Verify credit risk outputs, monitor delinquency signals, and trace every important number back to its source without becoming your AI system’s quality-control layer.
Trusted by 100k+ companies across the globe.
AI for credit risk and delinquency monitoring uses automated analysis to examine rates, balances, financial statements, receivables, reserves, cash flow, and related source documents for signals that matter to credit decisions. Energent Audit adds an independent verification layer: it recomputes numbers, traces them to the exact source file, row, and field, checks the deliverable, and returns a pass/fail verdict with evidence. This makes it useful for analysts, finance and accounting teams, operations and procurement groups, research teams, and enterprise workflows that need reviewable results rather than an unverified AI answer. For teams building a broader control environment, credit risk audit workflows can make recurring checks more consistent.
The provided dashboards show how an analyst can connect rate repricing, delinquency, financial statement red flags, valuation scenarios, and counterparty ratios into a documented monitoring process.
Complex documents, scans, spreadsheets, and other files can be reviewed together, with outputs designed to show how conclusions were constructed. This is particularly relevant when a credit file contains mixed formats or requires more than ordinary text extraction.
A monitored workflow can surface receivables growth that outpaces revenue, allowance coverage compression, accrued-liability swings, and cash-flow divergence. The resulting dashboard gives a reviewer a focused list of signals to re-check rather than requiring a manual scan of every period.
The vendor-spend audit example illustrates the practical value of a visible verdict. Instead of hiding uncertainty in a narrative, an audit can identify a failure and place the supporting notes and evidence next to the result.
Recurring jobs can be saved as named workflows or skills. Corrections become persistent audit rules, helping teams repeat a credit or financial-monitoring process without rebuilding the same review instructions each time.
The workflow is designed to narrow human attention to exceptions while preserving the evidence needed for a review.
Trace every important number to its source file, row, field, and reference.
Recompute figures and assertions before they reach a stakeholder.
Focus review time on flagged rows instead of checking every row manually.
Deliver Excel workbooks, Word documents, PowerPoint decks, annotated PDFs, HTML dashboards, and other supported outputs.
Reuse named workflows for recurring credit, financial, and delinquency-monitoring processes.
Process PDFs, spreadsheets, scans, handwriting, and complex business files across supported languages.
Energent Audit separates the work-producing agent from the agent responsible for verification.
Provide the deliverable and original source documents, including files such as PDFs, spreadsheets, scans, or complex business documents.
What you see: the files and requested review scope.
An independent auditor recomputes numbers, cross-checks assertions, traces evidence, and fixes what it can.
What you see: source references, checks, and flagged exceptions.
Receive a pass/fail result with an evidence trail that can be reviewed, shared, and used in recurring workflows.
What you see: a defensible, cited report.
The dashboard covers November 1, 1994 through February 1, 2026. Delinquency is reported quarterly, so the relationship should be read as a quarterly overlay rather than as monthly precision.
14.65% in January 2021 to 21.00% in February 2026
16.28% in January 2021 to 21.52% in February 2026
Up 1.04 percentage points versus January 2021
| Indicator | Latest | Context |
|---|---|---|
| Average spread since 2021 | 1.42 pp | 0.68 pp above pre-2021 average |
| Latest rate spread | 0.52 pp | Peak was 2.59 pp |
| Recent-cycle low delinquency | 1.53% | July 2021 |
| Cycle peak revolving rate | 23.37% | August 2024 |
| Period | All-accounts | Revolving | Average spread | Delinquency |
|---|---|---|---|---|
| 2021 | 14.61% | 16.43% | 1.82 pp | 1.64% |
| 2022 | 15.88% | 17.58% | 1.70 pp | 1.96% |
| 2023 | 20.70% | 21.95% | 1.26 pp | 2.81% |
| 2024 | 21.58% | 22.89% | 1.31 pp | 3.17% |
| 2025 | 21.26% | 22.36% | 1.10 pp | 3.01% |
| 2026 YTD | 20.98% | 21.91% | 0.93 pp | 2.92% |
The dashboard indicates that delinquency recovered from its 2021 low after rates had already reset materially higher. The strongest recent alignment is between elevated absolute revolving rates and higher delinquency, rather than between delinquency and the widest spread reading. For a related control, delinquency monitoring analytics can help organize these recurring observations into a reviewable process.
