Disclosure 3.0: Making Disclosure Smarter

What if, instead of making the consumer smarter or the disclosures more comprehensible, as discussed in my last several posts, we made financial product disclosures smarter? For the uninitiated, “smart disclosure,” according to the federal White House Task Force on Smart Disclosure, is “the release of data sets in usable forms that enable consumers to compare and choose between complex services.” The Task Force description continues: “Smart disclosure requires service providers to make data about the full range of their service offerings available in machine-readable formats such that consumers can then use these data to make informed choices about the goods and services they use. While consumers may access the data directly in some cases, the data may also be useful in enabling government agencies or third parties to create online tools for consumer choice.” 

The idea is that both the government and firms will be required to release, in close to real time, complete price, feature, and performance data about products and services offered by the firm or government entity (“product data”) so that consumers can input their own preferences into on-line or mobile app tools (“infomediaries”) that can then recommend the products or services that will best meet those preferences. Kayak for everything! 

But smart disclosure can go further, requiring the release of data about an individual consumer’s past product or service use history (“personal use data”). As Cass Sunstein, in a memo issued when he was head of OIRA at OMB, has explained, smart disclosure might improve consumer decisionmaking “by informing consumers about the nature and effects of their own past decisions (including, for example, the costs and fees they have already incurred).” 

A straightforward example would be a requirement that all credit card issuers publicly release detailed information about the price structures of each card they issue (annual fees, interest rates, late fees, foreign exchange fees, etc.) and privately release to each of their own customers that customer’s card usage and payment history so that, for example, an infomediary tool or app could forecast which card currently offered would be cheapest for the consumer in the next period, assuming that the consumer’s usage patterns remained constant. Emir Kamenica, Sendhil Mullainathan, and Dick Thaler have called this RECAP – Record, Evaluate, Compare Alternative Prices. To help consumers optimize on a broader range of features then just price, Tom Baker, Eric Johnson, and Ran Hassin have suggested a similar approach for state health exchanges established under the Affordable Care Act: the exchanges would take the consumer’s own ranked health plan attribute preferences (cost, quality rankings, doctor participation in the plan, etc.) and past health care history and use these to recommend a health plan offered by the exchange. 

For smart disclosure to reach its full potential, I would add to product data and personal use data a third type of information not discussed in the literature: data about the product and service experiences of other consumers who share statistically predictive characteristics with this consumer (“predictive data”). As Jon Hanson and Doug Kysar have explained well, a major source of
information asymmetry between firms and consumers is not firm
omniscience about what a particular consumer will do in the future, but firm knowledge of how consumers like this consumer tend to act. It is this predictive data that firms can frequently use to make better
forecasts about consumers’ behavior than consumers make about
themselves.

At a minimum, predictive data would be needed for situations where the consumer has no past use history. But it also could be useful when a consumer’s past use history is unlikely to be very predictive, such as when the consumer has a major life change or the product under consideration differs significantly from the product used by the consumer in the past. For example, someone who has a fixed rate mortgage and is considering refinancing into an ARM would benefit from advice that takes into account not only the individual's own past payment history but also the predictions the lender makes about how payment patterns for a consumer with these characteristics change when monthly payments are no longer fixed. 

Timely, accurate, and complete product data, personal use data, and predictive data, when fed into independent and reliable infomediary tools, could provide consumers with personalized, statistically-based advice about which credit products to buy, taking into account all of the alternatives available in the market. 

In theory, these tools would relieve consumers of the burdens of locating all alternatives, understanding disclosures, engaging in complex calculations, and overcoming their own cognitive biases. For example, without smart disclosure a consumer who has paid many late fees in the past might nonetheless consider only a few familiar brands of credit cards and then select whichever card has the lowest annual fees even if that card imposes high late fees. Perhaps the consumer has forgotten her own record, hopes that she will turn a new leaf and start paying bills on time, or simply cannot determine how to incorporate potential late fees into her decisionmaking. An infomediary might scour the market and select for her instead a high-annual-fee, low-late-fee card. And an infomediary could do so at a cost much lower than the price of formal financial advice. Billshrink.com, for example, although it relies on consumers to report their own credit card usage patterns, currently attempts to provide this type of advice at no apparent consumer cost. 

Disclosure 3.0 takes thus aim at the second and third of the four fundamental problems with disclosure that I identified in my earlier post, sidestepping the need for consumers to research, locate, and understand the offerings in the market or, in most cases, to predict their own likely future behavior. It has the potential to facilitate comparison shopping, respond quickly to changes in the market and in consumer preferences, perform unbiased predictions for consumers, and aggregate total likely product or service costs. Even if consumers never used smart disclosures in any great numbers, laws requiring the data releases that would be needed to make smart disclosure work could also force some degree of standardization in product structure and pricing schemes, and this alone might assist consumers in comparison shopping. 

However, smart disclosure also presents perils. Releasing individual consumer data, even if the data is intended to be used only by the consumer herself, holds the potential for a range of privacy violations. The expense of data management could be a barrier to small firm entry.  Releasing what has heretofore been treated as proprietary data, and predictive data in particular, could have anticompetitive effects.  Standardization can hamper innovation.  

It also seems likely that firms will game the system, just as they do with internet search engines today. Firms will have an incentive to withhold or misreport data so as to move their offerings to the top of infomediary recommendation lists and to keep profitable consumers  Ensuring accuracy and completeness in credit reporting alone is something we do poorly (as the FTC recently detailed); ensuring accuracy, completeness, and timeliness in disclosure of everything smart disclosure might require would be much more challenging. Third-party infomediaries could become corrupted (as allegedly occurred with Expedia), using consumer data to target consumers for particular products or pricing schemes rather than being used by consumers to target the best products or pricing.

Finally, smart disclosure does not control consumers’ decision frames and resources. As David Laibson has pointed out, not all consumers will access smart disclosure tools, particularly those on the wrong side of the digital divide (you may recall that I do not own a smartphone). To the extent that competition leads firms to give better deals to sophisticated consumers who use smart disclosure, firms may shift more costs onto less sophisticated consumers, such that less sophisticated consumers might subsidize product use by more sophisticated consumers.

Where it is a government service or a product fairly tightly controlled by the government, such as health insurance plans on a health exchange, smart disclosure has lots of potential, provided that privacy concerns are managed well. But for consumer financial products, the perils need to be addressed before we embrace smart disclosure.