Judgment at scale: How AI is changing the deal management process in private credit and secondaries

The Unique Data Intensity of Deal Management in Alternatives

To understand where AI adds value in deal management, it helps to map the information architecture of
the deal process in private credit and secondaries specifically — because it differs substantially from
M&A deal processes or public market investment workflows, and most AI deal management tools have been designed primarily for those contexts.

Private Credit: From CIM to Close to Covenant Compliance

A direct lending transaction begins with an information event — typically a Confidential Information
Memorandum (CIM) distributed by an investment bank or advisor — and unfolds through a series of
analytical stages, each with distinct data requirements. The initial screen requires rapid extraction of
headline financial metrics: revenue, EBITDA, leverage multiples, interest coverage, management equity
ownership. The investment committee presentation requires granular financial model analysis: downside
scenarios, covenant headroom projections, sensitivity analysis across rate and performance variables.
Legal due diligence generates a documentation set — credit agreement, intercreditor agreement, security
documents, management representations — that must be analyzed for structural protections and risk
provisions.

After close, the data burden does not reduce — it transforms. Quarterly financial statements must be
mapped to the credit agreement’s defined financial covenants. Amendment requests must be modeled for impact on covenant headroom and return expectations. PIK interest accruals must be tracked against modeled assumptions. Delayed-draw term loan availability must be reconciled with drawn amounts and commitment schedules. Each of these post-close tasks is recurring, structured, and data-intensive — and in the aggregate, they represent a significant portion of a private credit team’s operational bandwidth.

Secondaries: Valuation Density Across a Portfolio of Portfolios

Secondary transactions present a different but equally demanding information processing challenge. A
portfolio acquisition may involve stakes in twenty or thirty funds across multiple vintages, strategies,
and geographies. Each underlying fund has its own NAV reporting, its own capital account statements, its
own portfolio company list, and its own management commentary. Synthesizing this information into a
coherent view of aggregate portfolio value, risk concentration, and liquidity profile — the analytical
foundation for pricing — requires processing volumes of data that make individual credit underwriting
look comparatively streamlined.

GP-led continuation vehicles present their own information challenges. The buyer must evaluate not just
the current NAV of the assets being rolled into the continuation vehicle but the credibility of the GP’s
forward business plan, the reasonableness of the exit assumptions embedded in the return model, the
alignment of interests created by the GP’s own capital commitment, and the structural protections
negotiated on behalf of incoming investors. This is simultaneous financial, operational, and legal
analysis, compressed into a deal timeline that competitive secondaries markets have made progressively
shorter.

Where AI Is Creating Measurable Value in Deal Management

Document Extraction and Structured Data Creation

The single highest-value application of AI in deal management for private credit and secondaries is the
automated extraction of structured data from unstructured documents. This is not a glamorous capability
— it does not involve sophisticated pattern recognition or predictive modeling. But it addresses one of
the most persistent and costly bottlenecks in the deal process: the manual extraction of financial data
from CIMs, offering memoranda, quarterly fund reports, portfolio company financial statements, and legal
documents.

In a conventional deal management workflow, an associate or analyst receives a CIM and spends several
hours manually transcribing financial data into an Excel model: pulling revenue and EBITDA figures
across historical periods, entering management projections, building out the debt structure, and
populating the initial screening template. AI-powered document extraction tools can perform the
equivalent of this transcription step in minutes, with high accuracy for structured financial data and
improving accuracy for less structured elements like management discussion sections and footnotes.

The productivity implication is significant. If an associate who might manually process four CIMs per
day can instead process twelve with AI support — applying analytical judgment to the output rather than
time to the extraction — the capacity of the investment team to evaluate opportunities without
proportional headcount growth increases meaningfully. In competitive private credit markets where the
volume of opportunities reviewed relative to investments made can run at ratios of twenty or thirty to
one, this capacity expansion translates directly into a broader opportunity set and potentially better
selection.

AI-Augmented deal scoring

Deal scoring — the systematic evaluation of pipeline opportunities against a defined set of
investment criteria — is a capability that the most operationally sophisticated asset managers have
been developing for years. The insight behind it is straightforward: not all opportunities that enter
a pipeline deserve equal analytical resources, and a structured scoring approach helps direct those
resources toward opportunities that best fit the investment strategy, improving both the efficiency of
the evaluation process and the quality of the resulting portfolio.

AI augments deal scoring in two specific ways. First, it enables the scoring model to incorporate a
broader and more current data set than manual scoring allows. An AI-augmented scoring system can
factor in market data on comparable transactions, sector-specific credit performance patterns,
historical loss rates in similar deal structures, and portfolio concentration metrics — all updated in
real time rather than refreshed periodically. Second, it enables the scoring model to learn from the
firm’s own historical decisions, identifying which deal characteristics have historically been
predictive of strong or weak performance within the firm’s specific strategy, and weighting the
scoring criteria accordingly.

The critical governance principle for AI-augmented deal scoring is that the scoring output is an
input to human judgment, not a replacement for it. Experienced credit professionals and secondaries
practitioners have contextual knowledge that scoring models cannot capture — the relationship history
with a sponsor, the investment team’s qualitative assessment of management, the strategic logic of a
particular sector bet at a particular point in the cycle. A deal scoring system that generates a low
score for an opportunity that the investment team believes has genuine merit should prompt a
conversation about whether the scoring criteria need to be recalibrated, not an automatic pass.

