Financial data extraction
Statements, tax returns, schedules from any format. FRS canonicalization maps line items to the Canonical Field Library. Multi-year, multi-entity financials normalized.
Use Cases
Credit & Risk Evaluation
MightyBot executes credit risk evaluation end-to-end. Financials extracted,
DSCR calculated, credit memos generated with full evidence trails. Not assisted. Finished.
Why MightyBot
MightyBot executes credit risk evaluation from raw financials to structured credit memo — extracting data, calculating DSCR and risk metrics, evaluating policies, and generating audit-ready memos with evidence pointers tracing every figure to source. Not assisted. Finished.
Automate Credit Risk Evaluation
Every credit memo starts the same way: pull financial data from tax returns, income statements, balance sheets, and cash flow statements across multiple years and entities. Calculate ratios. Apply policies. Assemble the memo. MightyBot executes this in minutes with every number traced to source.
The Problem
Analysts spend the majority of their time on data gathering and spreadsheet construction — not credit judgment. Inconsistency compounds: two analysts calculate DSCR differently based on which adjustments they include. Memo formats vary, making portfolio-level risk reporting unreliable. Regulators examining inconsistent credit files increase scrutiny systematically. Financial statements arrive in different formats. Multi-entity borrowers require consolidation. Manual processes don't scale.
Format variability
Financial statements from different firms in different formats and standards
Multi-entity complexity
Consolidated analysis with intercompany reconciliation
Ratio inconsistency
DSCR and leverage metrics calculated differently by different analysts
Policy interpretation
Credit policies applied with varying rigor across branches
Memo assembly
Hours of manual synthesis that looks different every time
How MightyBot Executes
Risk metric calculation
DSCR, debt-to-equity, current ratio, cash flow coverage with transparent inputs. Every formula documented with evidence pointers to source.
Policy-based evaluation
Your credit policies evaluated deterministically. Passes at one branch, passes at every branch. The engine doesn't interpret — it executes.
Credit memo generation
Consistent, auditable format matching your standards. Every data point includes evidence pointers. Ready for committee. No manual assembly.
Before vs After
Production Metrics
95% Reduction in credit analysis processing time
99%+ Accuracy on financial data extraction
10x Throughput without proportional headcount
1x Deterministic risk metrics across analysts and branches
100% Evidence trail for every data point and calculation
Credit memos in minutes. Every number traced to source.
Request a demoFAQ
Frequently Asked Questions
How does MightyBot calculate DSCR and financial ratios?
Line items are extracted from financials, then adjustments are applied according to your methodology for add-backs, one-time items, and normalization. Every formula and source remains documented.
Can MightyBot handle multiple entities?
Yes. Multiple entities can be processed and normalized within the same framework, including intercompany relationships, guarantor analysis, and global cash flow consolidation.
Does MightyBot replace credit analysts?
No. It finishes extraction, normalization, and calculation so your team can spend more time on actual credit judgment instead of spreadsheet assembly.
How does MightyBot handle different accounting standards?
FRS canonicalization maps line items across GAAP, tax basis, cash basis, and different compilation or review formats into one consistent schema.
Can we customize the credit memo template?
Yes. Section order, disclosures, ratio definitions, and policy presentation can all match your institutional template while preserving evidence pointers.
What if borrower data has inconsistencies across documents?
Discrepancies are flagged automatically with evidence pointers to both conflicting sources so they are visible before committee review rather than discovered later.