TLDR
For the narrative version, read Private credit's first real cycle.
Portfolio monitoring breaks during a credit cycle when sequential, spreadsheet-based workflows cannot keep pace with several borrowers or portfolio companies needing attention at once. When multiple names report late, inconsistently, or breach covenants in the same week, hand-reconciled processes built on spreadsheets and email can't keep up. This applies equally to private credit lenders tracking borrowers and private equity firms tracking debt covenants at portfolio companies.
In Lumonic's 2026 State of Private Credit Technology poll, reported AI adoption rose from about 20% to roughly 50%, while reported adoption of loan monitoring and portfolio management tools stayed flat.
In Lumonic's view, the root cause is that much of this AI tooling still runs on fragmented, hand-reconciled spreadsheet inputs, so faster AI sits on top of slow, manual data collection.
Distressed exchanges accounted for 94% of Morningstar DBRS private credit downgrades to default or selective default in the 12 months ending February 2026, so every day spent reconciling data narrows the window to act.
The fix is speed to a trusted, traceable number, achieved by structuring and validating borrower data at the source rather than rebuilding it by hand each cycle. Lumonic is built specifically around this fix, and firms using it have cut portfolio review prep from weeks to days.
Why legacy monitoring was never built for this moment
Sequential monitoring becomes a bottleneck when several borrowers need attention together. An analyst who must reconcile each borrower's financials against the prior period before testing its covenants cannot review the next submission until that work is done.
A credit cycle can put several borrowers under stress in the same reporting window. Late submissions, inconsistent formats, and potential breaches then compete for the same analyst's attention. A sequential review creates a queue, delaying a clear view of exposure across the portfolio.
An unresolved schedule or EBITDA add-back can delay a covenant assessment when a borrower seeks to renegotiate. The lender then has less time to check the calculation against the agreement and supporting documents before discussing its options.
More AI tooling has not fixed this. AI adoption among lenders more than doubled, moving from about 20% to roughly 50% in Lumonic's 2026 State of Private Credit Technology poll, and yet adoption of loan monitoring and portfolio management tools stayed flat. In Lumonic's view the reason is mechanical. Much of this AI tooling still runs on the same fragmented, hand-reconciled spreadsheet inputs that the sequential workflow produced. An AI layer that summarizes or flags a document cannot repair data that was never structured consistently at the source. The bottleneck sits in how borrower data enters the process, not in how a model reads it once it arrives.
The same mismatch shows up in private equity. A PE firm's portfolio companies carry debt with their own covenant packages, and the firm's monitoring team faces the identical sequential bottleneck when several portfolio companies report in the same window. iLEVEL (S&P Global) is a long-established private markets portfolio monitoring platform, and S&P Global has announced Automated Data Ingestion (February 2025), which extracts data from PDFs and spreadsheets with click-to-data-trace to the source document and runs recurring collection workflows per portfolio company, and Covenant Monitoring and Financial Spreading as part of iLEVEL Credit (August 2025). Whatever the platform, the question for a PE monitoring team is whether the reconciliation work actually leaves the analyst's desk or just moves upstream of the tool.
What actually breaks, and why
Four recurring data problems can slow monitoring when several borrowers need review together. When borrowers report late or breach covenants in the same window, these weaknesses surface together instead of one at a time.
Inconsistent reporting formats. Borrowers may submit financials in different templates. Without a common intake process, an analyst must map each submission before comparing figures across the portfolio. Several late reports can create a reconciliation backlog.
EBITDA add-back disputes. A borrower reports adjusted EBITDA that clears its covenant, and the lender's team disagrees with the add-backs used to get there. The dispute breaks the workflow because there is no shared, documented definition of which adjustments the credit agreement permits, so each case turns into a manual argument over source documents. During normal periods you resolve one at a time. During a cycle, several borrowers push aggressive add-backs at once, precisely when the covenant math matters most.
