Finance
Non-bank financial institutions’ reliance on banks for contingent credit under stress and its consequences
In recent years, banks’ credit line exposure to ‘shadow banks’, or non-bank financial institutions (NBFIs), has grown significantly faster than exposure to non-financial corporations. Between 2013 and 2023, bank credit lines to NBFIs tripled from $500 billion to $1.5 trillion, and in 2023 over 20% of all bank credit lines were committed to NBFIs (Acharya et al. 2024). How do the growing linkages between banks and NBFIs impact the performance and systemic stability of banks? We answer this question by studying an important leading example of a non-bank financial institution – real estate investment trusts (REITs; Acharya et al. 2025).
REITs are significant investors in commercial real estate (CRE), with over $4 trillion in investments, corresponding to 20% of the CRE market that is currently valued at $21 trillion.
Rising interest rates and an economic slowdown can therefore exert considerable pressure on the CRE sector.
Considering the vast scale of the CRE market, disruptions in the CRE sector can influence the availability of bank credit to households and businesses. Consequently, regulators and policymakers have increasingly focused on the risks associated with CRE loans in recent times. REITs, being large CRE investors, inherit these fundamental economic and financial risks.
Importantly, nearly half of all bank-originated credit lines to public NBFIs are allocated to REITs. As shown in Figure 1, REITs exhibit significantly higher utilisation rates on bank credit lines compared to other NBFIs and non-financial corporates. Moreover, their credit line usage is markedly more sensitive to aggregate market performance, as indicated by the slope coefficients in the figure. Notably, REIT utilisation rates spike during periods of market stress (such as the COVID-19 period), making credit lines to REITs a potentially significant source of systemic risk for banks.
However, despite these factors, the significant exposure of large banks to the CRE sector via their credit lines to REITs is often underappreciated. It is commonly assumed that disruptions in the CRE sector mainly affect smaller banks. Figure 2 illustrates the on-balance-sheet exposure in the form of CRE loans as a proportion of total equity over the past decade for three types of banks: community banks (assets under $10 billion), regional banks (assets between $10 billion and $100 billion), and large banks (assets exceeding $100 billion). The exposure of regional and community banks, when scaled by equity, is approximately four and five times greater, respectively, than that of large banks. As per this exposure measure, there has been a notable increase over the past decade in CRE loan exposure among regional and, especially, community banks, but not among large banks. This might suggest that the CRE stress does not pose systemic risk to the largest banks in the economy.
Figure 1 Average credit line utilisation by borrower group
Notes: This figure plots the average credit line utilisation rate by three groups of borrowers – REITs, NBFIs (excluding REITs), and non-financial companies – versus the S&P 500 return. Each dot indicates the utilisation rate in one of the quarters between 2005Q1 and 2023Q4. The dots for 2008Q4 and 2020Q1 are labelled to highlight the main crisis quarters. The solid blue line indicates the slope of a regression of utilisation rates onto the S&P 500 return for REITs, the dashed red line and the green dotted line indicate the respective slopes of the same regression for NBFIs excluding REITs and non-financial companies. Data are obtained from Capital IQ and CRSP.
However, these figures ignore loans and credit lines provided by banks to REITs. The primary conclusion that emerges from our empirical analysis is that to get a complete picture of bank exposure to CRE risks, it is important to focus not just on the direct CRE exposure of banks but also on the provision of credit, especially by large banks, to REITs. Once the indirect exposure of banks via term loans and credit lines to REITs is accounted for, CRE exposures are concentrated not only in the portfolios of smaller banks but also among the largest US banks. Figure 3 illustrates this fact. In this figure, we categorise bank exposure into direct CRE exposure, indirect exposure via term loans to REITs, and indirect exposure through credit lines to REITs. For large banks, indirect exposure constitutes about a third of their total exposure, whereas for regional banks, the indirect exposure through REITs is considerably smaller, and for community banks, it is practically negligible.
