AI-audited financial analysis

Credit Risk Assessment Software

This directory brings together seven credit-risk assessment software capabilities and analytical dashboards covering independent AI audits, debt-service stress testing, macroeconomic indicators, model validation, and portfolio risk. It is designed for lenders, finance teams, analysts, operations groups, and anyone who needs a traceable answer rather than an unsupported number. Browse the available tags, compare the documented metrics, and open the linked dashboards for deeper review. The strongest way to use this collection is to pair scenario analysis with an evidence trail that shows how every figure was checked.

documented entities

150+

supported file types

2026

current data period

What Is Credit Risk Assessment Software? (Quick Definition)

Credit risk assessment software helps evaluate whether a borrower, project, portfolio, or financial model can withstand debt obligations and changing economic conditions. It can combine source documents, operating assumptions, scenario analysis, macroeconomic data, model diagnostics, and audit evidence. This category is intended for teams that need to identify under-coverage, stress-test assumptions, validate predictive relationships, and support defensible credit decisions.

Category Snapshot

listed software views and audit capabilities

150+

file types supported by Energent

€28.8K

baseline 10-year cumulative cash flow

30.532 pp

largest post-2020 model error

10.6%

modeled ETF portfolio volatility

7 Credit Risk Assessment Software Resources

Audit capability

Energent Audit

Type:: Independent AI auditor
Key Metric:: 3× fewer hallucinations in public evaluations, company claim
Description:: Energent Audit checks work produced by another AI system, recomputes numbers, traces them to the source file, row, field, and reference, and issues a pass/fail verdict. It is designed to reduce manual quality-control work by directing attention toward flagged rows and attaching evidence to the result.
User Reviews:: “Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.” — Alyse H., Digital Collection Curator. “The interactive outputs add real value to my work.” — Amjad M., Telecommunications Engineer.
Primary Use Case:: Independent verification of AI-generated credit-risk analyses and deliverables
Website:: energent.ai

AI auditReproducibilityEvidence trail

Project dashboard

Project Cash Flow Dashboard

Type:: Lender-style rental property stress test
Key Metric:: Rate-shock minimum DSCR: 0.87x
Description:: The dashboard assesses a French short-term rental project with a €620.0K Year 1 entry value. It compares baseline, Rate Shock (+200 bps), and Stagflation scenarios through DSCR, break-even occupancy, annual debt service, and cumulative cash flow.
Primary Use Case:: Debt coverage, occupancy resilience, interest-rate shocks, and cash-flow pressure
Website:: Open dashboard

Stress testingDSCRCash flow

Economic dashboard

Economic Indicators Dashboard

Type:: Macroeconomic indicator analysis
Key Metric:: Federal funds rate / unemployment correlation: -0.44
Description:: This dashboard presents credit-relevant relationships between the federal funds rate, unemployment, and CPI. It records peak unemployment of 14.80% in April 2020, a peak federal funds rate of 5.33% in August 2023, and a latest CPI year-over-year change of 4.17% in May 2026.
Primary Use Case:: Macro context for borrower, portfolio, and credit-cycle analysis
Website:: Open dashboard

Macro dataInflationUnemployment

Quantitative model

Macro-Quantitative Modeling Dashboard

Type:: Macro-financial modeling dashboard
Founded / Released:: Coverage from January 2000 to May 2026
Key Metric:: May 2026 Fed Funds Rate: 3.63%
Description:: The dashboard compares policy rates, 10-year Treasury yields, inflation, unemployment, and real GDP. It includes rolling correlations, a relationship heatmap, rate paths, and an observation that the 10-year Treasury minus Fed Funds spread was negative for 66 months.
Primary Use Case:: Macro scenario construction and quantitative credit-risk context
Website:: Open dashboard

Macro dataCorrelationModeling

Failure analysis

Macro Model Failure Dashboard

Type:: Model-risk diagnostics dashboard
Key Metric:: Largest post-2020 prediction error: 30.532 percentage points in April 2020
Description:: This resource compares model behavior across calm and disrupted regimes. Naive model RMSE was 0.32 percentage points during 2005–2007 and 7.85 percentage points during the 2008–2015 zero-lower-bound regime, showing how coefficients can fail when economic conditions move outside the original sample.
Primary Use Case:: Detecting regime sensitivity, residual error, and unstable credit-model assumptions
Website:: Open dashboard

Model riskResidualsRegimes

Validation diagnostics

Macro Financial Modeling Diagnostics

Type:: Regression and data-leakage diagnostics
Key Metric:: R² degradation after removing lookahead: 61.3 percentage points
Description:: The dashboard contrasts spurious levels-based regression with differenced and realistic lagged-data specifications. It reports a 98.1% levels R², 19.0% corrected differences R², 80.0% naive lookahead R², and 18.6% realistic lagged-data R².
Primary Use Case:: Testing predictive validity, lookahead bias, common trends, and factor relationships
Website:: Open dashboard

Model riskLookahead biasValidation

Portfolio dashboard

€40,000 ETF Portfolio Dashboard

Type:: ETF portfolio-risk analysis
Key Metric:: Modeled annual return: 15.3%
Description:: The dashboard models €40,000 across a 65% equity and 35% bond allocation. It provides annualized return, volatility, maximum drawdown, correlation, VIX context, final allocation, and expected-return contribution views.
Primary Use Case:: Portfolio concentration, volatility, drawdown, and cross-asset risk review
Website:: Open dashboard

Portfolio riskVolatilityDrawdown

Credit Risk Data and Scenario Comparison

The project cash flow dashboard provides the clearest side-by-side credit-risk comparison in this collection. Its figures show how a seemingly resilient baseline can change under rate and operating stress, while the macro dashboards add broader context for interpreting those scenarios.