No shared covenant definitions across borrowers. The same covenant, a fixed charge coverage ratio for example, gets calculated differently across deals because each agreement was papered and interpreted separately. The break comes from definitions living in individual credit memos rather than in a common testing framework, so portfolio-wide reporting requires an analyst to reconcile apples to oranges before any trend is visible. You cannot answer "how many borrowers are within 10% of a breach" quickly when the ratios were never computed the same way.
Tribal knowledge loss when an analyst leaves. One person often holds the context for how a borrower's numbers get normalized, which add-backs were previously contested, and where the source data actually sits. When that analyst leaves, the break is immediate, because the knowledge lived in their spreadsheets and memory rather than in a system anyone else can query. A departure mid-cycle can strand an entire sub-portfolio.
What breaks | Why it breaks |
|---|---|
Inconsistent reporting formats | Intake never standardized, so each package is rebuilt by hand |
EBITDA add-back disputes | No documented, shared definition of permitted adjustments |
No shared covenant definitions | Ratios calculated per-deal, not in a common framework |
Tribal knowledge loss | Context lives in one analyst's head, not a queryable system |
Each failure shares a root cause. The data was never structured and validated at the source, so every reporting cycle repeats the same manual reconstruction.
Why the delay is the real risk
Morningstar DBRS reported that distressed exchanges accounted for 94% of its private credit downgrades to default or selective default in the 12 months ending February 2026, a sign that many stressed borrowers do not simply miss a payment and hand you a clear signal. They renegotiate. A distressed exchange trades cheaper debt terms or delayed payments in return for keeping the borrower alive, and the lenders who move first shape those terms. Every week you spend reconciling numbers is a week competing creditors use to lock in their position ahead of yours.
Unresolved covenant calculations can delay a lender's assessment of a stressed borrower. Checking an EBITDA add-back against the agreement and its source documents helps establish whether a reported covenant miss is valid. That assessment informs the lender's response to a waiver or restructuring request.
Simultaneous reporting delays can also obscure aggregate exposure. If several borrowers' figures remain unverified, you cannot confidently summarize which credits are approaching their own covenant thresholds for an investment committee or LP report.
Speed to a trusted, traceable number is the operational advantage. Lenders who reconcile borrower data in days instead of weeks enter restructuring conversations with defensible figures and a stronger negotiating position. Lenders still rebuilding spreadsheets arrive late, with numbers they cannot fully stand behind, into a process where the earliest and best-informed creditor sets the terms.
The fix: speed to a trusted, traceable number
The problem shrinks when borrower data arrives structured and validated at the point it enters your system, rather than getting rebuilt by hand every reporting cycle. A trusted number is one you can produce fast and defend later. Both properties come from how the data is captured, not from how hard an analyst works to reconcile it after the fact.
Two mechanisms do the work. Standardized intake forces every borrower's financials into a common structure at ingestion, so a lender's EBITDA figure means the same thing across the portfolio before anyone runs a covenant test. Source-cell traceability links each computed number back to the exact line in the borrower's original statement, so when an add-back is disputed you can point to where it came from instead of reconstructing the logic from memory.
Together these convert scattered borrower submissions into a defensible number, staged for review shortly after the file lands rather than after the days it takes to chase, reformat, and cross-check. When ten borrowers report in the same week, the number for each one is waiting for review on arrival. Correlated stress stops overwhelming the workflow because the reconciliation work already happened at the source.
Contrast that with rebuilding every figure inside a spreadsheet after collection. The hand-reconciliation approach makes speed and defensibility trade against each other, since a fast number skips the checks and a checked number takes too long. Structuring data at the source removes the trade-off. The number is both quick and traceable because the validation is built into intake rather than bolted on afterward. That is the operational advantage during a cycle, when the window to act closes before a manual process finishes catching up.
What cycle-ready portfolio monitoring infrastructure looks like
Cycle-ready portfolio monitoring infrastructure collects, structures, and validates borrower data at the source, so a firm can produce a defensible number the same week many borrowers report late or breach covenants at once. Five concrete attributes separate infrastructure that holds up under multi-borrower stress from tooling that fails when the whole portfolio moves together.
