Asset-Based Finance: Income Built Differently

Asset-Based Finance: Rethinking Income and Resilience

 

As allocators seek income, diversification and resilience simultaneously, asset-based finance offers a structurally different solution for credit portfolios.

Asset-based finance (ABF), also known as specialty finance or asset-based credit, is rapidly gaining traction among allocators seeking more resilient and diversified sources of income. Encompassing financial instruments secured by a diverse range of contractual cash flows from financial and hard assets, the ABF market is estimated at upward of $20 trillion¹—and it is still growing.

That scale is grounded in real-economy activity—from consumer credit and small business lending to equipment and infrastructure financing—connecting main street commerce with institutional capital markets.

The attraction goes beyond yield. Investors are increasingly drawn to ABF as a way to access that real-economy breadth through shorter-duration assets designed to mitigate downside risk, potentially offering lower sensitivity to broader market volatility. For most allocators, balancing the need for predictable income against tighter capital rules and pressure to diversify, that combination can be compelling.

A Different Kind of Private Credit

In a world shaped by structural volatility, persistent inflation uncertainty and shifting market correlations, traditional assumptions about portfolio construction are being challenged. Duration is no longer functioning as a consistent diversifier, correlations between equities and bonds have become less predictable, and income generation has re-emerged as a central driver of allocation decisions. ABF speaks directly to each of those pressures.

ABF is a form of private credit in which returns—driven primarily by interest and principal payments—are secured by the hard or financial assets underpinning each transaction. The underlying collateral—whether consumer receivables, equipment, aircraft or data center infrastructure—provides a discrete and tangible source of repayment that is largely independent of corporate earnings cycles.

That has practical consequences for portfolio construction. ABF investments are typically self-amortizing, generating regular principal and interest repayments over shorter timeframes, with underlying asset durations often ranging between six months and two years. This provides greater flexibility and faster repricing ability in volatile rate environments, a meaningful advantage for allocators navigating uncertain inflation and rate trajectories.

Rethinking the Yield-Duration Trade-Off

ABF also challenges a long-standing assumption in fixed income investing: that accessing higher income requires extending duration. Today, many shorter-duration private asset-based opportunities can offer comparable—or higher—yields than longer-duration corporate credit exposures. According to Neuberger's capital market assumptions (as of March 31, see disclosure text at the end of the article), private ABF strategies can potentially offer higher estimated returns across multiple weighted-average-life ranges relative to traditional public fixed income assets.

This reconfiguration of the yield-duration relationship is particularly relevant at a time when investors are wary of locking into long-dated exposures amid persistent macroeconomic uncertainty. ABF offers a way to generate meaningful income without taking on commensurate duration risk.

Structural Protection and Downside Management

ABF is not risk-free. Credit losses can and do occur, particularly in stressed environments. What distinguishes the asset class, however, is how those losses are anticipated and absorbed. Many transactions are structured to mitigate downside risk, be it through over-collateralization, first-loss protection, covenant-heavy structures or bankruptcy-remote vehicles. These protections are designed to act before problems compound—giving the asset class a degree of downside resilience that is structurally embedded rather than discretionary.

Four Potential Benefits

There are four principal potential benefits of an allocation to ABF:

• Attractive risk-adjusted returns: ABF strategies typically offer superior risk-adjusted returns relative to traditional public assets, and less duration risk.
• Diversification: The breadth of borrower type, industry, geography and underlying asset creates differentiated cash flow profiles that are difficult to replicate within conventional fixed income or corporate credit allocations.
• Downside mitigation: Tightly negotiated covenants, collateral coverage requirements and first-loss protection can limit losses materially when conditions deteriorate.
• Inflation protection: The value of collateral—particularly hard assets—tends to rise in an inflationary environment, providing a measure of protection that purely cash-flow-based strategies lack.

Building the Case for ABF

The case for ABF is, at its core, a case for structural alignment. In an environment where traditional fixed income is being asked to do more with less—generating income, managing volatility and providing diversification simultaneously—ABF offers a genuinely differentiated set of tools.

Its shorter duration addresses rate sensitivity. Its structure addresses downside risk. Its breadth across asset types, geographies and underlying collateral addresses concentration. And its real-economy grounding addresses the growing demand for investments with tangible, observable underlying activity.

For investors willing to look beyond familiar credit formats, ABF represents one of the more compelling opportunities in today's fixed income landscape—not as a replacement for existing allocations, but as a structural complement that can meaningfully improve the risk-return profile of an income-oriented portfolio.

