Rewriting Credit Analysis in Private Markets with Purpose-Built AI
Private credit has long been driven by relationships, experience, and deep domain knowledge. Investment teams rely on judgment honed over years—balancing qualitative nuance with quantitative rigor. But in a landscape increasingly shaped by scale, speed, and complexity, the old playbook is no longer enough.
At Claira, we are building richer credit analysis with AI. Not by replacing human insight, but by synthesizing and amplifying it—surfacing patterns, risks, and opportunities buried in the growing volume of unstructured data firms accumulate with every deal.
The question is no longer if AI will impact private credit workflows. It’s how soon firms will adapt—and which ones will lead the transformation. Over the next several posts, we will dig deeper into the impact AI is having in the credit market. In this blog, let’s look at the issues and how and where AI fits in.
The Challenge: Credit Teams Are Drowning in Data, Not Insights
Modern private credit firms face a paradox: they have more information than ever, yet critical insights can remain buried or even worse, other firms are finding them quicker.
Investment memos, data rooms, financial models, legal agreements, borrower communications, external research, portfolio reviews—these sources hold a wealth of intelligence. But they’re often spread across disconnected systems, buried in untold numbers of PDFs, or siloed in individual inboxes. As firms scale, institutional memory becomes harder to access, and knowledge gaps emerge even within seasoned teams.
This fragmented data environment creates several risks:
Fragmented underwriting discipline across deal teams
Redundant diligence on repeat borrowers or structures
Inconsistent covenant packages for comparable credits
Delays in investment committee prep due to manual information retrieval
Loss of institutional memory due to team turnover or siloed workflows
AI has the potential to reverse this trend—by unlocking institutional knowledge at scale.
The Shift: AI Brings Structure to the Unstructured
The most transformative impact of AI in private credit isn’t automation—it’s understanding.
Our AI technologies are capable of parsing vast amounts of unstructured deal data. They can extract terms, identify borrower patterns, flag anomalies, and even summarize relevant sections across thousands of documents. AI then creates a structured, human-ready layer of intelligence across the firm’s history of deals, borrowers, and investment outcomes.
Imagine an analyst preparing for a new borrower meeting and being able to:
Immediately synthesize data from disparate sources, highlighting key points and issues
Collaborate with your deal team within a centralized dashboard that houses all your data and communications
Instantly reference every past deal with a similar term structure
Generate benchmarking data from prior investments and memos
This shift turns AI into a force multiplier—not a replacement for analysts’ knowledge and intuition, but a tool delivering deeper context and historical grounding.
Why It Matters Now: Speed and Precision Are the New Edge
The pace of private credit dealmaking is accelerating - the industry is on pace to grow to $2.6 trillion by 2029. LPs expect not just returns, but repeatable processes, risk frameworks, and defensible decision trails. AI supports this shift by enabling:
Faster deal evaluation without sacrificing depth
Consistency in underwriting across decentralized teams
Improved risk management through pattern recognition
Enhanced collaboration across origination, credit, and ops
Perhaps most importantly, AI platforms help firms retain and operationalize institutional knowledge—especially as teams grow, turnover increases, or new strategies emerge.
Looking Ahead: AI as a Strategic Asset
We are still early in the adoption curve. But the trajectory is clear: credit teams that treat their data as an asset—and leverage AI to unlock it—will gain a material edge in sourcing, diligence, and execution.
The future of credit analysis is not about replacing judgment with automation. It’s about equipping credit professionals with faster access to the firm’s collective knowledge—so they can make better decisions with confidence and precision.
In future posts, we’ll explore practical ways credit teams can adopt this kind of AI into their workflows, the types of data AI models learn from, and how firms are building competitive advantages with internal deal intelligence.
Because in a market where data is increasingly commoditized, knowledge becomes your true differentiator.
