{"id":28641,"date":"2018-11-12T14:28:46","date_gmt":"2018-11-12T19:28:46","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-powering-the-insurance-industrys-buy-it-now-button\/"},"modified":"2018-11-12T14:28:46","modified_gmt":"2018-11-12T19:28:46","slug":"machine-learning-powering-the-insurance-industrys-buy-it-now-button","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-powering-the-insurance-industrys-buy-it-now-button\/","title":{"rendered":"Machine Learning: Powering the Insurance Industry\u2019s \u201cBuy It Now\u201d Button"},"content":{"rendered":"

Market Dynamics: Insuretech Competitors Emerging <\/strong><\/p>\n

Sales of individual life-insurance policies in the United States have declined more than 40% since the 1980\u2019s [1]. Meanwhile, the way one purchases life insurance has remained largely unchanged for decades. The traditional purchasing process is long and cumbersome, often including multiple interviews, lengthy paper applications and a medical exam. In all, it can take months for an applicant to be approved.<\/p>\n

Equipped with an increasing amount of digital data sources and supported by advances in machine learning technology, a number of up-start \u201cinsuretech\u201d companies (e.g., Bestow, Ladder, Ethos [2] in life and Lemonade, Slice Labs and Cover in the property and casualty market) are investing in algorithmic underwriting capabilities to support direct-to-consumer business models. In addition to eliminating friction in the purchasing process, start-ups hope to reduce operating expenses (i.e., selling and underwriting costs) through the implementation of artificial intelligence and machine learning.<\/p>\n

As a result of this perceived market opportunity and advances in technology, venture capital interest in insurance has skyrocketed. Per Willis Tower Watson\u2019s industry report, venture investments in insurance startups grew by more than 7x from 2013 to 2017 while the number of venture capital investors more than doubled. While some of these dollars are flowing into alternative technologies, a sizable percentage are related to artificial intelligence and machine learning.<\/p>\n

MassMutual\u2019s Response<\/strong><\/p>\n

To address these market dynamics in the short term, MassMutual, a Springfield-based life insurance mutual company founded in 1851, has established Haven Life (\u201cHaven\u201d). Haven is a direct-to-consumer affiliate company which has built an algorithmic underwriting capability that allows the company to quote and bind (i.e., issue) a policy in a matter of minutes. A Haven Life application has approximately 30 questions which Haven supplements with third-party data from sources such as motor vehicle records, prescription histories and the Medical Information Bureau [4]. In some cases, the model is insufficient and a human underwriter is needed. However, Haven expects human-reviewed applications to drop as a result of continually \u201creassessing the data and rules the service\u2019s AI relies on\u201d, per Chief Technology Officer, Todd Rogers.<\/p>\n

Building on this capability and to position the company for the medium-term, MassMutual has launched LifeScore Labs to distribute MassMutual\u2019s risk models to the market [5]. The models have been trained on a historical database of MassMutual life insurance applicants. In February and March 2018, LifeScore announced distribution partnerships with iPipeline, a provider of software solutions for the insurance and financial services industry, and SwissRe, a global reinsurer which will utilize LifeScore as an analytics option within its own underwriting system, respectively [6\/7]. Customers of LifeScore will have access to applicants\u2019 risk score, risk visualizations as well as details on contributing risk factors. If successful, LifeScore Labs will diversify MassMutual\u2019s sources of revenue and deepen LifeScore\u2019s data set which should in turn improve the models used by its customers and MassMutual.<\/p>\n

The Future <\/strong><\/p>\n

In addition to underwriting and distribution, machine learning will impact MassMutual\u2019s product innovation, asset management business and claims processes. As a result, expect the Firm to seek investment across the following categories to position itself for the future:<\/p>\n