¹ S&P Global, “The Opportunity of Asset-Based Draws in Private Credit,” November 20, 2024

 

Disclaimer

Capital market assumptions used herein reflect Neuberger’s forward-looking estimates of the benchmark return or volatility associated with an asset class. Estimated returns and volatilities are hypothetical return and risk estimates generated by Neuberger’s Institutional Solutions Group.

Estimated returns and volatilities do not reflect the alpha of any investment manager or investment strategy/vehicle within an asset class. Information is not intended to be repre­sentative of any investment product or strategy and does not reflect the fees and expenses associated with managing a portfolio or any other related charges, such as commis­sions and surrender charges. Estimated returns and volatilities are hypothetical and generated by Neuberger based on various assumptions and inputs, including current market conditions, historical market conditions and subjective views and estimates.

Capital market assumptions shown reflect Neuberger’s long-term (20+ years into the future) estimates or intermediate-term (five to 10 years into the future) estimates which are reviewed at least annually. Results will differ depending on whether they are based on Neuberger’s long-term (20+ years into the future) or intermediate-term (five to 10 years into the future) capital market assumptions. Neuberger’s capital market assumptions are derived using a building block approach that reflects historical, current, and projected market environments, forward-looking trends of return drivers, and the historical relationships asset classes have to one another. These hypothetical returns are used for discussion purpos­es only and are not intended to represent, and should not be construed to represent, predictions of future rates of return. Actual returns may vary significantly. Neuberger makes no representations regarding the reasonableness or completeness of any such assumptions and inputs.

Assumptions, inputs and estimates are periodically revised and subject to change without notice. Estimated returns and volatilities should not be used, or relied upon, to make investment decisions.

Rate of Return Estimate: Rate of return or geometric return is a measure of average returns of an investment over a period of time. Geometric rates of return are typically referred to as annualized compound rate of returns and are always less than or equal to the arithmetic mean return of the same time series. Geometric rates of return are used for straight-line calculations within the analysis, for example, the cash flow calculations. In straight-line calculations, each year is represented as a gain, so the compound (geometric mean) rate of return is used to adjust for the amount needed to make up for a loss in a given year. For example, if you lose 5% in one year, and gain 5% the year after, you still have less than you started with at the beginning of year one.

Arithmetic Mean Estimate: Arithmetic mean or average return is calculated by dividing the sum of a series of numbers by the number of overall items. This is more typically thought of as an “average” of the data set. Arithmetic mean or average return ignores the impact of compounding in the context of analyzing investment returns and is the simple average of returns observed over a period of time. Arithmetic mean returns are used in this material and, if applicable, the Efficient Frontier, because, through randomization, losses and gains are being accounted for each year.

Standard Deviation: A statistical measure of the volatility based on the distribution of a set of data from its mean (average value). For example, a portfolio with an average return of 10% and a standard deviation of 15% would return a result between -5% and +25% the majority of the time (68% probability or 1 standard deviation), almost all of the time the return would be between -20% and +40% (95% probability or 2 standard deviations). If there were 0 standard deviation then the result would always be 10%. Generally, more aggressive portfolios have a higher standard deviation and more conservative portfolios have a lower standard deviation.

Asset-Based Finance: Built to Protect

 

Asset-based finance offers a toolkit of structural mechanisms to help protect investor capital.

Asset-based finance (ABF) is not simply a source of income. It is, by design, a framework for managing risk. In an asset class where transactions are structurally bespoke, highly diversified and secured by real-world collateral, the mechanisms that protect investor capital tend to be foundational not incidental. Understanding how those mechanisms work is essential to appreciating why ABF can offer consistent performance across market cycles.

Structure as a Defense

ABF transactions can take many forms, but two structures illustrate the core principles at work.

In the first, investors purchase loans or receivables from originators via forward-flow agreements—arrangements whereby an originator sells its beneficial interest in receivables on an ongoing basis, subject to pre-determined eligibility criteria. Many originators retain a portion of originations on their own balance sheet, maintaining alignment of interest with investors, who receive cash flows after fees. In the second, loan originators place assets into a bankruptcy-remote special purpose vehicle (SPV), typically retaining 5–25% of the loan value as equity while investors finance the remainder for a contractual fixed or floating-rate coupon.

In both cases, the investor holds a more senior position in the capital structure than the originator, which absorbs first losses (“first loss protection”). That structural subordination is not a technical detail—it is a primary source of downside protection.