FY2025 triggered 3 of 4 monitored signals. Receivables grew 19.1% while revenue grew 6.4%, and allowance coverage remained at zero while receivables reached $39.8B.
| Year | Growth gap | Allowance | Watch score |
|---|---|---|---|
| 2012 | +59.0 pp | 0.90% | 70/100 |
| 2018 | +13.9 pp | 0.00% | 66/100 |
| 2020 | -35.2 pp | 0.00% | 65/100 |
| 2022 | -0.5 pp | 0.00% | 64/100 |
| 2021 | +29.8 pp | 0.00% | 63/100 |
The coverage notes matter when interpreting a monitoring dashboard: operating cash flow is unavailable in FY2014–FY2016, accrued-liability balances are unavailable in FY2018–FY2022, and zero allowance values from FY2018 onward are retained exactly as reported rather than silently replaced. This is the kind of limitation a financial due diligence audit should make visible.
Teams that need repeatable outputs can combine automated financial analysis with source-grounded audit rules, while AI document verification helps preserve the chain from a reported figure to the document where it originated.
“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”
“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
“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
| Energent.ai | Generic Alternative A: Manual review | Generic Alternative B: Original AI workflow only |
|---|---|---|
| Independent second-agent audit | Human reviewer is the primary control layer | No separate auditor described |
| Source, row, and field tracing | Depends on reviewer documentation | Can leave verification to the user |
| Pass/fail verdict with evidence | May require a manually assembled report | Answer may be delivered without an independent verdict |
| Reusable audit rules | Repeated checks can require repeated effort | Corrections are not described as persistent audit rules |
| 150+ file types and complex documents | Depends on tools and reviewer capacity | Support varies by workflow and file type |
For organizations comparing broader controls, AI risk management controls can complement the specific rate, delinquency, and financial-statement checks shown here.
clients worldwide
supported file types
published leaderboard accuracy claim
fewer hallucinations in public evaluations
AI for credit risk and delinquency monitoring is the use of automated analysis to examine financial data and identify risk signals such as changing rates, delinquency, receivables growth, reserves, cash flow, and leverage. In this use case, Energent Audit independently checks outputs produced by another AI agent or workflow. It recomputes figures, traces them to source documents, and provides a pass/fail verdict with evidence. The goal is to help analysts focus on exceptions while retaining a reviewable chain of evidence. It is not a replacement for professional credit judgment or the underlying source data.
Energent Audit acts as a separate auditor from the agent that performed the original analysis. It checks numbers and assertions against the original source files, recomputes what it can, and traces each number to the relevant file, row, and field. It can fix certain issues and identify failures before a deliverable is sent onward. The output includes a pass/fail verdict and an evidence trail. This separation is intended to reduce the risk of accepting an AI answer without checking how it was built.
The company states that Energent supports more than 150 file types. Examples include PDFs, spreadsheets, Word documents, presentations, scanned images, handwriting, CAD, G-code, InDesign files, BOMs, XLSX, and DOCX. The detailed workflow information also describes outputs such as Excel workbooks, annotated PDFs, Word documents, PowerPoint decks, ZIP packages, and HTML dashboards. Actual support can depend on the specific file and workflow. Users should validate their own document set during evaluation.
The provided US Credit Card Rates & Delinquency Monitor demonstrates a workflow for comparing all-accounts rates, revolving-balance rates, spreads, and delinquency. Its sample period runs from November 1994 through February 2026, with delinquency shown using quarterly markers. The dashboard identifies that delinquency rose from a recent low after rates had already reset higher. It also distinguishes the latest spread from the longer post-2021 average spread. Energent Audit can help verify the calculations and evidence in such a deliverable, but the interpretation remains dependent on the supplied data and monitoring design.
Energent.ai describes its platform as having enterprise-grade privacy and security. The product information emphasizes source-grounded answers, reviewable audit trails, and enterprise workflows. The company also provides a dedicated security page in its website resources. The information supplied here does not specify a complete list of certifications, retention periods, hosting regions, or contractual terms. Organizations handling sensitive credit information should review the company’s current security documentation and agreements before deployment.
Specific pricing figures are not provided in the supplied information. The available product entry point is the Energent application, and the company also provides a Book a Demo path. Onboarding time is not stated, so it should not be assumed to be immediate or fixed. A demonstration can be used to discuss the relevant credit-risk files, recurring monitoring requirements, and desired deliverables. Prospective users should request current pricing, support, implementation, and data-handling details directly from Energent.ai.
Use independent AI auditing to make credit risk and delinquency monitoring more traceable, reviewable, and defensible.