Use Cases
Covenant Monitoring Automation
What is covenant monitoring automation?
Covenant monitoring automation uses policy-driven AI agents to track loan covenant compliance continuously across the portfolio: extracting financial metrics from borrower reports, calculating DSCR and leverage ratios, evaluating them against loan-specific thresholds, and alerting on breaches in real time. Every calculation keeps an evidence trail examiners can follow.
The Problem
Financial covenants are the early warning system, DSCR minimums, leverage caps, liquidity requirements. They flag deteriorating credit before losses materialize. Manual monitoring defeats the purpose of policy enforcement.
Per loan: 30-60 minutes. Across hundreds of loans: thousands of analyst hours per year. Statements sit in inboxes for weeks. A breach two quarters ago isn't flagged until the next credit review. Examiners treat missed monitoring as systemic weakness.
Volume at scale
Thousands of hours per year across the portfolio
Delayed detection
Breaches discovered quarters late
Format variability
Every borrower submits differently
Deadline tracking
Overdue submissions slip through manually
Inconsistent methodology
Analysts calculate metrics differently
How MightyBot Executes
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Ingest
Borrower statements processed regardless of format.
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Extract
FRS normalizes to a consistent schema. Different borrowers, firms, periods. Same structured output.
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Calculate
DSCR, leverage, liquidity, occupancy, and custom metrics from normalized data. Evidence pointers link each input to source. Consistent methodology across every loan.
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Evaluate
Each loan's covenants encoded as policies. Breaches, cure periods, and waiver conditions tracked. Evaluation runs as soon as data arrives. Not when an analyst gets to it.
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Alert
Breach detected: alert with specific covenant, calculated value, required threshold, and evidence pointers. Portfolio dashboards show status across all loans. Reporting deadline tracking flags overdue submissions.
Missed covenants are missed risk. Continuous monitoring catches what periodic spot-checks miss. Bidirectional by default.
Before vs After
Missed covenants are missed risk.
Continuous monitoring.
Every loan. Every threshold
Use-case map
How Covenant Monitoring Automation works in MightyBot
MightyBot automates covenant monitoring by extracting borrower financials, calculating covenant metrics, evaluating loan-specific thresholds, and alerting on breaches in real time.
| Inputs | Borrower financial statements, covenant schedules, credit agreements, reporting calendars, waivers, amendments, and portfolio data. |
|---|---|
| Execution | Extracts borrower data, normalizes financial fields, calculates DSCR, leverage, liquidity, and custom ratios, then evaluates loan-specific policies. |
| Outputs | Breach alerts, overdue reporting alerts, covenant status dashboards, trend analysis, and credit review packages. |
| Audit trail | Every alert links to covenant terms, calculated values, source documents, thresholds, and policy versions. |
| Best for | Banks and lenders managing large portfolios where periodic spot checks miss late filings and early deterioration. |
FAQ
Frequently Asked Questions
What is covenant monitoring automation?
Covenant monitoring automation is the continuous, software-driven tracking of the financial and reporting covenants in loan agreements. Instead of quarterly spreadsheet reviews, AI agents extract the required metrics from borrower financials as they arrive and evaluate them against each loan's thresholds.
How do AI agents monitor loan covenant compliance?
Agents read incoming borrower reports, extract the covenant inputs (DSCR, leverage, liquidity, reporting deadlines), calculate each ratio, and compare results to the covenant terms for that specific loan. Breaches and trends toward breach are flagged with the source documents attached.
Can AI detect a covenant breach before the reporting deadline?
Yes. Because monitoring runs when documents arrive rather than on a review calendar, deteriorating ratios surface as soon as the underlying financials do, which is typically weeks before a scheduled quarterly review would catch them.
How does MightyBot handle different covenant structures?
Each loan's covenants are encoded as individual policies - trailing twelve-month DSCR, quarterly leverage, custom metrics like occupancy. Loan-specific and enforced per credit agreement terms. Your covenants. Your thresholds. Executed precisely.
Can MightyBot track reporting deadlines?
Reporting schedules are encoded as policies. Submission status is tracked across the portfolio, with alerts when deadlines approach and escalations when they pass.
What if a borrower's statement format changes?
FRS canonicalization handles it. New accounting firm, different fiscal year, different format - extracted and normalized to the same schema without manual re-mapping.
Does MightyBot support trend analysis?
Financial data is normalized consistently across periods. Period-over-period trends are calculated automatically so deteriorating metrics can be caught before they breach thresholds.
How does covenant monitoring integrate with credit review?
Results flow directly into credit review workflows with full context - covenant, calculation, source data, and evidence - so the analyst starts with the answer.
Can MightyBot handle waivers and amendments?
Updated terms are reflected in policy configuration, and the history of waivers and amendments is tracked alongside borrower performance as a complete compliance record.