Six Pillars of Resilience

Not all ABF transactions are created equal, but the asset class offers toolkit of structural protections that, we believe when properly deployed, can work in concert to mitigate risk and support consistent performance across market cycles.

• Excess spread is the first and perhaps most powerful buffer. High-yielding underlying assets generate contractual returns significantly above what is paid to investors, creating a cushion that absorbs deterioration in collateral performance before interest or principal is affected.
• Shorter duration reduces the vintage and repayment risk that can weigh on longer-dated strategies—particularly in a downturn. Self-amortizing structures mean principal and interest repay steadily throughout the life of a transaction rather than in a single bullet maturity, limiting the exposure to refinancing risk and market dislocation.
• Credit enhancement provides structural protection from the outset. Investments are typically over-collateralized: the value of the underlying assets exceeds the capital at risk, giving investors a built-in margin of safety that does not depend on market conditions remaining benign.
• Covenant-heavy structures create early-warning mechanisms. Bespoke performance triggers allow investors to identify and act on deterioration before problems compound—a proactive discipline that stands in contrast to the reactive nature of public market investing.
• Pricing power is a structural advantage of the private market. Access to non-competitive, directly sourced transactions means terms are negotiated rather than accepted, enabling structures that can deliver more return per unit of underlying risk than comparable public exposures. Realizing this advantage, however, depends on the depth of an ABF investor’s relationships and sourcing infrastructure.
• Diversification, finally, operates at the portfolio level. Exposure across large pools of small-balance loans and receivables limits concentration risk, ensuring that no single default or cluster of defaults can threaten the integrity of the broader portfolio.

When Data Is the Underwriting Edge

These principles are best understood through practice. Consider, as an illustrative example, a small business lending platform embedded in the daily operations of thousands of merchants across the United States—hair salons, coffee shops, restaurants, retail stores. What sets this originator apart is not simply its lending model but the data infrastructure underpinning it.

The platform processes payments for its merchant clients every day, accumulating granular, real-time data on their financial health that conventional lenders cannot access. Its lending arm uses that data to offer financing on an invite-only basis—a positive selection mechanism that filters for established, creditworthy businesses before underwriting even begins. The example model draws on transaction frequency, revenue trends and early indicators of both macro and micro stress, enabling a depth of insight that peer lenders cannot replicate.

The result: loss rates should remain stable even as origination volumes have grown—a track record underpinned by the quality and durability of the model across cycles. This is ABF's structural resilience in practice: proprietary data driving better selection, better underwriting and more consistent outcomes.

The Compounding Effect

What makes ABF's defensive architecture so compelling, in our view, is not any single feature but the way these pillars compound. Excess spread absorbs early losses. Over-collateralization provides structural headroom. Covenants trigger early action. Short duration limits vintage exposure. And diversification ensures that stress in one pocket of the portfolio does not propagate across it.

In an environment where income generation, capital preservation and resilience are no longer separable objectives, ABF offers a framework designed to pursue all three simultaneously—not as a trade-off, but as a structural outcome.

 

Disclaimer

Capital market assumptions used herein reflect Neuberger’s forward-looking estimates of the benchmark return or volatility associated with an asset class. Estimated returns and volatilities are hypothetical return and risk estimates generated by Neuberger’s Institutional Solutions Group.

Estimated returns and volatilities do not reflect the alpha of any investment manager or investment strategy/vehicle within an asset class. Information is not intended to be repre­sentative of any investment product or strategy and does not reflect the fees and expenses associated with managing a portfolio or any other related charges, such as commis­sions and surrender charges. Estimated returns and volatilities are hypothetical and generated by Neuberger based on various assumptions and inputs, including current market conditions, historical market conditions and subjective views and estimates.

Capital market assumptions shown reflect Neuberger’s long-term (20+ years into the future) estimates or intermediate-term (five to 10 years into the future) estimates which are reviewed at least annually. Results will differ depending on whether they are based on Neuberger’s long-term (20+ years into the future) or intermediate-term (five to 10 years into the future) capital market assumptions. Neuberger’s capital market assumptions are derived using a building block approach that reflects historical, current, and projected market environments, forward-looking trends of return drivers, and the historical relationships asset classes have to one another. These hypothetical returns are used for discussion purpos­es only and are not intended to represent, and should not be construed to represent, predictions of future rates of return. Actual returns may vary significantly. Neuberger makes no representations regarding the reasonableness or completeness of any such assumptions and inputs.