Data extraction from any borrower format. The system reads financials, compliance certificates, and borrower reports in whatever format each borrower sends, then maps them into a common structure and stages them for review. An analyst reviews figures instead of retyping them into a master spreadsheet.
Automated covenant testing. Covenant definitions live in the platform, not in an analyst's memory or a formula buried in a workbook. Every borrower is tested against its own terms on the same schedule, and a breach is flagged for review when the data lands.
Source-cell traceability. Every reported number links back to the exact cell or document it came from. When a credit committee questions an EBITDA figure during a workout, you show the trail in seconds instead of rebuilding it.
LP-ready reporting. The same validated data that drives internal monitoring feeds investor reporting without a second reconciliation pass. Portfolio-wide exposure is available on demand rather than assembled ahead of each quarter-end.
Cross-borrower standardization. Shared definitions apply across the book, so add-backs, leverage, and compliance metrics mean the same thing for every borrower. Comparing stressed credits against healthy ones takes a query, not a manual normalization exercise.
Lumonic is built to this definition, serving private credit managers, private equity firms, and venture debt funds with active monitoring obligations. Avante Capital Partners cut portfolio review prep from two to three weeks down to two to three days after moving onto this kind of infrastructure, and a middle-market private credit firm eliminated about 20 intern hours a week of covenant testing.
Any firm heading into a real credit cycle can test its current stack against the five criteria above, whether that stack is a dedicated platform such as iLEVEL, Chronograph, 73 Strings or Cobalt, or spreadsheets and email. Ask each vendor to show every criterion working on your own documents from the last reporting cycle.
FAQs
Why hasn't AI adoption changed how lenders run monitoring?
AI adoption among lenders more than doubled, from about 20% to roughly 50% in Lumonic's 2026 State of Private Credit Technology poll, while adoption of loan monitoring and portfolio management tools stayed flat. In Lumonic's view, monitoring workflows still run on the same fragmented, hand-reconciled spreadsheet inputs those tools inherit. An AI layer that reads inconsistent borrower reports still produces inconsistent numbers, so the underlying data problem stayed unsolved. Faster tooling on top of unreliable data just reaches the wrong answer sooner.
What is a "cycle-ready" monitoring stack?
A cycle-ready stack collects and structures borrower data at the source, tests covenants automatically, and traces every reported figure back to its origin cell. Lumonic builds toward this model for private credit, private equity, and venture debt firms with active monitoring obligations. The practical benefit is that you reach a trusted, traceable number quickly when many borrowers report late or inconsistently in the same week.
How does covenant tracking fit into broader portfolio monitoring software?
Covenant tracking is the layer that checks each borrower's reported metrics against negotiated thresholds, flagging breaches as data comes in. In Lumonic, covenant testing runs inside the full monitoring workflow rather than as a standalone tool, so breaches surface against the same structured data that feeds reporting. That integration lets you spot a synchronized wave of breaches across the portfolio while there is still time to act.
Does this apply to private equity firms, or only private credit lenders?
It applies to both. PE portfolio companies carry debt with their own covenant packages, so a PE firm's monitoring team hits the same synchronized-stress problem as a credit lender once several portfolio companies report late or trip a covenant in the same window. Lumonic serves private credit managers, private equity firms, and venture debt funds with the same underlying infrastructure, and PE firms get debt-compliance visibility as part of the same monitoring workflow.
Disclaimer
This article was written by Lumonic, a PitchBook company, and reflects Lumonic's views as of October 2026. Statements about third-party products and companies are based on publicly available information, including the vendors' own websites, press releases and documentation, and on the sources linked in the text. Lumonic has not independently tested third-party products, and their capabilities, pricing and positioning may have changed since this article was last updated. Statements about Lumonic's own product describe the platform at the time of writing, and customer outcomes are taken from the published customer stories linked in the text. Nothing here is a guarantee of results. Third-party names and trademarks belong to their respective owners and are used for identification only. If you represent a company named here and believe a statement is inaccurate, contact contact@lumonic.com and we will review and correct it.