Figure 2 Total on-balance-sheet exposure to the commercial real estate market
Notes: This figure shows the total reported on-balance sheet exposure to the commercial real estate market scaled by the total book value of equity of the bank. Data are from the FR Y-C at the quarterly frequency from 2013Q1 to 2023Q4. We split banks into three types: community banks (assets
Figure 3 Total exposure of banks to commercial real estate
Notes: This figure shows the total exposure of banks to commercial real estate (CRE) by stacking their direct exposure through on-balance sheet CRE loans and indirect exposure through banks’ term loans and credit lines to Real Estate Investment Trusts (REITs). Banks are classified as follows: community banks (assets
What, then, are the underlying mechanisms through which credit-line exposure of banks to REITs might pose a system-wide risk? In summary, there is a higher utilisation rate of credit lines by REITs relative to other NBFIs and non-financial corporates, especially when the performance of the underlying real estate assets declines and particularly during periods of aggregate economic stress. This behaviour is associated with a notable decrease in stock returns for banks more heavily exposed to undrawn credit lines extended to REITs, consistent with capital encumbrance imposed by credit line drawdowns impeding banks’ future intermediation activities.
We first tease out why REITs have higher utilisation rates on credit lines, especially during stress. By regulation, REITs are required to pay out at least 90% of their income in the form of dividends, restricting the amount of cash REITs can accumulate.
This leads to a disproportionately large dependence of REITs on bank credit lines for liquidity during stress periods. For example, Blackstone REIT (BREIT) and SREIT (managed by Starwood Capital) relied on their lines of credit during 2022 and 2024 respectively, nearly exhausting their credit line capacity to satisfy investor withdrawal requests.
We show that the findings in these case studies generalise to a broader setting in which we find significant positive correlations between redemptions and credit line drawdowns for all REITs in our sample. We also find that REITs increase investments and dividend payouts and reduce cash in the four quarters after a drawdown. This seems to indicate that they use both their cash and the liquidity from credit lines to acquire properties and pay out dividends. During crises (Global Crisis and COVID-19) however, we find that REITs start building cash buffers and they discontinue investing, i.e. acquiring properties. In fact, 72 cents of each dollar drawn is used to increase cash holdings. In other words, REITs use bank credit lines like ‘working capital’ for business activities in normal times, but to hoard cash during stress times.
We next investigate the impact of higher credit line utilisation by REITs on banks. Unlike term loan exposures that banks report on their balance sheet and fund with capital, and whose potential risks they manage through loan loss provisions, credit lines are off-balance-sheet and funded with equity capital to a much lesser extent until drawn down. Moreover, the risk of simultaneous drawdowns by borrowers during widespread market stress may suddenly constrain bank capital and/or liquidity, thereby reducing the banks’ ability to intermediate effectively. Consistent with these channels, we find that banks with higher undrawn credit line commitments to REITs experience lower stock returns during crises (controlling for banks’ total credit line commitments).
Finally, we document that credit lines to REITs substantially increase banks’ capital requirements during aggregate stress periods. We estimate an expected (market-equity-based) capital shortfall under aggregate market stress (e.g. -40% correction to MSCI Global Index) vis-à-vis a benchmark capital requirement (e.g. 8% of market equity relative to market equity plus non-equity liabilities), by incorporating REIT and non-REIT credit lines in stress test scenarios. We compare three models: one treating all borrowers uniformly, one distinguishing REITs by their unique drawdown behaviour, and one considering direct on-balance-sheet CRE exposure. As of Q4 2023, we estimate that the incremental capital requirement for publicly traded US banks rises by approximately 20% — from $180 billion to $217 billion — primarily due to REIT drawdowns, while CRE exposures add only $2 billion. Notably, over 90% of this additional capital burden falls on large banks. These results highlight the systemic risks posed to banks, and in turn to the real economy, by REIT credit lines, underscoring the need for careful regulatory scrutiny.
While we have focused on publicly traded REITs, this raises broader questions about the growing linkages between banks and NBFIs. Acharya et al. (2024) document that NBFI drawdowns have risen from 25% in 2013 to over 50% post‐COVID, with private NBFIs accounting for nearly 60% of drawdowns by private firms (compared to 30% for public ones). Additionally, credit lines to NBFIs such as business development companies (BDCs) and collateralised loan obligations (CLOs) have increased from 28% to 42% of total bank credit to NBFIs between 2013 and 2023. Given that private NBFIs generally exhibit higher credit line utilisation rates than REITs, stress in their funding conditions could similarly affect banks via the credit line channel. In essence, as NBFIs continue to expand their role in credit intermediation, their continuing reliance on banks for contingent liquidity highlights a critical channel through which risks may be transmitted back to the banking system.
