Structured finance is entering a period of significant change as private credit expands, and AI transforms investment analysis, creating new opportunities and considerations for investors.
Private credit is increasingly moving into asset-backed finance (ABF), while advances in AI are enabling investors to analyze more complex portfolios and transactions in new ways.
Together, these developments are broadening the investment landscape while increasing the importance of transparency, high-quality data and sophisticated analytics.
1. Structured finance growth: Where are the opportunities?
Private credit is reshaping how capital is raised, deployed and distributed. Once largely associated with direct corporate lending, it has evolved into structured credit, particularly asset-backed finance allowing for the packaging and distribution of exposures.
The Moody’s 2026 Private Credit Outlook highlights this shift, noting that asset managers are seeking to finance more diverse asset pools, including consumer loans and data infrastructure credit. It also anticipates that a broader range of assets will support growth in structured finance.
The rapid expansion of AI infrastructure is one example. ‘Massive amounts of money are piling into data centers and infrastructure supporting AI,’ says David Little, Head of Asset Management. Financing these real assets could create opportunities to package the resulting exposures into a range of investment vehicles, alongside other emerging asset classes such as renewable energy. This is an area where structured finance could play a role.
Source: Moody’s Ratings1
In 2023, hyperscaler capital expenditure (capex) remained largely flat before accelerating sharply with the AI race in 2024 and 2025. Spending is projected to reach $785 billion in 2026 and approach $1 trillion by 2027. As infrastructure investment outpaces internal cash flow, hyperscalers are expected to issue $240 billion in debt and take on more than $1 trillion in lease commitments. This is creating a structural funding gap that private credit is increasingly helping to fill.
The opportunity comes at a time of rapid expansion for private credit. Assets under management are projected to exceed $2 trillion in 2026 and almost $4 trillion by 2030, with asset-backed finance expected to be a key driver of growth.2
For investors, the opportunity extends beyond financing new assets to understanding how risk is structured and where it resides. Because private markets typically offer less readily available information than public markets, transparency and insight into asset quality, transaction structures and portfolio concentrations are increasingly important. As the opportunity set expands, investors need the ability to look through structures and evaluate the underlying exposures.
2. AI: How is it transforming structured finance analysis?
Structured finance has always been analytically demanding. Investors must evaluate asset-, deal- and tranche-level data, run multiple scenarios, and assess how changing assumptions affect performance. Historically, answering even relatively straightforward questions often required multiple models and extensive analysis.
Rather than adding another layer of output, AI can help investors interrogate complex datasets and address more sophisticated questions, such as:
- Which structured finance instruments are most exposed to rising defaults?
- How would widening spreads affect different transactions?
- Should we refinance this CLO?
- Where are the largest concentrations of risk within a portfolio?
David points to his team’s work on knowledge graphs for collateralized loan obligations (CLOs) as an example of how AI can help answer these types of questions. By connecting complex datasets, these tools can help investors to compare transactions and assess how different deals might respond to a market event. Rather than first deciding which models to run, investors can reach the relevant analysis far more quickly. ‘AI can do it in a matter of seconds,’ says David.
As AI advances, its potential extends beyond structured datasets. Generative AI can help investors analyze unstructured information, automate parts of the reporting process and make complex insights more accessible.
Its value lies not only in speed, but also in scale.
AI enables investors to ask more questions, test more scenarios and compare more transactions than traditional methods allow. Realizing these benefits depends on consistent, high-quality, granular data and the ability to use it effectively, helping investors assess risk more rigorously and make better-informed decisions.
Faster analysis does not remove the need for judgment. As AI becomes more deeply embedded in investment workflows, transparency, explainability and validation remain essential. The objective is not to replace investment expertise, but to augment it by allowing investors to focus on interpretation and decision-making.
3. Structured risk in private credit
Private credit investors are seeking new ways to finance assets, structure transactions and distribute risk.
Private credit is no longer confined to traditional bilateral lending. As David explains, ‘GPs in the private credit market are increasingly offering creative solutions for LPs interested in particular risk types, asset classes and transaction structures.’
The growth of asset-backed finance illustrates this trend in practice. A private credit manager may originate financing for consumer receivables or data infrastructure, then structure the cashflow payments from its pool of assets, creating different risk tranches. These exposures are placed with investors whose return and risk requirements vary.
These structures can broaden access to capital and diversify portfolios, but they can also introduce greater complexity, opacity and liquidity risk. Investors therefore need to assess collateral quality, leverage, cash-flow waterfalls and portfolio concentrations as well as the links among asset managers, banks and insurers. As markets become more interconnected, stress in one area can spread more quickly across the financial system.
How Private Credit connects assets and investors
Private credit is increasingly financing assets that would have traditionally been financed within the public markets. For investors, this is creating a broader range of investment opportunities. However, it also increases the need to understand how transactions are structured and how different sources of risk interact.
Conclusion
Growth in asset-backed finance, advances in AI and the rise of private credit capital are expanding the range of opportunities available to investors. Capturing those opportunities will require more than speed or access to capital, but disciplined risk assessment, high-quality data, transparency, and a clear view of how structural features affect underlying risk will remain essential. Those investors able to combine rigorous analysis with new technologies will be best positioned to navigate an increasingly complex investment landscape.
Sources
- Moody's Ratings "Hyperscaler capex to near $1 trillion in 2027, fueling AI growth, memory shortage" (May 2026)
- Moody's 2026 Private Credit Outlook
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