on various different applications, we want to share our insights with you. Here, we take a look at 5 promising genetic algorithm startups. For our 5 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 111 relevant solutions.
It goes without saying that everybody is interested in adopting Artificial Intelligence and that too on a larger scale. However, based on global surveys, almost 70 percent of newer startups aren't using a lot of AI tools with their absence having minimal impact on the growth of the concerned organizations. That said, while some aren't sure about the effectiveness of this technology, we feel that this untapped resource isn't being utilized in a desirable manner. This is why we thought of sharing our insights regarding the latest and upcoming AI trends that could be strategic inclusions, especially in the next few years to come. Artificial Intelligence will only be able to make the necessary inroads if enterprises can realize the best implementation strategies leading to the same.
Mike Tyson famously said that "Everyone has a plan until they get punched in the mouth". Every company had a strategic plan coming into 2020. Then, Covid-19 walked into the ring. Insurance has been hit hard by Covid-19 and economic hardship. With many insurers focused on cash conservation, leading insurers can emerge from the crisis even stronger if they make smart investments in AI. Insurers' massive customer datasets and their famously manual processes create some'quick win' AI opportunities.