The New Engine of Private Credit: Why ABF Demands Better Infrastructure

Warm summer tailwinds are picking up once again for private credit markets as we dive into the second half of 2025. US legislators are considering a multibillion-dollar tax break on dividends paid by business development companies, a key private credit vehicle. The SEC's new chairman Paul Atkins wants to expand investor access to private markets. The US House of Representatives is also moving to expand the definition of accredited investors who are eligible to invest in certain private offerings.ⁱ The private equity (PE) slump is also feeding private credit’s fire. 

LPs are exploring alternatives to their alternatives, seeking differentiated deals in stressed markets and niche sectors like real estate, pooled consumer loans, small- and medium-sized business (SMB) loans, infrastructure, and litigation funding. Firms are finding new growth engines in strategies like asset-based finance (ABF), which offers opportunities to diversify with a more liquid form of lending that uses a company's assets as collateral. ABF has skyrocketed to a popular private debt strategy, with 24% of private debt investors indicating they wanted to commit more capital in the next year.ⁱⁱ

We expect ABF to soar in popularity, especially if firms can solve the operational and data management complexities that come with complex asset servicing, collateral tracking, and lifecycle management. Eager to merge into the new strategy, firms have been employing “shadow systems” to spin up ABF business quickly. While we applaud the intention, we caution against a premature launch using existing systems if they are cobbled together point solutions, unmanaged data stores, and shared drives.  

Manual or automatic: handling speedy private debt vehicles  

Hopefully, firms have talented analysts and managers that can deftly execute elaborate private debt vehicles. Preferably, they have operational processes in place to support new requirements for data collection, monitoring, and reporting. And ideally, these are not manual processes in which people have to chase down the right data. For example, servicers like Lending Club, BlueVine, and SoFi provide structured loans backed by assets like recurring revenue, invoices, equipment, or inventory. Each lending platform has distinct operating models, file formats, and methods of sharing data. Most funds starting their first foray into ABF are engaged in a complex, multi-stage process on Excel with Python and other proprietary scripts, with numerous touchpoints and manual interventions. 

ABF focuses on asset valuations rather than a borrower's credit score. That calls for superlative monitoring of the real-time value and performance of underlying assets valuations and efficient borrowing base calculations. Collateral tracking must be precise for interest accruals to be precise. 

Offline interest calculations for sound liquidity management 

Some fund administrators can only run standard accruals that harken back to a time of simple debt instruments like milquetoast bonds. Managers that are scaling or entering ABF territory will become bogged down if employing manual accounting methods. ABF products generate interest daily but may only pay monthly, quarterly, or irregularly. ’40 Act funds that have daily redemptions must have accurate NAVs cut daily. Interval funds allow quarterly redemptions, but performance reporting sometimes demands a daily NAV. Generally, ABF assets are not marked every day, but some fund wrappers require it. In any case, firms can ill afford to have liquidity mismatches in any of the complex private credit vehicles. 

ABF also brings certain nuances to accounting lifecycle events from drawdowns, prepayments, and rollovers to payment in kind and amortization via linear or scientific methods. Offline interest calculation models enable accountants to sync actual payments vs expected accruals by grabbing accrual snapshots, waterfall analysis, and NAV forecasts ahead of closing.  

Hidden risks of legacy systems 

Every dollar of accrued interest, principal amortization, prepayment, fee income, and default loss must be properly attributed. But imagine executing this shadow accounting using manual data extraction and validation, without a centralized accrual model or unified data feeds. Imagine trying to dole out the right dollar amount of profits by manually calculating the carried interest portion of distribution waterfalls. We are not merely talking about inefficiency. We’re talking about perturbed stakeholders receiving bad reports and incorrect fees. It could result in missed borrower delinquencies, defaults, or restructuring. No fund accountant needs waterfall triggers slipping through the cracks and errant P&Ls. These catastrophes deter fundraising and land directly on the bottom line.    

Observe and report  

Keeping lenders, investors, and regulators happy via risk–adjusted returns, accurate fees, and real-time reporting is paramount. Flexible reporting depends on systems that support the real-time generation of performance analytics including IRRs and multiples, TWRs, performance attribution, and contribution. As transparency and trust grow more crucial, managers must be ready to respond to investors who are accustomed to near-instant data access. 

Arcesium’s UBOR, our books-of-record engine, enables multiple standard reports that support essential operations, including daily NAV, investor statements, trial balance, as well as cash flow and cash activity reporting. The technology centralizes performance analytics across all asset classes and lending platforms. Managers can deliver accurate tracking and reporting of the costs of investments, returns, and distributions across packaged loans, consumer loans, residential loans, and more. 

Critical infrastructure decisions for ABF 

Technology-forward firms that have adopted comprehensive automation and integration of deal lifecycle operations can process all loan life-cycle events either from activity files or generating differences between daily, weekly, or monthly position snapshots. This ensures accurate up-to-date positions and cash balances so they can collect timely measurements of fund performance, synthesize attribution, daily NAV calculations, accurate pricing, and portfolio tracking. 