Portfolio monitoring and covenant intelligence

Post-close portfolio monitoring is one of the most operationally intensive activities in private
credit management — and one where AI is beginning to deliver substantial value. The challenge is
structural: a portfolio of fifty or sixty credits, each with quarterly covenant compliance
obligations, generates a monitoring workload that grows linearly with portfolio size. As firms scale
their AUM without proportional increases in portfolio monitoring headcount, the risk of compliance
events being identified late — after they have had operational consequence — increases.

AI can automate the mechanical compliance testing component of covenant monitoring: ingesting
quarterly financial statements, mapping the relevant line items to defined covenant calculations, and
generating exception reports that flag credits approaching or breaching defined thresholds. This
automation is well-suited to AI because the task is highly structured, the inputs are defined, and the
calculation logic is deterministic. The human role shifts from performing the calculation to
interpreting the exception and deciding on the response.

The more sophisticated application of AI in portfolio monitoring moves beyond compliance testing to
early warning signal detection. By analyzing patterns in a borrower’s financial reporting over time —
margin trends, working capital dynamics, revenue concentration shifts, changes in management
commentary language — AI systems can identify credits where the financial trajectory suggests
potential future covenant stress before that stress becomes visible in a compliance breach. This early
warning capability is particularly valuable in private credit because the management options available
to a direct lender — a proactive amendment conversation, additional monitoring requirements, an
increase in the interest rate to reflect elevated risk — are more effective when exercised early than
when exercised reactively.

Scenario Modeling and Stress Testing at Scale

The ability to run comprehensive scenario analysis — testing portfolio performance under a range of
macro and credit-specific stress conditions — is a standard requirement for investment committee
presentations and LP reporting in private credit. In practice, the complexity of running meaningful
scenario analysis across a full portfolio has historically limited how thoroughly this work is done,
particularly at smaller and mid-market managers where analytical resources are constrained.

AI-augmented scenario modeling changes this by enabling the rapid generation of portfolio-wide
stress scenarios from parameterized inputs. A credit team that previously might have run two or
three scenarios for an IC presentation — base case, moderate stress, and severe stress — can run
twenty, varying credit-specific assumptions (sector-level revenue stress, interest rate sensitivity,
covenant headroom cushion) alongside macro factors (rate paths, credit spread movements, default
rate cycles) to develop a genuinely comprehensive view of portfolio risk and resilience.

For secondaries managers, AI-driven scenario modeling has particular application in the NAV
sensitivity analysis that underlies pricing decisions. A secondaries buyer evaluating a portfolio of
fund interests needs to understand how the aggregate NAV changes under a range of exit multiple and
timeline assumptions for each underlying fund. Generating this analysis manually across a portfolio
of thirty funds, each with ten or fifteen underlying portfolio companies, is a multi-day exercise.
AI-powered scenario engines can generate the equivalent in hours, allowing more of the analyst’s
time to be spent on the assumptions that drive the scenarios rather than on the mechanics of
producing them.

Comparable transaction analysis in secondaries

One of the foundational analytical inputs for secondaries pricing is comparable transaction data:
what prices have similar fund interests traded at, in similar market conditions, with similar
vintage and strategy profiles? This analysis is limited by access to transaction data — secondaries
is a private market with limited systematic price transparency — but within the available data, the
challenge has historically been one of retrieval and synthesis: which transactions in the firm’s
historical database or available market data sources are genuinely comparable, and how should they
be weighted in the pricing analysis?

AI can improve both dimensions of this analysis. On retrieval, natural language processing enables
more sophisticated searching of transaction databases — querying not just by fund strategy label but
by underlying portfolio characteristics, investment period, geographic exposure, and realized return
trajectory. On synthesis, AI can generate structured comparability assessments that articulate why
certain transactions are most relevant to the pricing at hand and flag transactions that may appear
superficially similar but differ in ways that matter for valuation.

The output of AI-assisted comparable analysis is a more efficiently generated and more
comprehensively considered pricing anchor for the secondaries team’s judgment. It does not replace
the judgment — the ultimate pricing decision in secondaries involves conviction about the quality of
the GP, the credibility of the NAV, and the competitive dynamics of the specific process, none of
which are fully captured in comparable transaction data alone.

The Decision Authority Spectrum: A Framework for AI Governance in Deal
Management

The question of how much AI to deploy in deal management — and at what points in the process — is one
of the most important governance questions for asset managers integrating these capabilities. The
following table sets out a proposed decision authority spectrum for AI in deal management, organized by
activity type and the appropriate division of responsibility between AI systems and human professionals.

The pattern in the table reflects a consistent principle: AI should have authority over the assembly
and calculation functions of deal management, and human professionals should retain authority over the
interpretive and decisional functions. The risk of inverting this — delegating judgment to AI and
reserving only review for humans — is not primarily that AI will make wrong decisions. It is that the
quality of investment judgment degrades when professionals stop actively exercising it. The analytical
rigor that experienced credit investors and secondaries practitioners bring to their work is developed
through the practice of analysis, not through the review of AI-generated outputs.