When the financial system becomes searchable

Andreas Viljoen

Financial crises rarely begin with one self-contained weakness. They emerge when vulnerabilities connect: leverage meets a margin call; a margin call meets an illiquid market; falling prices meet common collateral; and a funding concern becomes a run. Before the event, each link may sit in a different spreadsheet, institution or jurisdiction. Afterwards, the route through them can look obvious. This post explores a possibility raised by advances in artificial intelligence (AI): that the financial system could become searchable, making more of those routes visible beforehand. It outlines two specific scenarios and their implications for financial authorities. First, system-wide testing by authorities should learn to search the way agents will. And consequential agent decisions and actions should be observable in operation, unlike today.

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When the floor rises: how Basel output floor could tilt bank lending

Marzio Bassanin

The 2017 finalisation package of the Basel III reforms to bank capital regulation aims to make capital requirements more robust and consistent across banks. One of its most significant changes is the introduction of the ‘output floor’, which limits how far capital requirements calculated using internal models can fall below those based on standardised approaches. But the output floor is not just about capital levels. We find that it may tilt lending towards corporate loans and some riskier mortgages, and away from the safest mortgages. And if banks’ responses to past reforms are any guide, banks won’t wait until its full implementation in 2030 to start reacting.

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A new framework for monitoring risks in the UK housing market

Tihana Škrinjarić

In my recent paper, I present a new model that helps assess risks in the UK housing market. Unlike traditional approaches that focus on average house price growth, the model estimates a full range of possible outcomes, allowing policymakers to identify potential risk of big house price drops. The analysis also highlights important regional differences: areas with more constrained housing supply tend to be more sensitive to changes in interest rates. Expanding supply can help ease price pressures. These insights can help improve the monitoring of housing market vulnerabilities and support financial stability policy.

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Distributional consequences of borrower-based macroprudential tools

Jagdish Tripathy, Arzu Uluc, José-Luis Peydró and Francesc Rodriguez-Tous

Borrower-based macroprudential measures – such as limits on loan to income (LTI) and loan to value (LTV) ratios – have become a standard feature of the post-crisis regulatory landscape. A growing body of country-specific evidence suggests these measures are effective in moderating the self-reinforcing loop between mortgage credit and house prices, and in reducing default rates and limiting house price volatility during periods of economic stress. Yet their distributional consequences are less well understood. In a new paper, we survey the existing evidence and find that these tools deliver clear financial stability benefits, while also generating distributional effects across borrower groups. Further, we identify areas where future research is needed to provide a comprehensive welfare assessment of these measures.

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Demographics, deposits and data: using machine learning to segment UK mortgages

Joe Grimshaw

Who are the UK’s mortgage borrowers, and how do their characteristics differ? Despite extensive literature on mortgage profiles, loan-level segmentation remains limited, existing work relies on aggregates or predefined categories. I address this gap by applying unsupervised machine learning to 20 years of data, allowing the model determine segments without prior assumptions. Three clusters emerge: one with low leverage, and two with high leverage but notably different income profiles. Lending composition has shifted gradually. High leverage, high-income borrowers now account for a larger market share, and first-time buyers increasingly fall into more leveraged segments. Machine learning is crucial for financial stability, revealing concentrations of characteristics, and trends, that aggregates and simple splits cannot, offering richer and earlier indications of potential vulnerabilities.

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Credit constraints and housing market access

Belinda Tracey and Neeltje van Horen

The Help-to-Buy (HTB) programme introduced in 2013 reopened the 95% loan to value (LTV) segment of the UK mortgage market, thereby reducing the minimum deposit requirement for many first-time buyers (FTBs) from 10% to 5% (Chart 1). That policy change offers a useful natural experiment to study how deposit constraints shape access to homeownership. We previously demonstrated that this easing of deposit constraints generated a clear increase in local spending. In a recent paper, we show that lowering this constraint increases FTB home purchases, particularly among households without access to external financial support for their deposit.

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Explainability in machine learning: do popular methods deliver on their promises?

Ivona Cickovic and Andrea Serafino

Machine learning models are increasingly used in organisational decision-making, yet their inner workings often remain opaque. When these systems influence real world outcomes, knowing what they predict is not enough – we also need to understand why. Explainability methods aim to illuminate this ‘black box,’ and feature attribution tools that link predictions to individual inputs are especially popular. They feel intuitive but rely on strict data assumptions that rarely hold, making their outputs unreliable. The 2019 Apple Card case illustrates why this matters: despite gender not being an explicit input, women appeared to receive lower credit limits than men with similar profiles – an outcome attribution methods struggle to explain. This post examines a key assumption underpinning these tools and how it distorts explanations.

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Opening the floodgates? Modelling spillovers from flood insurance protection gaps to UK mortgages

Will Banks and Kemal Erçevik

When extreme weather hits, households typically turn to insurers to cushion the financial blow. But rising temperatures and greater exposure in high-risk areas could test the insurance sector’s capacity to absorb such losses. As the Financial Policy Committee has highlighted, climate change could create insurance protection gaps, leaving households vulnerable and shifting risks across the financial system. We have built a model to estimate potential protection gaps, finding that – under conservative assumptions – the share of UK mortgagors uninsured could increase from 5% today to around 7%–10% in 2050, or up to 16% following a severe flood event. While this would have substantial welfare implications, our model suggests the aggregate impact on lenders would be small compared to previous financial crises.

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Bond financing conditions and economic activity in the UK: aggregate and firm-level evidence

Eduardo Maqui, Nicholas Vause and Márcia Silva-Pereira

In recent decades, the corporate bond market has grown from a relatively niche source of finance for UK corporations to a central pillar alongside bank loans. This transition raises an important question: as with bank credit conditions, have supply conditions in the corporate bond market come to significantly affect UK economic activity? Our recent research suggests the answer is a resounding yes. We show that a measure of corporate bond financing conditions − the Excess Bond Premium (EBP) − not only anticipates macroeconomic outturns in the UK, but also influences investment by UK firms, especially those that are highly leveraged and more reliant on bond finance.

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Monetary policy, state-dependent bank capital requirements and the role of non-bank financial intermediaries

Manuel Gloria and Chiara Punzo

The expansion of non-bank financial institutions (NBFIs) is transforming the financial landscape and introducing fresh challenges for financial stability and oversight at the same time as creating opportunities. Using a dynamic stochastic general equilibrium (DSGE) model, we find that while NBFIs may enhance long-term welfare for households and entrepreneurs in normal conditions, their greater role also heightens vulnerabilities to severe shocks in the financial system. Greater NBFI activity boosts competition in the financial sector, leading to more efficient resource allocation. A working paper detailing these results was recently published.

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