我们一直在紧密关注保险科技领域，但人工智能一直被认为会先取代传统的财富管理顾问，再取代保险代理商。尽管如此，来自MIT的创业公司Insurify发布了人工智能虚拟保险代理人Evia（Expert Virtual Insurance Agent，虚拟保险代理专家），它可以通过一张车牌照片为你找到更好的汽车保险。
不仅如此，这家创业公司还宣布获得了200万美元的种子轮融资，由Rationalwave Capital Partners领投。这一数额虽大，但与汽车保险行业1600亿美元的潜在规模相比，实在是小巫见大巫。
An InsuranceTech startup from Massachusetts has just replaced a human with AI. If you are in the car insurance industry, this might be the right time to start a transition into other field.
We have been looking at InsuranceTech attentively, but AI was really expected to replace traditional wealth management advisors before it replaced insurance agents. Nevertheless, Insurify, a startup out of MIT, announced the launch of Evia (Expert Virtual Insurance Agent), an artificially intelligent virtual insurance agent that aims to find you better car insurance using a photo of your license plate.
In addition to that, the startup also announced a $2 million seed funding round, led by Rationalwave Capital Partners. But even $2 million in funding fades against the potential opportunity in the $160 billion car-insurance industry.
Insurify’s system is already operating in 30 states, and the company can offer quotes from 82 insurance carriers, while Evia is available in a limited public beta.
Evia is a bot that will provide you a tailored quote after you snap a licence plate and send to Evia via text. Just like a human, Evia will ask some questions to verify your identity and will ask whether you own or lease the car. Then Evia can start sending quotes for plans that would be a good fit for you.
There is more. If there is something you don’t understand, certain terms, for example, you can send your question to Evia and she will answer based on her own knowledge. In the unlikely scenario that Evia doesn’t know something, a human will respond on her behalf. Admittedly, we were a little dramatic in saying that Evia has replaced a human as after she helped you to choose an insurance plan. The last step is still completed by a human, but the essence of the car insurance agent’s role in advising and communicating benefits is definitely lost.
Evia has an inherent superiority over human memory as just by ‘looking’ at a snap of your license plate she will instantly brush through millions of records to verify personal information and driving history and then deliver policy quotes and recommendations back to you via text message.
Tailored and accurate insurance in a snap appears revolutionary. Nonetheless, there is a drawback not missed by picky minds. Can Evia become a tool for criminals to get private information about a car owner? Well, Evia is smart enough to provide only a quote for a particular car that the user took a snap of without giving away the owner’s data.
Another interesting example may soon be hidden in your Facebook messenger. M, an AI-powered personal assistant, is an entry ticket for Facebook into AI and the personal assistant space. In October last year, Facebook started testing it in a small beta, as reported by BI. Excitingly scary, M is part AI and part human and as stated by BI, the promise of M being a revolutionary and mysterious hybrid of man and machine. As Facebook shared, M’s AI is “based on two old-school algorithms that form the statistical and probabilistic backbone of many automation services today.” With seemingly unlimited funds available for M from its parent and sophisticated trainers, M is not far from moving out from Messenger to the bank.
Players in lending, trading and fraud detection are already using machine learning to provide services. And of course the wealth management sector is the one most vulnerable to AI (or to benefit from it depending on your point of view) as machines like IBM Watson are learning at a rapid pace leaving humans far behind.
Notable FinTech players like Affirm, ZestFinance, BillGuard, Lending Club, Kabbage and LendUp are using machine learning for accurate decision making and predictive analysis. Affirm, for example, is mining vast amounts of data to successfully rewrite the rules on how credit is evaluated. To protect against fraud and build credit data, the company uses machine learning models.
ZestFinance uses machine learning techniques and large-scale data analysis to consume vast amounts of data and make more accurate credit decisions. ZestFinance takes an entirely different approach to underwriting by using machine learning and large-scale big data analysis.
BillGuard has expertise in big data mining, machine learning algorithms, security and consumer Web UX. The Kabbage team specializes in building the next-generation machine learning and analytics stack for building credit risk models and analyzing the existing portfolio.
According to investment bank, Bank of America Merrill Lynch, the total global market for robots and artificial intelligence will reach $152.7 billion by 2020, with estimates that the adoption of these technologies could improve productivity by 30% in some industries. Along with fascinating opportunities, there are potential dangers for the human workforce, one of which is possible massive unemployment of low-level sales representatives and consultants.