The Private Credit Operational Gap Is Growing. Is Your Fund Ready?

Private credit has evolved into a multi-trillion-dollar asset class, fueling a surge of new funds and strategies. New vehicles, like NAV‑based fund financing, have gone from niche to mainstream, reflecting unprecedented demand that has increased global private credit AUM roughly threefold. This boom is attracting non‐bank lenders and pushing deal sizes into the billions.

At the same time, however, rapid scale is straining the back office, where liquidity pressures and larger portfolios are colliding with outdated workflows. Fund managers and ops teams report that LPs are demanding extremely detailed reporting, even as systems struggle to keep up. In short, growth is outpacing operational capabilities.

Unparalleled Operational Challenges in Private Credit

Private credit’s complex deal structures and investment lifecycle differ significantly from those of public markets or traditional private equity. Private credit firms face uniquely labor‑intensive operational processes:

• Unstructured deal data: Credit agreements and loan documents are often bespoke PDFs, so data must be manually ingested and mapped. Much of the data is often still locked in siloed systems, in‑house Excel sheets, and unstructured documents, making scale almost impossible without added infrastructure. Increasingly, firms are turning to AI- and GenAI-powered document parsing to automate data extraction, transforming PDFs into structured, usable data in minutes.
• Bespoke loan terms and covenants: Each loan can have unique collateral requirements, covenant tests, guarantors, payment-in-kind options, and more. Loan terms vary among issued instruments, requiring a sophisticated, pliable platform to master all aspects of a loan. GenAI models are now being trained on historical agreements to tag key clauses and surface exceptions automatically, reducing review cycles significantly.
• High volume of servicing events: Repayments, interest resets, payment‑in‑kind (PIK) elections, prepayment penalties, and modifications occur frequently. Each event must be recorded and reconciled. Firms must handle various asset servicing events and treat them appropriately. AI-powered automated event tagging is emerging as a solution, offering real-time reconciliation with less operational friction.
  
• Persistent manual workflows: Unlike exchange‑traded instruments, private loans settle OTC. BNY Mellon reports that “end-to-end operations and reconciliation still rely heavily on manual, legacy processes,” leading to inefficiencies and errors. In practice, loan settlement often takes 10 to 25 days. AI and workflow engines can orchestrate task assignments, exception routing, and audit tracking, drastically reducing lag time.
  
• Data integration and reporting gaps: With multiple data sources (administrators, custodians, trustees), firms struggle to deliver a unified portfolio view. According to industry surveys, more than half of alternative fund managers cite issues with real-time data access or integration across functions. GenAI-driven data normalization and AI-enhanced dashboards enable real-time views across disparate systems, improving not just transparency but decision speed.
  

Together, these factors confirm that private credit operations are uniquely complex. Many processes remain manual — from data entry and reconciliation to covenant monitoring and investor reporting — because no standardized pipeline exists. Managers using spreadsheets and PDF‑based workflows must allocate significant staff just to keep the lights on, while peers who invest in automation and AI are scaling more efficiently.

The Widening Operational Divide

These challenges are contributing to an operational gap between fast-scaling firms and those clinging to legacy tools. Leading managers are aggressively modernizing, and most private credit executives plan to ramp up automation and AI/GenAI spending this year. In contrast, many mid‑tier and smaller firms still rely on spreadsheets and manual reconciliations. As one industry observer notes, “direct lending is still maturing compared to the bond market, and many players are managing portfolios with outdated tools like Excel. Transitioning to structured, digital platforms is critical — not just to remain relevant, but to scale effectively.”

The performance difference is stark. Firms with modern data infrastructures report faster closing of NAVs, more timely risk reports, and greater operational resilience, while those without this infrastructure face growing backlogs. Frequent reconciliation is now considered crucial for operational efficiency1, but many fund managers still find themselves scrambling to fix errors when reports are due, rather than doing this proactively. In practice, funds relying on legacy systems experience days-long lags in NAV calculation and investor statements during times of heavy activity. On the other hand, funds with unified platforms can leverage AI to reduce reconciliation errors and generate integrated views of portfolio performance and risk in near-real-time. Without urgent action, under‑resourced operations will become a competitive liability.

The Urgent Need for Technology Modernization

Closing the rapidly widening operational gap requires rapid technology and process upgrades. Key capabilities include:

• Unified data infrastructure: Implement a single data platform or cloud data lake to ingest and normalize all deal, fund, and market data. Modernizing infrastructure is the key to helping private credit firms ingest, clean, normalize, and distribute data to streamline processes. A centralized data model ensures that portfolio, accounting, and risk teams work from the same master data, eliminating silos.

• Automation and cloud platforms: Move middle- and back‑office functions to cloud-based platforms with workflow automation. OCR and GenAI can replace manual PDF processing, instantly extracting covenants and terms. For example, what once took hours of manual document review is now handled in seconds with GenAI-assisted clause detection. Likewise, rules‑based engines can handle interest accruals and amortization schedules without spreadsheets.

• Artificial intelligence and analytics: Embed AI tools for classification, data tagging, anomaly detection, and predictive analytics. Early adopters are training GenAI on proprietary datasets to produce first-draft investment memos, LP narratives, and even Q&A responses, saving valuable analyst time.

• Cloud and scalability: Public cloud infrastructure provides the agility that legacy systems lack. A modern, cloud-based platform without legacy constraints enables firms to adopt AI/GenAI at speed and scale. This also allows seamless integration with third-party data feeds and downstream systems without rekeying.

• Cross‑asset integration: For multi-asset managers, choosing systems built for both private and public assets ensures consistent accounting conventions and avoids mini-silos. A consolidated ledger (ABOR/IBOR) lets teams compare risk exposures across equity, credit, and other strategies.

Investing in these technologies yields immediate strategic value, with 69% of fund managers surveyed citing operational efficiency as the top reason to invest in tech.1 Those who have modernized report faster processing and improved audit trails as well as reduced headcount requirements, freeing staff to work on higher‑value tasks. Conversely, each quarter of delay and each manual spreadsheet risk undermines investor confidence. In a competitive fundraising environment, robust data and reporting capabilities are almost as important as a track record.

Bridge the Gap with an Integrated Platform

The private credit industry has reached a critical inflection point. Growth will continue, but operational complexity will only increase — from Basel III capital trades to new asset-backed lending structures. Funds cannot simply add more people to keep pace, so it is crucial to rethink technology foundations. Forward-looking managers treat these modern platforms as strategic tools rather than optional add-ons.

IVP for Private Funds integrates the IVP Security and Reference Master, the centralized IVP Data Warehouse, and IVP OMS (order management system), providing fund managers with a single source of truth across the enterprise. It provides an end‑to‑end platform for complex private credit and multi‑asset portfolios. By unifying data, automating processes, and delivering real-time reporting powered by AI and GenAI, IVP for Private Funds enables managers to close the operational gap and respond swiftly to market changes. Only firms that embrace cloud infrastructure, AI-powered analytics, and GenAI-driven intelligence will be ready for the next wave of private credit growth, and IVP for Private Funds is built exactly to meet that challenge.

1. “A Market in Transition: Optimizing CLO and Credit Fund Operations,” BNY, May 1, 2025.