Working Papers

Black Box Credit Scoring and Data Sharing [Paper]
Abstract: Should credit scoring algorithms be transparent or opaque? I study this question in a model where a lender uses data shared by borrowers for allocating and pricing credit, and is privately informed about how the algorithm maps data to allocations. I show that revealing the algorithm's parameters exposes it to gaming in the form of strategic withholding of unfavorable information. Under opacity, data withholding emerges as a prudent strategy against the unpredictability of the black box and the risk of credit rationing. The lender's optimal transparency regime maximizes data collection and is socially inefficient as it results in excessive credit rationing.  Algorithmic opacity can be welfare-improving as it reduces the stigma around data withholding, thereby expanding credit access for privacy-concerned borrowers. I analyze the distributional impacts of recent algorithmic transparency regulations and offer policy recommendations.

Market Information in Banking Supervision: the Role of Stress Test Design [Paper] (R&R at The Review of Financial Studies)
(with Haina Ding and Alexander Guembel)
Abstract: The Basel committee views market discipline as complementing banking supervision. This paper studies how supervisors should design stress tests when markets discipline banks via price signals their traded securities provide to bank creditors. We show that the optimal stress test is coarse and lenient. Speculators have incentives to identify bad banks that erroneously passed the test, which makes markets useful at reducing the type-2, but not the type-1, error of a stress test. Our results hold even when the supervisor can intervene directly based on private information. In the limit of costless supervisory interventions, the optimal stress test is uninformative.

Preemptive Competition, Risk Governance, and Artificial Intelligence [Paper]
(with Matthieu Bouvard and Samuel Lee)
Abstract: Preemptive competition over time-sensitive opportunities creates a contractual externality that undermines risk governance. Firms delegate search and vetting to agents; faster execution by rivals increases preemption risk, raising the agency rents required to incentivize compliance. This interaction leads to a deal-driven front-office culture where internal controls are bypassed, even if firms fully internalize their losses. Compensation regulation promotes risk governance by slowing the competitive race. While artificial intelligence can reduce human agency frictions, its acceleration of search efficiency may paradoxically exacerbate externalities. Consequently, laissez-faire outcomes under AI can remain inferior to regulated regimes with human agents.