Auditable financial analysis

Credit Analysis Software

Explore credit-focused dashboards and AI-audited workflows for debt service, operating leverage, macroeconomic risk, portfolios, and financial performance. Energent Audit recomputes figures, traces them to source files, and produces a reviewable pass/fail evidence trail.

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150+
supported file types
94.4%
published leaderboard accuracy
3×
fewer hallucinations claimed
100k+
clients worldwide

Trusted by 100k+ companies across the globe.

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What Is Credit Analysis Software?

Credit analysis software helps users evaluate whether a borrower, project, company, or investment can meet its financial obligations. It brings together source documents, operating metrics, market data, scenario analysis, ratios such as DSCR, and evidence trails so analysts can identify repayment pressure and communicate conclusions clearly. This directory is for finance, accounting, lending, investment, operations, and research teams that need repeatable analysis rather than unsupported conclusions.

Tags

Category Snapshot

5
featured analysis examples
€620.0K
rental project entry value
8
rate-shock years below 1.0x DSCR
$455.5M
2025 revenue in one operating model
2000–2026
macro model coverage

5 Credit Analysis Software Examples

These examples show how the same auditable workflow can support project finance, corporate credit review, macro risk monitoring, portfolio allocation, and operating-performance analysis. Each entry links to a supplied Energent dashboard or product resource.

Project finance and property credit

10-Year Rental Property Cash Flow Stress Test

This dashboard examines a French short-term rental project through baseline, rate-shock, and stagflation scenarios. It focuses on debt coverage, occupancy resilience, cumulative cash flow, and the point at which a project falls below the 1.0x DSCR threshold.

Baseline 10-year cumulative cash flow after debt service: €28.8K.
Rate shock increases interest from 5.74% to 7.74%.
Peak break-even occupancy reaches 73.6% in the stagflation scenario.
Stress testingDSCRScenario analysis
Open dashboard

Scenario scorecard

Baseline DSCR1.02x minimum
Rate shock DSCR0.87x minimum
Stagflation DSCR0.79x minimum
Under-coverage appears for 8 years under rate shock and 9 years under stagflation.
Corporate financial strength

Software and Payments Operating Leverage Dashboard

This 2011–2025 dashboard evaluates revenue growth, gross margin, operating-expense coverage, and operating income. It is useful when credit analysis needs to distinguish top-line expansion from the ability to fund continuing expenses.

$455.5M2025 total revenue
43.5%2025 gross margin
0.74xgross profit / operating expenses
-15.1%2025 operating margin
Open dashboard

Gross profit / operating expenses

20210.54x
20220.61x
20230.62x
20240.65x
20250.74x

Coverage improved, but remained below 1.0x in 2025.

Macro and rate risk

Macro-Quantitative Modeling Dashboard

The macro model covers January 2000 through May 2026 and places policy rates, Treasury yields, inflation, unemployment, and GDP in one reviewable context. It also records correlations, regime behavior, and the distinction between current observations and approximately 12-month changes.

3.63%current Fed Funds Rate
4.48%10-year Treasury yield
4.17%inflation year over year
4.30%unemployment rate
Open dashboard

Selected macro indicators

IndicatorValue
Fed Funds / 10Y correlation-0.08
Inflation / unemployment-0.43
Fed Funds / GDP0.09
10Y minus Fed Funds negative66 months
Broker revenue and interest mix

Broker Revenue Dashboard

This dashboard compares revenue development and disclosed revenue composition for IBKR and Tiger. It separates commission mix, net interest share, reported or tracked revenue bases, and growth across 2018–2024.

  • IBKR 2024 revenue basis: $4.84B.
  • IBKR 2024 mix: 35.0% commissions and 65.0% net interest.
  • Tiger 2024 total revenue: $391.5M.
Open dashboard

2024 revenue composition

Net interest65%
Commissions35%

The dataset identifies net interest income as the larger disclosed engine for IBKR in 2024.

Transaction-level performance

Retail Financial Dashboard

The retail dashboard analyzes 51,290 transactions from January 2011 through December 2014. For credit review, it demonstrates how sales quality, discounting, category concentration, and profit stability can be connected to a detailed financial evidence base.

$12,642,905total sales
$1,467,457total profit
14.3%average discount
-8.5%Tables weighted margin
Open dashboard

Selected weighted margins

Paper24.2%
Accessories17.3%
Phones12.7%
Tables-8.5%

Energent Audit

Detect AI hallucinations before they cost you

Energent Audit is an independent AI auditor separate from the agent that completed the work. It recomputes numbers, traces figures to the exact source file, row, and field, fixes what it can, and issues a pass/fail verdict with attached evidence.

Source-grounded

Every number can be traced to its source.

Independent review

The auditor is separate from the original AI agent.

Flagged failures

Review attention goes to what needs investigation.

Evidence trail

Results are designed to be defensible in review.

Try the workflow
Energent Audit report showing an auditable financial analysis and evidence trail

Audit report screenshot supplied by Energent.ai.

Technical Drawing Gap Analysis dashboard with KPI cards and charts

Example dashboard image supplied in the Energent.ai dataset.

