Thought Leadership
Industry
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Nov 25, 2025
4 minutes
The private credit market has grown into a $1.7 trillion asset class. But while assets have scaled, the operational engine supporting this growth - fund administration (FA) - has not evolved at the same pace. As lending structures become more bespoke, and reporting and regulatory pressures intensify, many managers now find themselves maintaining two parallel systems: the administrator’s ledgers and an internal “shadow” ledger that replicates and double-checks the FA’s work.
The result is a costly equilibrium. No one is truly satisfied with it, yet it persists because the perceived risks of changing the model feel higher than the pain of maintaining it.
This article explores why that equilibrium exists, what it costs managers in real terms, how technology can either become a reliable backbone for shadow booking, or replace portions of the FA function entirely, and why the next phase of evolution will be AI agents operating on top of this infrastructure.
Fund administrators perform several essential functions that underpin the integrity of private credit funds. They keep the fund’s official books, prepare NAVs and investor statements, support audits and regulatory reviews, and verify that key calculations—fees, waterfalls, and performance—are applied correctly.
Their role is foundational. But FA operating models were built for periodic, largely manual, and batch-driven workflows. As private credit has grown more complex, several pressures have strained this model.
Private credit transactions now span multi-tranche unitranche, mezzanine structures, delayed-draw mechanisms, PIK toggles, cross-currency facilities, structured fees, and covenants that evolve throughout the life of the loan. These often require intricate modelling that generic FA templates cannot easily accommodate.
LPs, regulators, and internal stakeholders increasingly expect intraperiod visibility into exposures, liquidity, performance, and risk.
Despite the size of the asset class, many private credit teams still depend on spreadsheets and manual data curation, with middle- and back-office teams spending significant time fixing breaks caused by disjointed data flows. This creates interpretation risk, slows workflows, and reconciliation burdens, none of which FAs are structurally designed to solve at scale.
The cost of fund administration rises sharply as private credit portfolios grow in size and complexity. Administrators charge more for higher AUM, greater transaction volume, multi-entity structures, cross-currency portfolios, and frequent amendments. Yet even as these fees increase, managers must still dedicate significant internal resources to validate and supplement the FA’s work. Operations analysts, fund accountants, controllers, and data teams maintain internal records to ensure accuracy, monitor positions intraperiod, and support investment decisions, because FA workflows, built around monthly cycles and manual processes, cannot keep pace with the daily evolution of private credit portfolios.
This parallel effort becomes most visible during month-end and quarter-end closes, when teams must reconcile data from the FA, loan agents, internal systems, and banking platforms. The result is substantial reconciliation overhead, with firms frequently delaying valuations, allocations, or capital movements until discrepancies are resolved. Manual processes and spreadsheets further fragment audit trails, increase key-person risk, and weaken institutional memory.
Combined, these factors create a costly and inefficient operating model. The expense is not only in the FA mandate itself, but in the internal effort required to correct, reconcile, and accelerate administrator output. And none of this work enhances yield, improves underwriting, or strengthens LP relationships—it simply compensates for a model that is too slow, too manual, and increasingly misaligned with the scale and complexity of modern private credit.
The good news is that the industry is no longer limited to a binary choice between “trust the FA blindly” and “run your own manual shadow book.” Purpose-built technology provides two viable paths:
Modern loan management systems are built to reflect private credit’s complexity accurately and without reliance on spreadsheets. They model complex structures, maintain live schedules and cash flows, support multi-entity and multi-currency setups, and record amendments or restructurings with a clear audit trail, providing managers a reliable operational backbone that establishes a true internal golden source of economic truth.
In that configuration, the internal system becomes the manager’s golden source of truth. Shadow booking doesn’t disappear, but it looks very different: instead of manually rebuilding calculations in spreadsheets, the manager relies on a fully automated, accurate internal record and uses it as the reference point for checking FA outputs. Reconciliations still occur, but they are narrower in scope and easier to executeץ
As managers establish a strong internal system of record, they can begin shifting meaningful portions of day-to-day work away from the fund administrator. The FA still provides independence, investor confidence, and audit support, but operational accounting, loan lifecycle tracking, and intraperiod reporting increasingly move into the manager’s own environment. Over time, this rebalancing reduces the complexity and cost of FA mandates, shortens close cycles, improves consistency between internal and external numbers, and enables firms to scale AUM without expanding back-office headcount at the same rate.
A practical example comes from Pinegrove Venture Partners, which used Hypercore’s loan management system to bring servicing fully in-house and remove the costly third-party administration services they had previously relied on, achieving major cost savings while supporting rapid portfolio growth without adding operational staff.
Once a manager has a reliable, structured operational foundation, the conversation naturally shifts from producing accurate numbers to operating the entire loan and fund workflow with far greater intelligence, speed, and autonomy. This is the environment in which AI agents will have their most transformative impact.
AI agents built specifically for private credit are not generic chatbots. They are domain-trained systems designed to understand deal structures, workflows, rules, timelines, and the operational rhythms of private markets. Their role is to convert the manager’s clean data and standardized processes into a continuously running operational layer, one that can take on work traditionally reserved for large teams or external service providers.
With structured data and well-governed workflows, AI agents can begin automating entire categories of operational work:
Once these components come together, the AI agent becomes the “always-on operator” - one that works continuously, without fatigue, delay, or version drift.
The operational model that private credit relies on today developed out of necessity, not design. As portfolios grew more complex and expectations for accuracy and intraperiod insight increased, managers built internal processes to compensate for the limitations of traditional fund administration. These parallel systems have kept the industry running, but they also impose a structural ceiling on efficiency and scale.
Even before full digitization and the adoption of AI agents, the simple fact that the industry is now prepared to challenge long-standing workflows signals a meaningful shift. “Good enough” is no longer sufficient in an environment where investors expect precision, speed, and transparency, and where competition demands tighter execution. That willingness to rethink the operating model is the inflection point. It marks the beginning of a broader technological disruption, one that will reward the firms that modernize early with a durable operational advantage as the asset class continues to expand.
Tech
Competitive
Nov 18, 2025
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