Compare headline and revolving-balance rates, quantify spread regime changes, and connect elevated pricing with delinquency in a reviewable workflow.
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Automated credit card pricing and spread analysis is a workflow that compares the headline all-accounts rate with the rate paid by balances that accrue interest, calculates the gap between them, and relates those movements to delinquency. Energent.ai helps teams recompute, trace, and cross-check figures against source documents instead of relying on unsupported AI output. Analysts, finance teams, accounting groups, and research teams can use the resulting pass/fail logic and evidence trail to review a result without rebuilding every calculation manually. For adjacent work, teams can connect this process with AI financial analysis and credit risk monitoring.
The dashboard covers November 1, 1994 through February 1, 2026, combining long-run rate history with recent-cycle markers and quarterly delinquency observations.
The all-accounts rate increased from 14.65% in January 2021 to 21.00% by February 2026. The revolving-balance rate rose from 16.28% to 21.52% over the same period.
The average spread since 2021 is 1.42 percentage points, 0.68 points above the pre-2021 average. The gap reached 2.59 points in August 2021 and was 0.52 points in the latest observation.
Delinquency recovered from a recent-cycle low of 1.53% in July 2021 and exceeded 3% in October 2023. The latest delinquency rate is 2.92%, while the revolving-balance rate remains 21.52%.
Because delinquency is reported quarterly, the overlay and scatter analysis use quarterly markers rather than implying monthly precision. The strongest recent alignment is between high absolute revolving rates and higher delinquency, not necessarily the widest spread reading.
Related workflows include revolving-balance pricing, portfolio analysis, and SEC 10-K analysis.
Annual averages show the repricing cycle clearly: rates moved sharply higher in 2023 and 2024, while delinquency reached its highest annual average in 2024.
| Period | All-Accounts Rate | Revolving Rate | 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% |
Compare both rate series and calculate the spread across the full sample.
Track important dates, including the 20% threshold and cycle peaks.
Trace numbers and assertions back to original source documents.
Rebase series to 100 so relative acceleration can be compared despite different levels.
Separate monthly rate observations from quarterly delinquency markers.
Turn repeating analysis jobs into reusable workflows whose corrections become persistent audit rules.
Upload or connect the relevant rate, spread, and delinquency materials for analysis.
What you see: source files organized for review.The independent AI auditor calculates comparisons, moving averages, indexed series, and quarterly relationships.
What you see: charts, tables, and evidence-linked findings.Inspect the pass/fail result, source trail, thresholds, and conclusions before sharing the output.
What you see: a stakeholder-ready audit report.“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.”
Teams can also explore automated financial reporting for related recurring work.
| Energent.ai | Manual review | Unverified AI output |
|---|---|---|
| Recomputes and cross-checks numbers | Requires people to rebuild calculations | May provide assertions without recomputation |
| Evidence trail and pass/fail verdict | Evidence is assembled manually | No stated evidence trail in the described approach |
| Monthly and quarterly views | Timing alignment is manual | Can imply precision without source-aware handling |
| Reusable workflows and audit rules | Repeat work must be recreated | Corrections may not persist as rules |
| 150+ file types and brandable outputs | Depends on separate tools and processes | File and output support varies |
It is a source-grounded workflow for comparing the headline all-accounts credit card rate with the revolving-balance rate. The workflow calculates the percentage-point spread between those series and examines how the spread changes over time. It can also compare rate movements with delinquency observations. The purpose is to make repricing and credit-stress analysis easier to reproduce and review. Energent.ai presents the results with calculations, visualizations, and an evidence trail.
This use case is relevant to analysts, finance and accounting teams, operations and procurement groups, research teams, and enterprise users. It is particularly useful when a team needs to compare several financial series and explain the result to other stakeholders. Users can work from natural-language prompts rather than needing to be experts in every data-processing step. The workflow supports high-volume analysis and stakeholder-ready outputs. The company also describes applications across analytics and finance workflows.
The supplied dashboard covers November 1, 1994 through February 1, 2026. It includes an all-accounts rate, a revolving-balance rate, their spread, and delinquency. The latest all-accounts rate is 21.00%, while the latest revolving-balance rate is 21.52%. The latest spread is 0.52 percentage points and the latest delinquency rate is 2.92%. Delinquency is reported quarterly, so the relationship views use quarterly markers.
The company states that the platform supports more than 150 file types. Listed examples include PDFs, XLSX files, DOCX files, scans, CAD, G-code, InDesign files, and bills of materials. This breadth is intended for workflows that combine structured and complex documents. The platform can recompute, trace, and cross-check figures from deliverables rather than treating every file as plain text. Actual workflow suitability depends on the files and rules used for a specific analysis.
Energent.ai describes an independent AI auditor that verifies outputs against original source documents. It recomputes numbers, traces assertions, and cross-checks the result before producing a pass/fail verdict. Each result is intended to include an evidence trail that a reviewer can inspect. The company cites 3× fewer hallucinations in public evaluations, which is a company claim rather than an independent conclusion on this page. Reusable workflows can preserve corrections as audit rules for later jobs.
Specific pricing for this use case is not provided in the supplied information. Users can access the product entry point or request a demonstration through the available Energent.ai links. A demonstration can help clarify how the workflow maps to a team’s files, rules, and reporting requirements. The page does not claim a particular free-trial duration, subscription amount, or implementation cost. Teams should confirm current commercial details directly with Energent.ai.
Start a source-grounded workflow or talk with the Energent.ai team about your analysis process.