Assumptions, inputs and estimates are periodically revised and subject to change without notice. Estimated returns and volatilities should not be used, or relied upon, to make investment decisions.

Rate of Return Estimate: Rate of return or geometric return is a measure of average returns of an investment over a period of time. Geometric rates of return are typically referred to as annualized compound rate of returns and are always less than or equal to the arithmetic mean return of the same time series. Geometric rates of return are used for straight-line calculations within the analysis, for example, the cash flow calculations. In straight-line calculations, each year is represented as a gain, so the compound (geometric mean) rate of return is used to adjust for the amount needed to make up for a loss in a given year. For example, if you lose 5% in one year, and gain 5% the year after, you still have less than you started with at the beginning of year one.

Arithmetic Mean Estimate: Arithmetic mean or average return is calculated by dividing the sum of a series of numbers by the number of overall items. This is more typically thought of as an “average” of the data set. Arithmetic mean or average return ignores the impact of compounding in the context of analyzing investment returns and is the simple average of returns observed over a period of time. Arithmetic mean returns are used in this material and, if applicable, the Efficient Frontier, because, through randomization, losses and gains are being accounted for each year.

Standard Deviation: A statistical measure of the volatility based on the distribution of a set of data from its mean (average value). For example, a portfolio with an average return of 10% and a standard deviation of 15% would return a result between -5% and +25% the majority of the time (68% probability or 1 standard deviation), almost all of the time the return would be between -20% and +40% (95% probability or 2 standard deviations). If there were 0 standard deviation then the result would always be 10%. Generally, more aggressive portfolios have a higher standard deviation and more conservative portfolios have a lower standard deviation.

 

Asset-Based Finance: Why Manager Selection Is Important

 

In asset-based finance, who you invest with matters as much as whether you invest at all.

In most asset classes, manager selection matters. In asset-based finance (ABF), it is the defining variable. The qualities that determine outcomes in ABF—sourcing relationships, structuring discipline, data infrastructure, ongoing surveillance—are not easily replicated, nor quickly built. For allocators, , understanding what separates a capable manager from a genuinely differentiated one is as important as the allocation decision itself.

An Operationally Demanding Asset Class

ABF is not simply private credit with different collateral. It is operationally distinct in ways that have direct consequences for risk and return. Diligence processes are data-intensive, structures are highly bespoke, and outcomes depend as much on underwriting discipline and documentation quality as on portfolio construction. Complexity is a key marker of the asset class—often, positions are backed by thousands of underlying loans.

This complexity demands an end-to-end analytics capability. Effective underwriting requires ingesting large, often complicated datasets; translating historical loan-level performance into base, adverse and severe scenario projections; and using those outputs to structure robust protections that hold up under stress.

Critically, that diligence does not end at close. Effective portfolio management depends on frequent monitoring and a clearly defined early-warning playbook for when asset performance deviates from expectations. In ABF, the ability to act early—before problems compound—is a key for a successful manager. It requires the infrastructure and discipline to see the signal before it becomes a problem.

A More Crowded Market

The growth of ABF has attracted a significant influx of new capital and new entrants. That is not inherently problematic, but it has made manager selection considerably more complex. Strategies are difficult to compare on a like-for-like basis; product labels are inconsistent; and the distinction between genuinely differentiated managers and those offering little more than broad beta exposure is not always immediately apparent.

That distinction matters enormously. The most attractive opportunities in ABF—shorter-duration, higher-yielding and more bespoke transactions with stronger downside protection—are not widely accessible. They require repeat access to originators, disciplined structuring capability and the breadth to compare opportunities across sectors, geographies and capital structures. These are capabilities that take years to build and cannot be acquired through capital alone. Investors should be alert to the difference between managers who can access the ABF market and those who can navigate it with genuine edge.

The Characteristics That Define a Differentiated Manager

In our view, five characteristics distinguish the most capable ABF managers from the rest.

