At the core of the financial system is the processing and aggregation of vast amounts of information into price signals that coordinate participants in the economy. Throughout history, advances in information processing, from simple book-keeping to artificial intelligence (AI), have transformed the financial sector. We use this framing to analyze how generative AI (GenAI), emerging AI agents and, more speculatively, artificial general intelligence will impact finance. We focus on four functions of the financial system: financial intermediation, insurance, asset management, and payments. We also assess the implications of advances in AI for financial stability and prudential policy. Moreover, we investigate potential spillover effects of AI on the real economy, examining both an optimistic and a disruptive AI scenario. To address the transformative impact of advances in AI on the financial system, we propose a framework for upgrading financial regulation based on well-established general principles for AI governance.
This paper studies the impact of green fiscal rules-designed to protect climate-related spending-on debt dynamics.Simulations of green rules that exempt green spending from the rule limits for an emerging market economy illustrate that they can lead to unsustainable debt dynamics when the net zero emissions goal is pursued mostly using spending-based instruments(e.g.,investment and subsidies).Or the rule would need to implicitly assume a large fiscal adjustment in the non-green budget,which would undermine its credibility.It will be needed to build broad public consensus for a more comprehensive fiscal strategy that tackles the difficult policy tradeoffs that will be required and takes into account long-term effects.A more appropriate mix of climate policies,including actively employing carbon pricing,should be pursued within the overall setting of fiscal and debt objectives.Developing"green"medium-term fiscal frameworks would help to integrate climate change considerations into fiscal policy design in a more comprehensive manner.
This paper offers a comprehensive analysis of the implications for financial stability of a central bank issuing a digital currency to the public at large.We start with a systematic analysis of balance sheet changes that arise from the new liability for the central bank and the banking system,and examine how they depend on preconditions,central bank choices,and banking system responses.Based on this,we discuss the range of implications for financial stability that may arise in steady state,in the context of adoption,and in crisis times.Threats to financial intermediation in steady state arise mainly in situations where the central bank balance sheet expands,and triggers adjustment mechanisms that lead to more costly or less stable funding of the banking system,while in crisis times run risk may increase.Our analysis of policy choices to control these effects considers macroprudential policy,and an expansion of central bank lending to commercial banks,but finds that a main contribution needs to come from a design of the CBDC that encourages its use as a means of payment rather than a store of value.
We analyze how aging populations might affect the stability of banking systems through changes in the balance sheets and risk preferences of banks over the period 2000-2022. While the anticipated decline in maturity transformation due to aging hints at a possible reduction in risk exposure, an older population may propel banks towards yield-seeking behaviors, offsetting the diminishing prominence of conventional lending operations. Through a comprehensive examination of advanced economies over the past two decades, our findings reveal a general enhancement in bank stability correlating with the aging of populations. However, the adaptive responses of banks to these demographic changes are potentially introducing tail risks. Given the rapid global shift towards aging societies, our analysis highlights the critical need for policymakers to be proactive and vigilant. This is particularly pertinent considering historical precedents where periods of relative stability have often been harbingers of emerging risks.
This paper investigates the impact of automation on the U.S.labor market from 2000 to 2007,specifically examining whether more generous social protection programs can mitigate negative effects.Following Acemoglu et al.(2020),the study finds that areas with higher robot adoption reduced employment and wages,in particular for workers without college degree.Notably,the paper exploits differences in social protection generosity across states and finds that areas with more generous unemployment insurance(UI)alleviated the negative effects on wages,especially for less-skilled workers.The results suggest that UI allowed displaced workers to find better matches The findings emphasize the importance of robust social protection policies in addressing the challenges posed by automation,contributing valuable insights for policymakers.
