Author: Tony Chung
Beyond returns: The new transparency standard in private credit
The private credit landscape is undergoing a fundamental shift. As LPs increase their allocations, they are moving beyond simple return metrics to demand deeper insights into how those returns are generated. In a recent fireside chat at Pension Bridge Private Credit, I sat down with Bill McMahon, Director at Alpha FMC, and discussed the emergence of "Track Record 2.0" and how firms can leverage AI and modern operating models to meet these evolving demands.
The rise of “Track Record 2.0”
The traditional “track record” is no longer sufficient for sophisticated LPs. Today’s investors are prioritizing underwriting credibility over raw performance. As McMahon put it, “It’s no longer just about the explicit returns…investors really want to understand the credibility of your underwriting.”
This new framework, which he called “Track Record 2.0,” requires GPs to demonstrate how a deal has performed against its original underwriting assumptions, not just where it ultimately landed.
LPs are now looking for granular, deal-level transparency, including:
- Fundamental trajectories: Tracking leverage, EBITDA growth, and service coverage ratios from underwriting through maturity.
- Stress-period performance: Demonstrating how portfolios behaved during volatile environments, such as the COVID-19 pandemic.
- Total portfolio views: Providing capitalization context that reflects the rise of SMAs and co-investment strategies, where allocators are increasingly underwriting alongside managers.
But transparency isn’t simply about providing more data. As I added to his comments, LPs want more speed, but not at the expense of accuracy and auditability. Transparency must be fast, but it must also be certified, defensible, and investor-ready.
The infrastructure problem: A fractured ecosystem
One of the primary obstacles to achieving this level of transparency is what McMahon described as a “fractured ecosystem.” Critical information is often siloed across fund administrators, loan servicing platforms, and legal documentation. As firms diversify strategies and expand product offerings, those silos multiply.
A common failure mode, McMahon explained, is the inability to distinguish between what should be standardized across strategies and what must remain unique to a specific asset class. Without that clarity, firms end up “repeating the same frameworks…collecting, consolidating, and manufacturing the insights that are relevant to their strategy,” but with “very little capability across strategies to be able to blend those together.”
The result is reporting that works at the strategy level, but breaks down at the platform level. And in the era of Track Record 2.0, platform-level coherence is exactly what LPs expect.
AI as an accelerator, not a replacement
In private credit, AI’s value isn’t theoretical—it’s operational. The most immediate impact is in the middle office, where teams are processing high volumes of transaction-level documentation, cash movements, and loan-level reporting. As McMahon explained, AI is being used to automate “structured and certainly unstructured data in an automated way and providing operational efficiency,” including agent notice processing, financial remittances, and collateral tapes
In practice, that means:
- Parsing covenant changes from agent notices
- Reconciling remittance data against servicing systems
- Extracting borrower-level metrics from collateral tapes
- Standardizing inputs for downstream reporting and underwriting models
These are high-frequency, detail-sensitive workflows, and small errors compound quickly. AI increases throughput and reduces manual intervention, particularly in strategies with large loan counts or complex capital structures.
But neither speaker framed this as automation for automation’s sake. We both agreed: AI must enhance professionals, not replace them. McMahon emphasized that “Ultimately, this is a relationship and human-driven business.” AI can create scale, but credit underwriting still depends on contextual judgment—sponsor quality, covenant flexibility, restructuring optionality, sector nuance. AI can benchmark a deal against historical patterns; it cannot decide whether to flex a structure in a volatile market.
To preserve trust, AI must be layered on top of connected infrastructure, not used to compensate for fragmented data. McMahon put it plainly: AI should only be introduced “on the back of a fully vetted and trusted infrastructure” and used to surface information that has already been validated.
The firms that will win in this environment aren't just adopting AI, they're building the certified data infrastructure that makes AI trustworthy. Speed without auditability isn't a competitive advantage; it's a liability.
Building a best-in-class operating model
If firms want to scale without a linear increase in headcount, their operating models must evolve. Looking ahead 12–24 months, the two outlined three priorities:
Deep partnerships
Firms should avoid duplicating their administrators' work. As McMahon observed, many credit shops are unintentionally “duplicating efforts of their administrators or of their partners.” Instead, managers need “the right administration partner that gives you full transparency and access to everything that you're using to really tell your investment story.” Shadow operations create friction. Deep integration with a partner provides the "certified" data foundation necessary for everything from LP reporting to AI deployment.
Process discipline
Best-in-class firms focus relentlessly on investing, “seeing as many relevant deals as they can…triaging through an underwriting process, and executing with speed.” The objective is clear: increase deal throughput and profitability without introducing proportional headcount. As McMahon put it, “You don’t want a linear model in terms of profitability.” Technology and AI are the levers that break the link between AUM growth and headcount
Define baseline data requirements
For firms looking for a practical starting point, the advice was straightforward. McMahon recommended that managers “really need to understand…the baseline information that you require to really deliver the competent stories and capabilities that your firm needs.”
This baseline will differ by asset class (e.g., corporate credit vs. real estate debt). Without defining these metrics early, the "Track Record 2.0" that LPs demand remains purely aspirational.