• Direct and differentiated sourcing. The best managers cultivate proprietary, direct relationships with originators that generate differentiated deal flow not available to the broader market. These are lifecycle partnerships, built over time, that provide preferential access to higher-quality transactions on better terms. Sourcing is not a commercial function in this context; it is an investment edge.
• Distinctive focus. Breadth without depth is a risk in ABF. The most effective managers maintain a clear focus—whether in receivables lending, consumer and small business loans or hard assets—that allows them to develop genuine sector expertise, build originator networks and underwrite with conviction. That focus is what enables consistent identification of the most attractive risk-adjusted opportunities within a chosen universe.
• A relative value framework. ABF spans a vast and varied opportunity set. Deploying capital effectively requires a disciplined, dynamic framework that can identify and compare risk-adjusted opportunities across sectors, geographies and capital structures in real time—and rotate toward the most attractive pockets as conditions evolve. Without that framework, portfolio construction becomes reactive rather than deliberate.
• Bespoke structuring focused on downside mitigation. Generic structures do not adequately protect investors in stress environments. The strongest managers negotiate bespoke terms—covenant packages, collateral triggers, over-collateralization levels—designed to withstand severe scenarios, not just base-case ones. That structuring discipline is the difference between a portfolio that holds up in a downturn and one that does not.
• Data science as a core capability. In ABF, data is not a supporting function—it is the foundation of investment decisions. Leading managers have dedicated data science teams deploying proprietary infrastructure to extract actionable insights from granular loan-level data. That capability drives more informed underwriting, more dynamic portfolio management and, ultimately, more consistent outcomes for investors.

The Bottom Line

ABF is an asset class where the gap between the best and the rest is wide—and where that gap is determined not by market timing or macro calls, but by capabilities that are structural, accumulated and hard to replicate. For investors, the question is not simply whether to allocate to ABF. It is who to allocate with—and why.

 

Disclaimer

Capital market assumptions used herein reflect Neuberger’s forward-looking estimates of the benchmark return or volatility associated with an asset class. Estimated returns and volatilities are hypothetical return and risk estimates generated by Neuberger’s Institutional Solutions Group.

Estimated returns and volatilities do not reflect the alpha of any investment manager or investment strategy/vehicle within an asset class. Information is not intended to be repre­sentative of any investment product or strategy and does not reflect the fees and expenses associated with managing a portfolio or any other related charges, such as commis­sions and surrender charges. Estimated returns and volatilities are hypothetical and generated by Neuberger based on various assumptions and inputs, including current market conditions, historical market conditions and subjective views and estimates.

Capital market assumptions shown reflect Neuberger’s long-term (20+ years into the future) estimates or intermediate-term (five to 10 years into the future) estimates which are reviewed at least annually. Results will differ depending on whether they are based on Neuberger’s long-term (20+ years into the future) or intermediate-term (five to 10 years into the future) capital market assumptions. Neuberger’s capital market assumptions are derived using a building block approach that reflects historical, current, and projected market environments, forward-looking trends of return drivers, and the historical relationships asset classes have to one another. These hypothetical returns are used for discussion purpos­es only and are not intended to represent, and should not be construed to represent, predictions of future rates of return. Actual returns may vary significantly. Neuberger makes no representations regarding the reasonableness or completeness of any such assumptions and inputs.

Assumptions, inputs and estimates are periodically revised and subject to change without notice. Estimated returns and volatilities should not be used, or relied upon, to make investment decisions.

Rate of Return Estimate: Rate of return or geometric return is a measure of average returns of an investment over a period of time. Geometric rates of return are typically referred to as annualized compound rate of returns and are always less than or equal to the arithmetic mean return of the same time series. Geometric rates of return are used for straight-line calculations within the analysis, for example, the cash flow calculations. In straight-line calculations, each year is represented as a gain, so the compound (geometric mean) rate of return is used to adjust for the amount needed to make up for a loss in a given year. For example, if you lose 5% in one year, and gain 5% the year after, you still have less than you started with at the beginning of year one.

Arithmetic Mean Estimate: Arithmetic mean or average return is calculated by dividing the sum of a series of numbers by the number of overall items. This is more typically thought of as an “average” of the data set. Arithmetic mean or average return ignores the impact of compounding in the context of analyzing investment returns and is the simple average of returns observed over a period of time. Arithmetic mean returns are used in this material and, if applicable, the Efficient Frontier, because, through randomization, losses and gains are being accounted for each year.

Standard Deviation: A statistical measure of the volatility based on the distribution of a set of data from its mean (average value). For example, a portfolio with an average return of 10% and a standard deviation of 15% would return a result between -5% and +25% the majority of the time (68% probability or 1 standard deviation), almost all of the time the return would be between -20% and +40% (95% probability or 2 standard deviations). If there were 0 standard deviation then the result would always be 10%. Generally, more aggressive portfolios have a higher standard deviation and more conservative portfolios have a lower standard deviation.