Debt capital markets teams often need to work through hundreds of pages of documentation while keeping issuer performance, capital structure, and market precedent in view.
AI due diligence supports that process by helping professionals find relevant terms, connect information across sources, compare transactions, and surface questions that require closer review.
Professional judgment remains central to how those findings are interpreted and applied. AI reduces the manual work involved in gathering and organizing evidence, while analysts, lawyers, and other specialists determine what that evidence means for a transaction or credit.
What is AI due diligence?
AI due diligence is a process of using artificial intelligence to help professionals find, organize, compare, and evaluate information during a diligence process. In debt capital markets, that can include offering memoranda, credit agreements, covenants, financial disclosures, capital structures, transaction terms, and comparable deals.
Unlike generic document review, credit-focused AI can connect findings across documents and market context. Teams can identify relevant provisions, changes, discrepancies, and areas for further investigation. AI can also link covenant language, issuer performance, capital structure, and precedent rather than treating each source in isolation.
AI supports the research process, but specialists still determine materiality and interpretation. Professional review remains necessary for legal conclusions, credit judgments, conflicting documents, amendments, and final investment or transaction decisions.
What credit documents can AI help review?
Debt-market diligence draws on different sources depending on the issuer, instrument, and stage of the transaction. Teams need to connect findings across sources to build a complete view of the credit.
| Document or data source | What teams may review |
|---|---|
| Credit agreements | Debt capacity, security, maturity provisions, definitions, baskets, and lender protections |
| Offering memoranda | Transaction terms, risk factors, capital structure, financial disclosure, and covenant packages |
| Covenant packages | Restricted payments, investments, debt incurrence, liens, exceptions, and other creditor protections |
| Amendments and waivers | Changes to existing rights, restrictions, definitions, or financial terms |
| Financial statements | Revenue, earnings, cash flow, leverage, liquidity, and changes in operating performance |
| Capital structures | Debt instruments, seniority, security, maturity dates, leverage, and liquidity |
| Earnings and company disclosures | Management commentary, guidance, operating developments, and emerging risks |
| Comparable transactions | Market precedent, covenant terms, structure, pricing, and documentation differences |
Tracing findings across sources shows how developments in one area can affect the broader credit. A change in an issuer’s leverage, for example, may affect how a team evaluates debt capacity or refinancing options under the applicable documentation.
That cross-source review can span offering memoranda, credit agreements, filings, covenant information, financials, and related credit data. In 9fin, those sources can be searched together, while Covenant Explorer supports comparisons across documents and precedent transactions.
How due diligence automation supports the deal process
Due diligence automation can reduce the manual retrieval, comparison, and cross-referencing required to assess a credit. A structured workflow moves teams from document collection through source verification while keeping final legal, credit, and investment judgments with the relevant specialists.
1. Collect and organize relevant sources
The process begins by bringing together agreements, offering documents, amendments, financial disclosures, earnings materials, and other sources relevant to the credit. Centralizing those materials gives AI-assisted workflows a clearer information set to search while reducing the need to move between disconnected documents and systems.
2. Search for relevant terms and provisions
Teams can then search across documents for clauses, definitions, baskets, maturity provisions, security terms, and other information tied to the diligence question. In legal diligence workflows, this can include reviewing offering memoranda and credit agreements side by side and tracing findings back to the relevant source language.
3. Compare findings across documents and precedent
Once relevant provisions are identified, teams can compare them with prior document versions and similar transactions. Current loan covenant data can add market context around areas such as debt capacity, portability, security, and restricted-payment flexibility.
4. Add issuer and capital structure context
Document findings can then be reviewed alongside the issuer’s financial performance, liquidity, leverage, maturities, and broader capital structure. Reviewing contractual terms alongside current credit data shows how individual provisions interact with the borrower’s existing obligations and financial position.
5. Verify material findings against original sources
Before relying on a material finding, teams should return to the underlying agreement, filing, transcript, or data point. Source-linked AI responses let reviewers confirm the language, surrounding definitions, exceptions, and amendments that shape how the finding should be interpreted.
