Financial Regulation and AI: A Faustian Bargain?

TL;DR

This paper explores the integration of predictive models into central bank regulation, developing a framework for optimal policy and introducing a novel graph transformer architecture that enhances systemic risk forecasting.

Contribution

It introduces a formal framework for regulator decision-making with predictive models and proposes a new graph transformer model tailored for financial holdings data.

Findings

Predictive models can improve welfare despite causal uncertainties.

Predictive accuracy and causal knowledge are complementary.

The graph transformer achieves state-of-the-art forecasting performance.

Abstract

We examine whether and how granular, real-time predictive models should be integrated into central banks' macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between implementing models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator's optimal policy in a setting in which private portfolios react endogenously to the regulator's model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data--a graph transformer--and we discuss why it is optimally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.