Jeffrey S. Allen and Max S. S. HatfieldWe examined the performance of four families of large language models (LLMs) and a variety of common fuzzy matching algorithms in assessing the similarity of names and addresses in a sanctions screening context. On average, across a range of realistic matching thresholds, the LLMs in our study reduced sanctions screening false positives by 92 percent and increased detection rates by 11 percent relative to the best-performing fuzzy matching baseline.

Yesol Huh and Matthew Vanderpool KlingThis paper introduces parallel trends forest, a novel approach to constructing optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to construct an optimal control sample that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data.

This study examines the drivers of inflation levels, inflation variability, and growth variability collectively representing long-term central bank performance across 37 advanced economies in the Great Moderation era. A key finding is that central bank performance is consistently linked to the overall quality of institutions, while central bank-specific factors such as independence, exchange rate regimes, or inflation targeting show no significant impact. The analysis is extended to the 2022 inflation resurgence, using pre-2022 country characteristics.

Anne Lundgaard Hansen and Seung Jung LeeThis paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in investment decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends.