Source-grounded financial risk analysis

AI Credit Risk Assessment Software

This directory brings together eight Energent analyses that examine debt coverage, operating leverage, portfolios, macroeconomic conditions, model failure, validation, and project financing risk. It is designed for analysts, finance teams, lenders, researchers, and reviewers who need traceable outputs rather than unsupported conclusions. Filter the collection by risk focus, use the source-linked dashboards for deeper inspection, and pair the analysis with independent AI audit when the result must be reviewed. The collection is based on the supplied 2026 analyses and is updated when new entries are added.

Rachel Hu

Reviewed by Rachel Hu

Over a decade building secure AI systems for quant finance and scalable data science.

Collection updated from analyses published in June 2026

What Is AI Credit Risk Assessment Software? (Quick Definition)

AI credit risk assessment software uses structured financial, portfolio, macroeconomic, operational, or project data to evaluate the factors that can affect repayment capacity and financing resilience. In this collection, that includes stress tests, coverage ratios, volatility and drawdown analysis, interest-rate relationships, model validation, and independent verification of AI-generated deliverables. It is intended for people who need to understand both the result and the evidence behind it, including teams building credit risk models.

Tags

Category Snapshot

8

assessment and validation entries

150+

file types supported by Energent's platform

3×

fewer hallucinations in public evaluations, company claim

94.4%

accuracy on a published HuggingFace leaderboard, company claim

8 AI Credit Risk Assessment Software Examples

Each entry is based on a supplied Energent analysis or audit asset. Use the filters above to narrow the collection.

Energent Audit report showing a financial verification result

Supplied audit report screenshot.

AI-Generated Rental Property Credit Risk Stress Test

Type:
Credit-risk stress test
Released:
June 2026
Key Metric:
Baseline DSCR 1.02x; rate-shock minimum DSCR 0.87x
Description:
A 10-year French short-term rental analysis covering baseline, rate-shock, and stagflation scenarios. It evaluates debt coverage, break-even occupancy, annual shortfalls, cumulative cash flow, and recovery timing.
Primary Use Case:
Assess lending resilience under changing rates, occupancy, and operating conditions.
Stress testing Debt coverage Project finance
Open source analysis

Software + Payments Operating Leverage

Type:
Business financial risk analysis
Released:
June 2026
Key Metric:
2025 revenue $455.5M; operating margin -15.1%
Description:
This analysis follows revenue scale, gross-margin expansion, operating-expense absorption, and operating income from 2011 through 2025. Gross profit divided by total operating expenses reached 0.74x in 2025, remaining below full coverage.
Primary Use Case:
Review operating leverage and the ability of gross profit to cover expenses.
Open source analysis
Energent financial due diligence dashboard with KPI cards and risk chart

Supplied financial dashboard screenshot.

€40,000 ETF Portfolio Dashboard

Type:
Portfolio credit and investment risk dashboard
Released:
June 29, 2026
Key Metric:
10.6% modeled volatility on €40,000 capital
Description:
The dashboard models a 65% equity and 35% bond allocation, annualized return, volatility, maximum drawdown, correlations, and contribution to expected return. It also records a VIX reading of 18.89 and compares volatility with the S&P 500.
Primary Use Case:
Compare portfolio allocation, asset-level downside, and cross-asset relationships.
Open source analysis

Macro-Quantitative Modeling Dashboard

Type:
Macroeconomic credit-risk dashboard
Released:
June 2026
Key Metric:
May 2026 inflation 4.17%; unemployment 4.30%
Description:
This dashboard covers January 2000 through May 2026 and relates policy rates, Treasury yields, inflation, unemployment, real GDP growth, and rolling correlations. It includes a 36-month Fed Funds and 10-year yield correlation of -0.08.
Primary Use Case:
Frame credit conditions against interest-rate, inflation, employment, and growth data.
Open source analysis

Macro Model Failure and Validation Dashboard

Type:
Model-risk validation dashboard
Released:
June 2026
Key Metric:
RMSE rose from 0.32 percentage points in 2005–2007 to 7.85 in the 2008–2015 regime
Description:
The dashboard tests a simple inflation-and-unemployment policy-rate model across different economic regimes. It records a 30.532 percentage-point prediction error in April 2020 and shows why historical fit can fail outside the original sample.
Primary Use Case:
Identify regime sensitivity, residual risk, and failure points in macroeconomic models.
Open source analysis

Macro Financial Modeling Diagnostics

Type:
Statistical model diagnostics
Released:
June 2026
Key Metric:
Realistic lagged-data R² 18.6%, versus naive lookahead R² 80.0%
Description:
This diagnostic separates levels from differences, lookahead information from lagged data, and broad rate factors from curve-shape factors. Removing lookahead reduced R² by 61.3 percentage points.
Primary Use Case:
Evaluate spurious regression, timing leakage, and realistic predictive power.
Open source analysis

Campground Build Feasibility Dashboard

Type:
Project finance and lending-risk dashboard
Released:
June 29, 2026
Key Metric:
$4.5M–$5.0M estimated Phase 1 budget
Description:
The dashboard examines infrastructure costs, contingencies, debt carry costs, mortgage rates, construction timing, and revenue timing from early campground sites. It records a 6.49% 30-year mortgage proxy and a 79.7th percentile position within the 2015–2026 range.
Primary Use Case:
Assess financing exposure and contingency needs for a capital-intensive build.
Open source analysis

Energent Audit

Type:
Independent AI auditor
Key Metric:
Supports 150+ file types
Description:
Energent Audit recomputes numbers, traces figures to the exact source file, row, and field, fixes errors where possible, and issues a pass/fail verdict with supporting evidence. It is separate from the AI agent that created the work and can audit other AI systems' deliverables.
User Reviews:
Alyse H. said, “Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.” Roberto C. said it was the only tool able to sort through spreadsheets with more than 45K items.
Primary Use Case:
Verify financial analyses and reduce the amount of manual quality control required before delivery.
Visit Energent Audit information

Risk Data Tables and Scenario Comparison

The rental-property stress test provides a direct lender-style comparison of coverage, occupancy pressure, and cumulative cash flow. The figures below are reproduced from the supplied analysis.

