Financial Data Extraction
Borrower statements processed regardless of format. FRS normalizes to a consistent schema. Different borrowers, firms, periods. Same structured output.
Use Cases
Covenant Monitoring
MightyBot executes covenant monitoring continuously. DSCR, leverage, liquidity, extracted, calculated, evaluated against thresholds. Breaches caught. Not missed.
Why MightyBot
MightyBot executes covenant monitoring continuously across the entire portfolio, extracting financial metrics from borrower reports, calculating DSCR and leverage ratios, evaluating against loan-specific thresholds, and alerting on breaches in real time. Not periodic spot-checks. Continuous policy-driven surveillance. Every covenant. Every loan. Every reporting period.
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.
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 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
Covenant Calculation
DSCR, leverage, liquidity, occupancy, and custom metrics from normalized data. Evidence pointers link each input to source. Consistent methodology across every loan.
Continuous Threshold Evaluation
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.
Breach Alerting and Compliance Reporting
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.
"We automated what no one else could."
Missed covenants are missed risk. Continuous monitoring catches what periodic spot-checks miss. The integration is the product.
95%
Before vs After
Missed covenants are missed risk.
Continuous monitoring.
Every loan. Every threshold
Request a demo FAQ
Frequently Asked Questions
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.