6. Escalate open questions for specialist review
The final step is to record conflicting language, missing information, unusual provisions, and unresolved credit questions. Those issues can then be escalated to lawyers, analysts, or investment professionals who can determine materiality, interpret the documentation, and decide whether additional diligence is required.
How AI credit analysis helps teams identify risk
Document review provides only part of the diligence picture. Credit analysis also considers how the issuer’s operating performance, obligations, capital structure, and documentation interact.
AI can surface connections between those signals and identify areas for deeper investigation. The resulting evidence may point to greater covenant flexibility, upcoming maturities, changes in leverage, structural complexity, or documentation that differs from relevant precedent.
1. Review covenants and documentation risk
Covenants define the contractual flexibility and restrictions available to an issuer. Reviewing baskets, exceptions, definitions, blockers, and amendments can reveal how much flexibility exists for additional debt, asset transfers, restricted payments, or other transactions.
Changes between document versions can also alter that analysis. Redlining and precedent comparison can help teams identify where protections have weakened, strengthened, or been drafted differently.
2. Connect issuer performance with debt obligations
Financial results provide context for the contractual obligations identified in the documents. Teams can review revenue, EBITDA, cash generation, liquidity, leverage, interest expense, and other issuer metrics alongside outstanding debt and covenant terms.
Reviewing financials, capital structures, covenant information, documents, and company research in the same credit workflow keeps each finding tied to the broader issuer context. AI can surface relationships between those data points, while analysts assess what they imply for the borrower’s credit profile.
3. Identify refinancing and maturity questions
Assessing refinancing risk requires more than the maturity date. Teams also consider the amount coming due, available liquidity, operating performance, existing leverage, market conditions, and where refinancing would sit within the capital structure.
Combining maturity information with the issuer’s financial and market context can identify credits that warrant closer review before a financing need becomes immediate.
What should deal teams verify before using AI findings?
AI findings should remain traceable to the source material and open to further review. Before using a material finding, deal teams should verify:
- Source: Identify the filing, agreement, transcript, dataset, or other material supporting the finding.
- Date: Confirm the information reflects the period or transaction being analyzed.
- Document coverage: Check whether all relevant agreements, amendments, and disclosures were included.
- Full language: Review the surrounding provision rather than relying on an extracted phrase.
- Definitions and exceptions: Trace defined terms, carve-outs, baskets, and cross-references that could change the interpretation.
- Document hierarchy: Determine whether a later amendment or controlling document supersedes earlier language.
- Current status: Confirm that financial, capital structure, and market information remains current.
- Specialist review: Escalate questions requiring legal interpretation, credit judgment, or another form of professional analysis.
Every material conclusion should support source traceability, giving reviewers a clear path from each AI-generated answer finding to the underlying evidence.
Why underlying debt-market data matters
The value of AI due diligence depends on the information available to the system. Reviewing one document may explain a transaction’s terms, but diligence often requires comparing them with the issuer’s existing debt, comparable deals, and current market precedent.
But market precedent can shift quickly. 9fin’s H1 2026 US Covenant Trends Report found margin ratchets in 40% of sponsored leveraged-loan deals, up from 29% in H2 2025, while first-lien ratio debt headroom tightened from 0.46x to 0.28x EBITDA. Those shifts illustrate why a historical precedent set may produce a different view from current market data.
Debt-focused AI can connect documents with capital structures, financials, covenants, transaction history, and comparable issuers while keeping findings traceable to their supporting sources.
9fin applies that source-linked debt-market context across its AI tools, connecting documentation, capital structure, and precedent within the same research workflow. The Sealed Air take-private walkthrough shows how analysts can move from reviewing maturities and debt capacity to examining change-of-control provisions, a new offering memorandum, and comparable transactions.
Complete debt-market due diligence faster with 9fin
Effective AI-assisted due diligence combines speed with current credit context, connected documentation, source traceability, and specialist judgment. Those elements help deal teams move from an initial finding to a fuller understanding of the issuer, its obligations, and the market around the transaction.
9fin brings those inputs into one debt-market workflow. Its AI-assisted research connects documents with financials, covenants, capital structures, precedent, news, and analysis. Teams get the context to test findings and investigate open questions more efficiently. Source-backed outputs also support the verification process by linking research back to the underlying evidence.