TLDR
For the narrative version, read Private credit's first real cycle.
Portfolio monitoring breaks during a credit cycle when sequential, spreadsheet-based workflows cannot keep pace with several borrowers or portfolio companies needing attention at once. When multiple names report late, inconsistently, or breach covenants in the same week, hand-reconciled processes built on spreadsheets and email can't keep up. This applies equally to private credit lenders tracking borrowers and private equity firms tracking debt covenants at portfolio companies.
In Lumonic's 2026 State of Private Credit Technology poll, reported AI adoption rose from about 20% to roughly 50%, while reported adoption of loan monitoring and portfolio management tools stayed flat.
In Lumonic's view, the root cause is that much of this AI tooling still runs on fragmented, hand-reconciled spreadsheet inputs, so faster AI sits on top of slow, manual data collection.
Distressed exchanges accounted for 94% of Morningstar DBRS private credit downgrades to default or selective default in the 12 months ending February 2026, so every day spent reconciling data narrows the window to act.
The fix is speed to a trusted, traceable number, achieved by structuring and validating borrower data at the source rather than rebuilding it by hand each cycle. Lumonic is built specifically around this fix, and firms using it have cut portfolio review prep from weeks to days.
Why legacy monitoring was never built for this moment
Sequential monitoring becomes a bottleneck when several borrowers need attention together. An analyst who must reconcile each borrower's financials against the prior period before testing its covenants cannot review the next submission until that work is done.
A credit cycle can put several borrowers under stress in the same reporting window. Late submissions, inconsistent formats, and potential breaches then compete for the same analyst's attention. A sequential review creates a queue, delaying a clear view of exposure across the portfolio.
An unresolved schedule or EBITDA add-back can delay a covenant assessment when a borrower seeks to renegotiate. The lender then has less time to check the calculation against the agreement and supporting documents before discussing its options.
More AI tooling has not fixed this. AI adoption among lenders more than doubled, moving from about 20% to roughly 50% in Lumonic's 2026 State of Private Credit Technology poll, and yet adoption of loan monitoring and portfolio management tools stayed flat. In Lumonic's view the reason is mechanical. Much of this AI tooling still runs on the same fragmented, hand-reconciled spreadsheet inputs that the sequential workflow produced. An AI layer that summarizes or flags a document cannot repair data that was never structured consistently at the source. The bottleneck sits in how borrower data enters the process, not in how a model reads it once it arrives.
The same mismatch shows up in private equity. A PE firm's portfolio companies carry debt with their own covenant packages, and the firm's monitoring team faces the identical sequential bottleneck when several portfolio companies report in the same window. iLEVEL (S&P Global) is a long-established private markets portfolio monitoring platform, and S&P Global has announced Automated Data Ingestion (February 2025), which extracts data from PDFs and spreadsheets with click-to-data-trace to the source document and runs recurring collection workflows per portfolio company, and Covenant Monitoring and Financial Spreading as part of iLEVEL Credit (August 2025). Whatever the platform, the question for a PE monitoring team is whether the reconciliation work actually leaves the analyst's desk or just moves upstream of the tool.
What actually breaks, and why
Four recurring data problems can slow monitoring when several borrowers need review together. When borrowers report late or breach covenants in the same window, these weaknesses surface together instead of one at a time.
Inconsistent reporting formats. Borrowers may submit financials in different templates. Without a common intake process, an analyst must map each submission before comparing figures across the portfolio. Several late reports can create a reconciliation backlog.
EBITDA add-back disputes. A borrower reports adjusted EBITDA that clears its covenant, and the lender's team disagrees with the add-backs used to get there. The dispute breaks the workflow because there is no shared, documented definition of which adjustments the credit agreement permits, so each case turns into a manual argument over source documents. During normal periods you resolve one at a time. During a cycle, several borrowers push aggressive add-backs at once, precisely when the covenant math matters most.