References
Acharya, V V, N Cetorelli and B Tuckman (2024), “Where Do Banks End and NBFIs Begin?”, NBER Working Paper.
Acharya, V V, M Gopal, M Jager and S Steffen (2025), “Shadow Always Touches the Feet: Implications of Bank Credit Lines to Non-Bank Financial Intermediaries”, NBER Working Paper No. w33590.
Gupta, A, V Mittal and S Van Nieuwerburgh (2022), “Work from home and the office real estate apocalypse”, Working Paper, NYU Stern School of Business.
Hardin III, W and M Hill (2011), “Credit line availability and utilization in REITs”, Journal of Real Estate Research 33: 507–530.
Jiang, E X, G Matvos, T Piskorski and A Seru (2023), “Monetary Tightening, Commercial Real Estate Distress, and US Bank Fragility”, NBER Working Paper.
Mei, J and A Saunders (1995), “Bank risk and real estate: an asset pricing perspective”, The Journal of Real Estate Finance and Economics 10: 199–224.
Finance
How Natura &Co Is Transforming Finance with Generative AI on SAP S/4HANA
For a company navigating one of the most consequential transformations in its history, financial clarity is not optional—it is essential. Natura &Co, the Brazilian personal care and cosmetics group behind iconic brands such as Natura and Avon, has long been committed to combining purpose-driven business with commercial performance. After a period of strategic portfolio reshaping, including the divestiture of its Aesop and The Body Shop holdings, the company is now sharpening its focus on profitability and operational excellence across Latin America and global markets.
At the center of that effort sits a deceptively complex challenge: understanding, in real time, which revenue and cost factors are driving or eroding gross margin across a highly diversified business. For years, answering that question meant manual reporting, delayed insights, and finance teams spending valuable time on data gathering rather than analysis.
That’s now changing, thanks to a co-innovation initiative developed together with SAP and Numen, a global SAP partner specializing in digital transformation and enterprise software implementation.
From manual reporting to proactive decision intelligence
The project’s goal was to replace a labor-intensive gross margin analysis process with a generative AI application embedded directly into Natura &Co’s financial workflows. Built on SAP Business AI Platform, SAP’s unified foundation integrating business technology, data, and AI capabilities, the application connects directly to data in SAP S/4HANA to provide finance teams with automated insights and narrative recommendations in real time, without the need for manual data pulls or offline reporting.
The application enables users to explore revenue, cost, and margin drivers interactively, identifying at a glance which elements are protecting or eroding margin performance across markets and product lines. Crucially, human oversight remains central to the design: the AI application generates insights, while finance professionals retain full control over interpretation and decisions.
“The implementation of gross margin analysis using AI in SAP S/4HANA marked an inflection point in the analytical capability of our finance area,” said Rogério Dias Garcia, tech manager, ERP Latam, Natura &Co. “We overcame delays and raised the standard of insights by integrating margin analysis from SAP S/4HANA with a large language model connected via the SAP AI Core layer. This architecture allowed us to provide, in an agile, secure, and completely anonymous manner, a stratified and precise view of gross margin offenders and protectors—discriminating exactly which revenue or cost elements were driving market performance.”
A collaborative architecture for scalable AI adoption
Natura &Co’s application derived from a prototype SAP partner Numen created in early 2024 at SAP’s global Hack2Build on business AI, leveraging the generative AI capabilities of SAP Business AI Platform. The solution was designed and developed through close collaboration between Natura &Co, Numen, and SAP. From the outset, the approach was to align AI adoption with concrete business priorities, ensuring the application would be scalable and production-ready rather than a standalone prototype.
Numen brought deep SAP implementation expertise to the project, combining knowledge of SAP S/4HANA architecture with hands-on experience in building solutions on SAP Business AI Platform. The technology stack—SAP S/4HANA, SAP AI Core, SAP Fiori, and SAP Business Technology Platform—provided the secure, integrated foundation needed to connect financial data with generative AI capabilities in an enterprise context.