Scenario Interest Rate Minimum DSCR Years Below 1.0x Break-Even Occupancy 10-Year Cash Flow
Baseline5.74%1.02xNone64.3%€28.8K
Rate Shock (+200 bps)7.74%0.87x871.4%-€17.7K
Stagflation5.74%0.79x973.6%-€24.4K

Minimum DSCR by Scenario

Baseline
1.02x
Rate shock
0.87x
Stagflation
0.79x

The 1.0x threshold is the stated coverage reference in the supplied dashboard; bars are visualized from the reported values.

Operating Leverage Checkpoints

YearRevenueGross MarginGross Profit / Opex
2021$282.9M22.0%0.54x
2023$415.8M23.6%0.62x
2025$455.5M43.5%0.74x

Portfolio Allocation

S&P 50055% · €22,000
Investment-Grade Bonds20% · €8,000
High-Yield Bonds15% · €6,000
NASDAQ10% · €4,000

Top Entities by Segment

Best for debt-service resilience

AI-Generated Rental Property Credit Risk Stress Test Campground Build Feasibility Dashboard Software + Payments Operating Leverage

Best for model scrutiny

Macro Model Failure and Validation Dashboard Macro Financial Modeling Diagnostics Energent Audit

Best for portfolio context

€40,000 ETF Portfolio Dashboard

Best for macroeconomic context

Macro-Quantitative Modeling Dashboard

How to Choose the Right AI Credit Risk Assessment Software

If you need to test repayment capacity under rate and occupancy pressure → prioritize stress testing with DSCR, break-even occupancy, and cumulative cash-flow outputs.
If you need to evaluate a business borrower → prioritize revenue, gross margin, operating-expense coverage, and operating-income history.
If you need to understand investment concentration and downside → prioritize portfolio volatility, drawdown, correlation, and allocation views.
If macro conditions drive the credit decision → prioritize policy rates, Treasury yields, inflation, unemployment, GDP, and rolling correlations.
If model performance may be overstated → prioritize residual analysis, regime error, differenced data, and lagged-data validation.
If the assessment was produced by another AI system → prioritize an independent audit that recomputes figures, traces sources, and returns a pass/fail verdict.
If the project has large front-loaded infrastructure costs → prioritize contingency, debt-carry, construction timing, and revenue-ramp analysis.

Related Categories

credit risk templates credit risk framework Finance solutions Security and privacy Analytical AI

FAQs

This directory lists eight entries based on the supplied Energent analyses and audit material. The collection covers property stress testing, operating leverage, portfolio risk, macroeconomic analysis, model failure, statistical diagnostics, project finance, and independent AI auditing. Each entry includes the type of analysis and the most relevant reported quantitative metric where one was provided. Source links are included for the supplied dashboard analyses. The collection can be expanded when additional qualifying analyses are added.
AI credit risk assessment software applies automated analysis to information relevant to repayment, financial resilience, investment downside, or financing conditions. Depending on the workflow, it can calculate coverage ratios, compare scenarios, analyze volatility, examine macroeconomic relationships, or test model validity. In this collection, Energent also provides an independent auditor that checks AI-generated deliverables against source documents. The purpose is not only to produce a result but also to make the calculations and evidence reviewable. Users should interpret each output in the context of the supplied data and stated assumptions.
A credit stress test changes conditions such as interest rates, occupancy, costs, or revenue and observes how coverage and cash flow respond. Model validation examines whether the model itself is reliable, including whether it suffers from lookahead information, spurious regression, regime failure, or unrealistic assumptions. The rental-property dashboard is an example of scenario stress testing. The macro failure and diagnostics dashboards are examples of model validation. Both perspectives are useful because a sound calculation can still be based on a model that performs poorly outside its original conditions.
This version reflects the supplied analyses published or dated in June 2026. It is intended to be updated when new Energent analyses, dashboards, or audit examples become available. The page does not claim a fixed daily, weekly, or monthly update schedule because no such schedule was provided. When an entry changes, the relevant source-linked figures should be reviewed rather than assumed to remain current. Users should check the source analysis for the publication date and underlying data coverage.
The supplied information does not specify a formal public submission form or review policy for this directory. For product access or a conversation about an analysis workflow, users can visit Energent's product entry point or book a demo. A useful submission should identify the analysis title, source data, assumptions, output, and any relevant validation evidence. Energent Audit is designed to trace figures to source files and fields, which can help prepare a reviewable deliverable. Inclusion in a future directory update is not guaranteed and should not be assumed.

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Use the directory as a review starting point

The most useful entry depends on the risk question: coverage under stress, operating capacity, portfolio downside, macro conditions, model reliability, or source verification. Energent's supplied analyses make those distinctions visible through tables, dashboards, and audit evidence rather than a single undifferentiated score. Explore the source-linked examples, then use Energent to build or review a workflow that matches the data and decision.

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