No shared covenant definitions across borrowers. The same covenant, a fixed charge coverage ratio for example, gets calculated differently across deals because each agreement was papered and interpreted separately. The break comes from definitions living in individual credit memos rather than in a common testing framework, so portfolio-wide reporting requires an analyst to reconcile apples to oranges before any trend is visible. You cannot answer "how many borrowers are within 10% of a breach" quickly when the ratios were never computed the same way.
Tribal knowledge loss when an analyst leaves. One person often holds the context for how a borrower's numbers get normalized, which add-backs were previously contested, and where the source data actually sits. When that analyst leaves, the break is immediate, because the knowledge lived in their spreadsheets and memory rather than in a system anyone else can query. A departure mid-cycle can strand an entire sub-portfolio.
What breaks | Why it breaks |
|---|---|
Inconsistent reporting formats | Intake never standardized, so each package is rebuilt by hand |
EBITDA add-back disputes | No documented, shared definition of permitted adjustments |
No shared covenant definitions | Ratios calculated per-deal, not in a common framework |
Tribal knowledge loss | Context lives in one analyst's head, not a queryable system |
Each failure shares a root cause. The data was never structured and validated at the source, so every reporting cycle repeats the same manual reconstruction.
Why the delay is the real risk
Morningstar DBRS reported that distressed exchanges accounted for 94% of its private credit downgrades to default or selective default in the 12 months ending February 2026, a sign that many stressed borrowers do not simply miss a payment and hand you a clear signal. They renegotiate. A distressed exchange trades cheaper debt terms or delayed payments in return for keeping the borrower alive, and the lenders who move first shape those terms. Every week you spend reconciling numbers is a week competing creditors use to lock in their position ahead of yours.
Unresolved covenant calculations can delay a lender's assessment of a stressed borrower. Checking an EBITDA add-back against the agreement and its source documents helps establish whether a reported covenant miss is valid. That assessment informs the lender's response to a waiver or restructuring request.
Simultaneous reporting delays can also obscure aggregate exposure. If several borrowers' figures remain unverified, you cannot confidently summarize which credits are approaching their own covenant thresholds for an investment committee or LP report.
Speed to a trusted, traceable number is the operational advantage. Lenders who reconcile borrower data in days instead of weeks enter restructuring conversations with defensible figures and a stronger negotiating position. Lenders still rebuilding spreadsheets arrive late, with numbers they cannot fully stand behind, into a process where the earliest and best-informed creditor sets the terms.
The fix: speed to a trusted, traceable number
The problem shrinks when borrower data arrives structured and validated at the point it enters your system, rather than getting rebuilt by hand every reporting cycle. A trusted number is one you can produce fast and defend later. Both properties come from how the data is captured, not from how hard an analyst works to reconcile it after the fact.
Two mechanisms do the work. Standardized intake forces every borrower's financials into a common structure at ingestion, so a lender's EBITDA figure means the same thing across the portfolio before anyone runs a covenant test. Source-cell traceability links each computed number back to the exact line in the borrower's original statement, so when an add-back is disputed you can point to where it came from instead of reconstructing the logic from memory.
Together these convert scattered borrower submissions into a defensible number, staged for review shortly after the file lands rather than after the days it takes to chase, reformat, and cross-check. When ten borrowers report in the same week, the number for each one is waiting for review on arrival. Correlated stress stops overwhelming the workflow because the reconciliation work already happened at the source.
Contrast that with rebuilding every figure inside a spreadsheet after collection. The hand-reconciliation approach makes speed and defensibility trade against each other, since a fast number skips the checks and a checked number takes too long. Structuring data at the source removes the trade-off. The number is both quick and traceable because the validation is built into intake rather than bolted on afterward. That is the operational advantage during a cycle, when the window to act closes before a manual process finishes catching up.
What cycle-ready portfolio monitoring infrastructure looks like
Cycle-ready portfolio monitoring infrastructure collects, structures, and validates borrower data at the source, so a firm can produce a defensible number the same week many borrowers report late or breach covenants at once. Five concrete attributes separate infrastructure that holds up under multi-borrower stress from tooling that fails when the whole portfolio moves together.
