Winning the Race to the Asset-Based Finance Opportunity

Winning the Race to the Asset-Based Finance Opportunity

The private credit boom is still booming, with seemingly endless demand. As macroeconomic uncertainty continues to prevail, firms and investors are happy with private credit’s risk-adjusted returns, flexibility, and distance from equities markets. As funds warm themselves around the private credit fire, they realize they need to throw new strategy logs on to keep it going strong. Asset-based finance (ABF) has emerged as an effective sub-strategy for funds seeking diversification and stable returns. 

The private markets may still be expanding, but that doesn’t guarantee success for every fund. Firms must tackle operational complexity, data challenges, and technology integrations that come with private credit strategies like ABF. Success hinges on firms giving their smart analysts and ambitious managers the tools to drive returns: automation and scalable data infrastructure to manage growing portfolios and enhance decision-making. Here is an inside look at the opportunities and obstacles in launching, expanding, or merging into ABF strategies. 

READ OUR WHITEPAPER: The Future Outlook of Asset-Based Finance 

Expanding the reach of private credit markets

Private credit funds are exploring new allocation strategies to branch out from the saturated direct lending market, which has displayed some slowing signs¹. Banks are partnering with funds. Pension funds are delving deeper into alts. Investors are avoiding crowded trades and seeking out differentiated deals in stressed markets and niche sectors. 

Ingenious managers are exploring alternatives to their standard alts, like unitranche financing, NAV lending, and specialty finance, aka ABF. KKR added $12 billion in new capital raised during the fourth quarter, bringing the 2024 total of $56 billion for private credit including asset-based finance strategies. Neuberger Berman recently announced that it raised over $1.6 billion for a fund that focuses on asset-based investments. 

ICYMI: Arcesium Provides Technology to Support Neuberger Berman’s Specialty Finance Asset-Based Financing Operations 

The ABF Advantage

As direct lending gathers more default risk, the ABF is a sensible opportunity to diversify with a more liquid form of lending that uses a company's assets as collateral. While large enterprises have traditionally dominated the ABF sector, small and medium enterprises are increasingly turning to ABF. This shift is driven by the sector's flexibility and accessibility, which are particularly valuable in the face of economic uncertainty and stringent traditional lending criteria. Tangible (and semi-tangible) assets like real estate, pooled consumer loans, infrastructure, royalties on intellectual property, litigation funding, and supply chain finance are providing innovative forms of collateral for more conservative investing. Moody’s observed that banks are also driving ABF growth², as they step away from riskier credit exposure and toe the line on risk-based capital requirements. 

Overall, competition is intense, pushing market participants to search for ways to differentiate themselves. McKinsey observed³, “Scale (as measured by the depth and breadth of capital) and the effective use of technology represent two ways to do this.” The ABF market ecosystem is not created equal in the battle to satisfy investor demand. As with any private market allocation strategy, firms that can swiftly and smoothly incorporate ABF products and scale with the increased trade volumes will be best positioned to seize the moment to grow AUM. Then there’s the data problem. The highest performing firms will need to have the modern data infrastructure that can handle the new datasets. 

Data delirium: Critical role of data management in ABF success

The digitally transformed financial markets are complicated. Private market portfolios are even more complicated. Today’s ingenious fund structures favor multi-strategy and multi-manager platforms, and public-private interval funds. Such sophisticated structures and strategies have made hiring an excellent Chief Data Officer or Head of Data Science almost indispensable. 

As ABF effectively becomes a notable sub-strategy of private credit business lines across the board, onboarding a new ABF program will introduce an entirely new dialect of investment data language. If the firm works manually on data that is not standardized and synchronized, it will result in slow and imprecise reporting to investors and regulators. Additionally, this approach will hinder exposure and risk tracking, negatively affect performance, lead to inaccurate attribution and position measurements, and limit the ability to generate actionable analytics. 

Private funds and asset managers can ill afford to distribute inaccurate reports, such as capital and liquidity reporting, transaction reporting, regulatory returns, client stock and asset records, customer fees and charges. Regulator and investor trust is a coveted differentiator. Poor response time and errant disclosures erode the confidence of critical stakeholders, including the internal ones. Private credit may be an opaque investment class, but portfolio managers need fully transparent information to conduct meaningful analyses and drive superior returns. 

Overcome the operational challenges of ABF

Whether a private fund originates an ABF fund, a fund executes a significant risk transfer (SRT) transaction, or a fund invests in securitized asset-backed securities, it will discover that ABF is more operationally intensive than traditional lending due to higher loan volumes, partner and integrate with different originators and servicers, the need to manage and track collateral, all compounded by the journey from quarterly to daily NAV cycles (if distributing registered products to private wealth) and different reporting needs (if having an insurance partner). A fund’s infrastructure and operations must be capable of harmonizing reference and transactional datasets from counterparties, loans and other instruments, pricing, positions, and all lifecycle events from different asset and sub-asset classes, and in support of any geography. 

Currently, most funds initiating their first business in asset-based finance will deploy a team to implement a complex, multi-stage process on Excel with Python and other proprietary scripts, with numerous touchpoints and manual interventions. It is imperative to streamline and automate workflows, providing firms with a systematic and structured approach to process orchestration and management. 

For example, a firm funding consumer loans likely buys loans in increments of $150,000 to $200,000. However, they must manage the details of individual loans with payments as low as $200, $400, or $1,000. The data mastering headache is security creation -- the need to model every single loan at the fundamental borrower level, the type of loan, the industry, credit scores, etc. This requires the efficient sourcing of data from multiple alternative lending platforms, accurately extracted from the loan tape. 

Firms need to be able to hop on and quickly monitor their portfolios, checking loan performance, checking FICO scores to gauge risk, looking at monthly repayments and debt repayments, and monitoring events such as defaults or bankruptcy. Operations needs the ability to process all loan life-cycle events either from activity files or generating differences between daily, weekly, or monthly position snapshots to ensure accurate up-to-date positions and cash balances. These capabilities depend on firms using systems that connect middle- and back-office data and operations, so the front office can tap a precise security master. Only then can they collect timely measurements of fund performance, synthesize attribution, accurate pricing, daily NAV calculations, and portfolio tracking. 

Future of ABF: Who will win?

Funds looking to scale ABF must prepare for the operational challenges that come with the sub-strategy’s complexity. To seize the burgeoning opportunity of ABF, private market funds should guard against a one-size-fits-all approach that overlooks the unique aspects of different asset types. The modeling must be unique to different asset types like consumer loans versus residential loans. The ABF market opportunity is not satisfied by firms using brute force, proprietary technology, and Python scripts. The private market managers that solve these problems will capture the ABF checkered flag and speed ahead to their next phase of growth. 

This article was first published by Alternatives Watch on March 12, 2025. Click here to view the original article.