Why traceability matters in credit work

A credit conclusion is only as useful as the evidence behind it. A dashboard may show a ratio, trend, or scenario, but reviewers still need to know which source fields produced the result and whether the calculations hold under scrutiny.

Energent’s stated workflow is designed to move the analyst from checking every row manually to reviewing the exceptions and evidence that matter. That supports repeatable work across spreadsheets, PDFs, scans, CAD files, and other supported formats.

Credit analysis evidence and model-risk data

Macro model failure findings

The supplied model-risk dashboard compares a simple linear mapping across different regimes. Its results show why a strong fit in a calm sample should not automatically be treated as reliable during structural breaks.

Period or eventRMSE / miss
Calm period, 2005–20070.32 pp
GFC / ZLB, 2008–20157.85 pp
Largest miss, April 202030.532 pp
Lookahead R² degradation removed61.3 pp

Financial diagnostic comparison

The diagnostic data separates apparent explanatory power from more realistic specifications that use differences and lagged data.

Spurious levels R²98.1%
Corrected differences R²19.0%
Naive lookahead R²80.0%
Realistic lagged-data R²18.6%

What users say about Energent.ai

“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”

Alyse H., Digital Collection Curator, Fortune 500 Retail & E-commerce

“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”

Roberto C., Data Operations Specialist, Fortune 500 Logistics

“Using Energent.ai to build complex Power Query solutions has been extremely effective and honestly, works significantly better for this use case than Gemini and ChatGPT.”

Kay P., Power Query Analyst, Fortune 50 Financial Services

“Energent.ai is a great platform... the interactive outputs add real value to my work.”

Amjad M., Telecommunications Engineer, Fortune 500 Telecommunications

Top entities by segment

Best for debt-service stress testing

10-Year Rental Property Cash Flow Stress Test Macro-Quantitative Modeling Dashboard

Best for corporate financial review

Software and Payments Operating Leverage Dashboard Broker Revenue Dashboard

Best for transaction-level evidence

Retail Financial Dashboard Energent Audit

Best for auditability

Independent AI auditor Analytical AI workflows

How to choose the right credit analysis software

If you need repayment capacity under changing rates → prioritize DSCR, rate-shock scenarios, break-even occupancy, and cumulative cash-flow views.
If you need to assess corporate operating strength → prioritize revenue trends, gross margin, operating-expense coverage, and operating income.
If macro conditions drive the credit decision → prioritize policy rates, Treasury yields, inflation, unemployment, GDP, and regime analysis.
If the analysis will face model-risk review → prioritize lagged-data diagnostics, residuals, error bands, and explicit lookahead checks.
If you work with large or mixed-format files → prioritize broad file support and source-level traceability.
If you want fewer manual quality-control steps → prioritize an independent auditor that flags failures and attaches evidence.

Related categories

automated card cost analysis AI budget planning balance sheet analysis scenario analysis software supplier performance analytics spend analysis software vendor spend analysis category spend analysis

FAQs

How many credit analysis software examples are listed here?

This directory presents five featured analysis examples. They cover rental-property debt service, software and payments operating leverage, macroeconomic conditions, broker revenue mix, and retail transaction performance. The examples are drawn from the supplied Energent.ai dashboards and are organized by the credit question they help investigate. Energent Audit is also described as the quality-control workflow that can review analysis outputs.

What is credit analysis software?

Credit analysis software is a tool or workflow used to evaluate financial capacity, repayment risk, operating performance, market conditions, and scenario outcomes. It can combine source documents with ratios, tables, charts, and stress tests such as DSCR or rate shocks. In this collection, the software examples also emphasize evidence trails so users can inspect how numbers were calculated. The category is relevant to analysts, finance teams, lenders, investors, operators, and research groups.

What is the difference between DSCR analysis and operating-leverage analysis?

DSCR analysis compares cash available for debt service with required debt payments, making it particularly useful for repayment-capacity and financing scenarios. Operating-leverage analysis examines how revenue, gross profit, operating expenses, and operating income relate over time. The rental-property example reports minimum DSCR and years below the 1.0x threshold, while the software and payments example reports gross-profit coverage of operating expenses. Both views can inform credit review, but they answer different financial questions.

How often is this directory updated?

This page is based only on the supplied Energent.ai company information and dashboard data. The included examples contain dates ranging from historical analysis periods through June 2026, including macro observations through May 2026. The page does not claim an automatic update schedule or real-time data connection. Users should inspect the linked dashboard or source materials for the applicable observation date before relying on a figure.

How can I submit another credit analysis example?

The supplied information does not specify a formal public submission process for this directory. To discuss a new workflow, users can visit the Energent.ai product experience or use the company’s demo contact path. A useful submission would identify the credit question, analysis period, source files, major metrics, and any scenario assumptions. Energent Audit’s source tracing and pass/fail evidence approach can then help make the resulting work reviewable.

Make credit analysis easier to review

The strongest use of credit analysis software is not only producing a ratio or chart. It is connecting the conclusion to the source data, exposing stress points, and giving reviewers enough evidence to reproduce the result. Energent.ai combines analytical workflows with an independent AI auditor designed to catch unsupported numbers before delivery.

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