Scenario scorecard

Scenario	Interest rate	Minimum DSCR	Years below 1.0x	Peak break-even occupancy	10Y cumulative cash flow
Baseline	5.74%	1.02x	None	64.3%	€28.8K
Rate Shock (+200 bps)	7.74%	0.87x	8 years	71.4%	-€17.7K
Stagflation	5.74%	0.79x	9 years	73.6%	-€24.4K

Minimum DSCR by scenario

Baseline1.02x

Rate Shock0.87x

Stagflation0.79x

Relative scale uses the baseline DSCR as the 100% reference. The 1.0x threshold is the documented debt-coverage reference.

Peak break-even occupancy

Baseline64.3%

Rate Shock71.4%

Stagflation73.6%

Relative scale uses the highest documented break-even occupancy as the 100% reference.

Macro indicators supporting credit-risk review

3.63%

Fed Funds Rate, May 2026

4.48%

10-Year Treasury Yield, May 2026

4.17%

Inflation year over year, May 2026

4.30%

Unemployment Rate, May 2026

Audit evidence

A live audit, from source to verdict

Energent Audit is designed as a second agent separate from the system that produced the original work. It recomputes figures, traces them back to source documents, identifies errors that can be fixed, and produces a pass/fail result with supporting evidence.

For credit-risk teams, that distinction matters: the review can focus on flagged rows and assumptions instead of treating every generated result as equally reliable. The evidence trail also supports review and compliance meetings.

Energent Audit report showing a pass or fail result with evidence

Why the numbers hold

The supplied audit video describes a workflow that retraces each figure to its source, verifies how it was built, and returns a report that can be reviewed rather than a black-box answer. This is especially relevant when credit conclusions depend on spreadsheets, PDFs, scans, or other complex documents.

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

How to Choose the Right Credit Risk Assessment Software

If you need to verify AI-generated credit work → prioritize an independent auditor that recomputes numbers and traces them to source fields.

If you need to assess debt repayment resilience → prioritize DSCR, debt-service, occupancy, and cumulative cash-flow scenarios.

If your credit view depends on economic cycles → prioritize macro indicators, rate paths, inflation, unemployment, and rolling correlations.

If you are concerned about model instability → prioritize regime error bars, residual analysis, and out-of-sample diagnostics.

If predictive results look unusually strong → prioritize checks for spurious regression, lookahead information, and realistic lagged data.

If you are reviewing investment or collateral exposure → prioritize volatility, drawdown, correlation, allocation, and return-contribution views.

Related Categories

credit risk frameworks bank credit analysis AI risk management supplier risk analytics credit delinquency monitoring spend analysis

FAQs

How many credit risk assessment software resources are listed?

This directory lists seven documented resources and capabilities. They include Energent Audit, a project cash-flow stress test, economic indicators, macro-quantitative modeling, macro model failure analysis, macro financial diagnostics, and an ETF portfolio dashboard. The collection is organized around the supplied data rather than a broad, unsupported vendor count. Each entry includes its documented type, quantitative metric where available, primary use case, and source link.

What is the most important metric in the project cash-flow assessment?

The project dashboard centers on Debt Service Coverage Ratio, or DSCR, alongside break-even occupancy and cumulative cash flow. In the baseline scenario, DSCR remains above 1.0x and 10-year cumulative cash flow is €28.8K. Under the Rate Shock scenario, minimum DSCR falls to 0.87x and remains below 1.0x for eight years. Under Stagflation, minimum DSCR falls to 0.79x and the peak break-even occupancy reaches 73.6%.

What is the difference between credit risk assessment and model-risk diagnostics?

Credit risk assessment focuses on the borrower, project, portfolio, or exposure and asks whether obligations can be met under stated conditions. Model-risk diagnostics focus on whether the analytical model itself is reliable, including its errors, assumptions, regime sensitivity, and use of information. The supplied dashboards show why the distinction matters: a naive model had an RMSE of 0.32 percentage points in a calm period but 7.85 percentage points during a later regime. A complete review can therefore examine both the credit outcome and the process used to produce it.

How often is this credit risk assessment software directory updated?

The supplied collection includes data through May 2026 and is presented as a current 2026 directory. No separate recurring update interval is specified in the source information. The dashboards themselves identify their coverage periods, such as January 2000 to May 2026 for the macro-quantitative model. When reviewing a result, use the observation date shown by the relevant dashboard rather than assuming every metric has the same date.

How can a team submit another resource or use case?

The supplied information does not define a formal submission process or submission form. Teams can begin by documenting the resource name, type, source URL, quantitative metrics, primary use case, and any authentic user review that can be verified. A useful submission should also explain whether it covers stress testing, macro data, model validation, portfolio risk, or independent audit. Keeping the evidence trail and source context attached makes a future directory entry easier to review.

Make Credit Analysis Easier to Defend

The most useful resource in this collection is the one that matches your decision risk: use scenario dashboards for coverage and resilience, diagnostics for model reliability, and Energent Audit when generated work needs independent verification. Explore the documented dashboards, inspect the evidence, and use the source-linked outputs in your review process.

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