Data extraction from any borrower format. The system reads financials, compliance certificates, and borrower reports in whatever format each borrower sends, then maps them into a common structure and stages them for review. An analyst reviews figures instead of retyping them into a master spreadsheet.
Automated covenant testing. Covenant definitions live in the platform, not in an analyst's memory or a formula buried in a workbook. Every borrower is tested against its own terms on the same schedule, and a breach is flagged for review when the data lands.
Source-cell traceability. Every reported number links back to the exact cell or document it came from. When a credit committee questions an EBITDA figure during a workout, you show the trail in seconds instead of rebuilding it.
LP-ready reporting. The same validated data that drives internal monitoring feeds investor reporting without a second reconciliation pass. Portfolio-wide exposure is available on demand rather than assembled ahead of each quarter-end.
Cross-borrower standardization. Shared definitions apply across the book, so add-backs, leverage, and compliance metrics mean the same thing for every borrower. Comparing stressed credits against healthy ones takes a query, not a manual normalization exercise.
Lumonic is built to this definition, serving private credit managers, private equity firms, and venture debt funds with active monitoring obligations. Avante Capital Partners cut portfolio review prep from two to three weeks down to two to three days after moving onto this kind of infrastructure, and a middle-market private credit firm eliminated about 20 intern hours a week of covenant testing.
Any firm heading into a real credit cycle can test its current stack against the five criteria above, whether that stack is a dedicated platform such as iLEVEL, Chronograph, 73 Strings or Cobalt, or spreadsheets and email. Ask each vendor to show every criterion working on your own documents from the last reporting cycle.
FAQs
Why hasn't AI adoption changed how lenders run monitoring?
AI adoption among lenders more than doubled, from about 20% to roughly 50% in Lumonic's 2026 State of Private Credit Technology poll, while adoption of loan monitoring and portfolio management tools stayed flat. In Lumonic's view, monitoring workflows still run on the same fragmented, hand-reconciled spreadsheet inputs those tools inherit. An AI layer that reads inconsistent borrower reports still produces inconsistent numbers, so the underlying data problem stayed unsolved. Faster tooling on top of unreliable data just reaches the wrong answer sooner.
What is a "cycle-ready" monitoring stack?
A cycle-ready stack collects and structures borrower data at the source, tests covenants automatically, and traces every reported figure back to its origin cell. Lumonic builds toward this model for private credit, private equity, and venture debt firms with active monitoring obligations. The practical benefit is that you reach a trusted, traceable number quickly when many borrowers report late or inconsistently in the same week.
How does covenant tracking fit into broader portfolio monitoring software?
Covenant tracking is the layer that checks each borrower's reported metrics against negotiated thresholds, flagging breaches as data comes in. In Lumonic, covenant testing runs inside the full monitoring workflow rather than as a standalone tool, so breaches surface against the same structured data that feeds reporting. That integration lets you spot a synchronized wave of breaches across the portfolio while there is still time to act.
Does this apply to private equity firms, or only private credit lenders?
It applies to both. PE portfolio companies carry debt with their own covenant packages, so a PE firm's monitoring team hits the same synchronized-stress problem as a credit lender once several portfolio companies report late or trip a covenant in the same window. Lumonic serves private credit managers, private equity firms, and venture debt funds with the same underlying infrastructure, and PE firms get debt-compliance visibility as part of the same monitoring workflow.
Disclaimer
This article was written by Lumonic, a PitchBook company, and reflects Lumonic's views as of October 2026. Statements about third-party products and companies are based on publicly available information, including the vendors' own websites, press releases and documentation, and on the sources linked in the text. Lumonic has not independently tested third-party products, and their capabilities, pricing and positioning may have changed since this article was last updated. Statements about Lumonic's own product describe the platform at the time of writing, and customer outcomes are taken from the published customer stories linked in the text. Nothing here is a guarantee of results. Third-party names and trademarks belong to their respective owners and are used for identification only. If you represent a company named here and believe a statement is inaccurate, contact contact@lumonic.com and we will review and correct it.