“SAP enabled the transformation by providing the technological foundation and expert support,” said Carlos Aravechia, head of Data Design & Intelligence at Numen.
The success of the project has validated a broader conviction at Natura &Co: that generative AI, embedded directly in ERP workflows, can fundamentally reposition finance from a transactional function to a strategic business partner.
A blueprint for other businesses
The Natura &Co project demonstrates a pattern that other organizations can replicate, particularly those running SAP S/4HANA. The combination of structured ERP data with the contextual reasoning capabilities of large language models creates a foundation for decision intelligence that goes well beyond traditional business intelligence tools.
The project was built within a six-month co-innovation sprint and went live in August 2025. It is currently in use across Natura &Co’s Equador operations.
Looking ahead, Natura &Co is already planning the next phase: integrating Joule Agents to further automate the extraction of standard analytical content and deepen the AI-driven optimization of financial processes.
“The success of this initiative validates the transformative potential of embedded AI within our ERP,” Dias Garcia noted. “We are now ready to move forward—deepening these insights and integrating the capability of Joule Agents to maximize the extraction of standard content and further optimize our business decisions.”
For SAP customers evaluating how to move from AI experimentation to AI in production, the Natura &Co project offers a concrete, replicable model: start with a high-value, well-defined business process, embed AI directly into existing workflows, and build in human oversight from the start.
Finance
Low-income Chinese girl aces gaokao, inspires live-streamers offering help
A girl from a disadvantaged rural family in central China topped this year’s gaokao, attracting numerous live-streamers eager to finance her education, which she declined.
The home of 18-year-old secondary school graduate Han Yaping in a Henan province village was recently bustling with live-streamers.
This attention came after Han achieved an impressive score of 699 out of 750 in the gaokao, China’s national college entrance exam.
She has received offers from China’s two leading universities, Tsinghua University and Peking University.
Han’s accomplishment is particularly remarkable given her family’s impoverished circumstances.
Her mother suffers from ankylosing spondylitis, an inflammatory arthritis affecting the spine, preventing her from working. Her father, who earns a living through farming and odd jobs, serves as the family’s sole provider. Han also has a younger sister.
Finance
UK financial regulator publishes landmark AI review
The UK’s Financial Conduct Authority (FCA) published a landmark review on Monday that proposes recommendations to regulate the impact of artificial intelligence (AI) on the financial decisions made by consumers.
The review, titled the Mills Review, anticipates that both consumers and firms will start delegating “more financial decision-making to AI systems,” including for agreements, initiating transactions, and executing decisions “within agreed parameters.” One of the key findings of the review outlined that while AI can help bridge advice gaps and “support growth,” there remain risks “associated with fraud, cyber security, and consumer harm.” Conducting the review, Sheldon Mills highlighted that “AI can also amplify risks: bias, discrimination, exclusion, opaque decision-making (particularly when multiple AI models interact), misleading or hallucinatory advice and erosion of consumer trust.”
The review stated that presently, one in five adults in the UK are “already open to AI making decisions for them,” particularly when decisions feel “complex or high stakes.” It found that roughly 26 percent of the population “trust general-purpose tools such as ChatGPT, Claude or Gemini for financial advice” with little awareness that such platforms provide no “formal routes to recourse” or protections.
Overall, the Mills Review identified four areas that it anticipates will be impacted by AI in the financial sector: “the transformation of firms,” “new consumer journeys,” “a reshaped competition landscape,” and “amplified financial crime and cyber risk.” The FCA projected the shift in how consumers and firms consult AI to take place by 2030.
The Mills Review put forth seven “priority” recommendations to be considered by the FCA Board. It recommended that any transitions to autonomous AI models be monitored and that regulatory frameworks and perimeters be adapted and secured. The review called for the strengthening of “system-wide coordination and oversight,” the scaling up of the FCA’s AI Lab to enable it to support AI models and innovation for agentic finance, and an “AI-enabled agentic supervisory model” to be built and adopted. Finally, it recommended that a trusted “public-interest AI-enabled financial capability service” be developed.
The FCA announced, in the press release, that it will launch an AI “good and poor practice publication” in late 2026.
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