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Monthly Archives: May 2017

Insurance as the Big Bad Wolf of Big Data

Max N. Helveston, Consumer Protection in the Age of Big Data, 93 Wash. U. L. Rev. 859 (2016).

In this insightful and well-researched article, Consumer Protection in the Age of Big Data, Professor Max Helveston arguably has opened stage two of a movement in contracts scholarship assessing the dangers and opportunities presented by large scale data aggregation for contract law and practice. Specifically, recent decades of contract scholarship have explored generalized issues surrounding information era contracting practices by producers with access to extraordinary amounts of data regarding their consumers. We could (but probably shouldn’t) refer this early stage as the “Oh crap! What does it all mean?” inquiry; it is probably better to stick with “Big Data & Contract 1.0.” That early stage examined the rapidly changing landscape of consumer-producer interactions in the early Internet and information-era context. The gist of Big Data & Contract 1.0 generally boils down to the proposition that consumers are largely screwed by the ability of producers to use data aggregation and analysis to bore down into consumers’ lives and preferences in a way never before possible in pre-information era contracting.

Despite the broad scope of the title, Consumer Protection in the Age of Big Data moves the discussion to “Big Data & Contract 2.0” by unpacking data analytics and aggregation in a specific contractual context: insurance. Insurance has always been problematic for contract law. The relationship between insurer and insured is traditionally perceived as a paradigm case involving gross inequality of bargaining power. The contracts involved are highly adhesive, consumers generally must depend upon insurance agents to select appropriate coverage and terms, and the resulting terms—which consumers often receive only weeks after they have purchased the insurance and will likely read only when a [hopefully] covered loss occurs—are highly technical and opaque to the typical consumer. This ground is well-traveled, and Helveston addresses the problem from a new angle.

Specifically, after surveying the broader scholarship on information era data aggregation and analysis as well as the privacy and data security concerns these practices raise, Consumer Protection in the Age of Big Data approaches the problem of insurance contracting by identifying the societal interests and norms surrounding insurance contracting. For Helveston, insurance contracts applying advanced data aggregation and analytics promise strong potential benefits. These benefits include increasing actuarial fairness by using Big Data to make more accurate assessments of each customer’s actual risk profile, creating incentives for consumers to reduce their risk profiles, and minimizing moral hazard problems.

But these practices also raise substantial concerns that insurers may use their market power and fine-grained insights into individual customers’ behavior to impair personal autonomy by requiring changes to behavior that reduce insurers’ exposure. Given the inelastic and non-substitutable nature of demand for consumer insurance products, insurers likely already have the ability to begin requiring insureds to agree to coercive lifestyle controls. As Helveston observes, “[i]f the datafication of the world becomes as extensive as some have projected, then a sword of Damocles might loom over many of individuals’ personal decisions.” (P. 33.)

Similarly, Big Data in the insurance industry creates substantial challenges for anti-discrimination norms. While states prohibit insurers from discriminating on the basis of race, religion, national origin, and other protected categories, aggregated data may provide numerous proxies that impose discriminatorily disparate impacts on consumers. As Helveston notes, wrongful discrimination may occur simply on the basis of machine learning algorithms that adjust premiums on the basis of factors correlated with protected classifications that nonetheless do not directly rely upon the insured’s membership in a protected class at all. Humans don’t need to discriminate; the algorithms may do it for us and without our knowledge or intent.

Helveston also notes that the ability of Big Data practices to provide potentially near perfect actuarial fairness in insurance contracts may ironically violate equality norms. As insurers develop more finely nuanced data regarding consumer risks, it becomes more likely that they will discover and price activities that correlate with risk. In this regard, Helveston observes, “[t]his is particularly disconcerting because many of the qualities that would lead insurers to confer beneficial treatment to an individual are not merely qualities that indicate that she possesses a low-risk profile, but are also qualities that cause one to receive more favorable treatment across social institutions.” (P. 33, n. 163.) Big Data practices thus threaten to exacerbate existing social and economic inequalities by pricing already disadvantages high-risk individuals out of the insurance market. In a related observation, insurers may similarly use such data practices to identify individuals least likely to contest a denial of coverage as well as to closely estimate the amount individuals would accept in settlement of their claims.

Helveston concludes by recommending federal regulation of consumer insurance contracts. In particular, Helveston proposes three key components for reform—community rating, policy content review, and prohibitions on consumer profiling. While I disagree substantively with the case for a single regulatory response to consumer protection in this area—the regulatory and government-induced market failures in the Obamacare/Affordable Care Act context suggest that the federal government has little expertise or competence in this area—Helveston makes a compelling case for reform.

This article is particularly important for the next stage of scholarship regarding data aggregation and analysis and its impact on contract law and practice. Helveston’s research is careful and his insights are challenging. More importantly, this article brings the Big Data & Contract 1.0 general field down to brass tacks by addressing a specific industry and the contracting practices that are impacted by Big Data practices. Information-era contract practice has matured, and this article calls us to examine the particularized implications of that field.

Cite as: Daniel Barnhizer, Insurance as the Big Bad Wolf of Big Data, JOTWELL (May 17, 2017) (reviewing Max N. Helveston, Consumer Protection in the Age of Big Data, 93 Wash. U. L. Rev. 859 (2016)),