Firms that use cloud-native systems that connect middle- and back-office data and operations powerful workflow and reporting capabilities to support complex lending strategies. This offers managers the competitive advantage of end-to-end loan setup management capabilities so the front office can generate precise security masters. Subsequently, they will be able to seamlessly handle collateral tracking, advance rates, and covenants. They will save time and prevent errors with automated interest accruals, fees, and waterfalls. They won’t get tripped up by non-standard cash flows and investment lifecycle events. Best of all, cloud-based fuels growth. 

Scale or perish 

Stubborn reliance on patchwork tech stacks and Python scripts only creates fragility and inefficiency just when scale and control matter most. Legacy technology creates migraines when trying to scale the business. Established players that have well-developed infrastructure and experience have an advantage. But new ABF entrants may face operational challenges and technology costs. These obstacles can make entry into the market difficult but also “create opportunities for differentiation in a competitive, yet less crowded market.”ⁱⁱⁱ Firms best positioned to grow AUM via ABF strategies and satisfy investor demand will be those that swiftly and smoothly incorporate ABF products and scale with the increased trade volumes. Modern, cloud-based operational platforms are more scalable, less resource-intensive, efficient, and reduce operational costs.  

Some ambitious ABF managers are thwarted at the gate by the inability to onboard new datasets or difficulty building data models that support asset- and facility-level granularity. Data platforms purpose-built for today’s private markets, like our own Aquata, increase the velocity at which new datasets are made available, to drive analytic modeling and strategy development. It offers elasticity on-demand, allowing firms to scale resources as needed. See our previous article for information on the 6 Indicators Your Data Platform Is Not Ready to Scale.  

Smooth running dataflows from rocky data sources 

The chief data scientist is often tasked with supporting the sourcing and aggregation of structured, consistent data from an array of alternative lending platforms, originators, and servicers. Unless their data infrastructure can pull in data and validate it from numerous sources dealing with multiple borrowers and data for the underlying collateral, ABF strategies will trudge along with underachieving returns and disgruntled investors. This demands access to the best normalized data available and the ability for weather shocks from managing exponentially larger data and servicing needs of ABF, relative to traditional direct lending. See our previous article: ABF Is a Data Business — Most Firms Just Don’t Know It Yet.

Transform legacy systems to grow ABF 

Private credit has demonstrated powerful endurance in the face of volatile economic conditions and uncertain policy. ABF has a remarkable upside as managers discover its potency and figure out how to solve its complexity. Blackrock’s new target-date fund includes private credit, offering millions of Americans access to private markets through their retirement savings accounts. On July 1st, Monroe’s Specialty Finance division announced its partnership with AIP to grow asset-based finance business into the aviation sector, with $500 million already in the till. Blue Owl’s head of credit Craig Packer believes the growth of the ABF opportunity set will remain accelerated as private capital continues to replace bank and even public market solutions.^iv   

 Private market funds in pursuit of ABF opportunities must guard against a one-size-fits-all approach that overlooks the unique aspects of different asset types. Their modeling must be unique to different asset types, like consumer loans versus residential loans. Firms need to step back from manual shadow banking like offline interest calculations, email-based approvals, and unmanaged data stores, if they are to optimize ABF strategies. 

Key Takeaways: 

Q1: Why is ABF gaining popularity in private credit markets? 

A1: ABF offers liquidity and diversification, structural flexibility, and risk-adjusted returns, appealing to investors seeking differentiated strategies amid private equity slowdowns and regulatory tailwinds. 

Q2: What are “shadow systems” and why are they risky? 

A2: Old infrastructure leads to a , failure to manage increased complexity of securities and growing AUM that direct lenders like Sixth Street haven't managed in the past.  

Q3: What challenges make ABF operationally complex? 

A3: ABF requires real-time asset valuation, precise interest accrual, collateral tracking, and lifecycle event management—all difficult to scale manually. 

Q4: How can firms modernize ABF operations? 

A4: By adopting cloud-native platforms that unify data, automate lifecycle events, and support real-time analytics, firms can scale efficiently and compliantly. 

Q5: What’s the impact of poor systems on ABF performance? 

A5: Inaccurate NAVs, delayed reporting, and failed compliance can derail investor trust and performance, limiting growth and fundraising potential

Sources:

i. Barron’s, June 24, 2025. https://www.barrons.com/advisor/articles/accredited-investor-bill-passes-house-3febf5333:43

ii Private Debt Investor, February 3, 2025.https://www.privatedebtinvestor.com/lp-perspectives-2025-read-the-full-report-here/3:43

iii Macfarlanes, The growth of asset-based finance in private credit markets, January 29, 2025. https://www.privatecapitalsolutions.com/insights/the-growth-of-asset-based-finance-in-private-credit-markets3:44

iv https://alternativecreditinvestor.com/2025/06/27/blue-owl-stability-of-private-credit-markets-amplified-by-market-volatility/