Monitor every AI lending decision before regulators do.
Avarent continuously monitors AI-powered lending decisions, detects disparate impact in real time, generates compliant adverse action notices, and prepares your institution for CFPB examinations without replacing your existing models.
What needs attention now
overviewOverview
Open compliance issues
findingsActive Findings
Severity breakdown
monitoringMonitoring Center
30-day approval-rate drift
disparityDisparity Trend
Readiness score snapshot
readinessExam Readiness
Adverse Impact Ratio
airAIR Analysis
Variable risk flagging
proxyProxy Detection
Reg B reason validation
adverse_actionAdverse Action
Segment approval rates
cohortCohort Analysis
Disparity without an alert
driftDrift Monitor
When your model denies but can't say why.
Most AI models return a score with no usable reason — and generic buckets like “credit history” fail examiners. Avarent reads the denied application and surfaces a set of plausible, data-grounded reasons your compliance officer can actually use. Model vendors only explain the models they built. Consultants aren't always-on. Avarent is neither.
Works on any model
Sits downstream of your existing underwriting. No model swap, no vendor lock — even black-box models that return a score and nothing else.
A set of reasons, not a guess
For each denial we surface a ranked set of plausible, data-grounded reasons — your compliance officer reviews and selects, keeping a human in the loop.
Built for the specificity standard
Every candidate reason is grounded in the applicant's own data and written to the CFPB Circular 2023-03 behavioral-specificity bar.
Application #4821 · Denied by model
No reason returned
Debt-to-income ratio above program threshold
Monthly obligations / stated income = 47%
Length of credit history below minimum
Oldest tradeline opened 14 months ago
Recent delinquency on revolving account
30-day late reported within last 6 months
Every AI lending decision creates regulatory risk.
Without continuous oversight, bias, model drift, and documentation gaps accumulate long before anyone notices.
Hidden Fair Lending Risk
Disparate impact often develops gradually across thousands of lending decisions before traditional reviews detect it.
Model Drift
Production behavior changes over time even when model code never changes.
Manual Examinations
Regulatory preparation becomes expensive when evidence is scattered across systems.
Incomplete Decision Explanations
Poor documentation increases compliance risk and slows customer dispute resolution.
Everything your risk team needs in one platform.
Monitor decisions, validate models, investigate alerts, generate regulatory evidence, and maintain continuous oversight from a single workspace.
| ID | Category | Issue Description | Affected Group | Severity | Age | Status |
|---|---|---|---|---|---|---|
| FN-204 | Portfolio | Adverse Impact Ratio Breach | Mortgage approval rate — Hispanic / Latino | Critical | 1d | Investigating |
| FN-199 | Mortgage | Sequential Proxy Correlation ... | Priya K. Sharma | Critical | 1d | Resolved |
| FN-203 | Portfolio | Single Proxy Variable | DeShawn R. Brown | High | 1d | Resolved |
From lending decision to regulatory evidence in seconds.
Connect your lending platform.
Integrate with your existing underwriting workflow without replacing your models.
Monitor every decision.
Avarent evaluates fairness, explainability, model drift, and regulatory metrics continuously.
Prepare for examinations automatically.
Every alert, investigation, and explanation becomes searchable audit evidence.
Purpose-built for modern AI governance.
Evidence packet automation that meets regulatory standards
of adverse action notices in production fail the CFPB specificity standard — a finding category examiners already screen for.
Circular 2023-03 · Behavioral specificity required
Know what analysis is possible
Know which statistical analyses your current fields support before leadership or examiners ask questions your data cannot answer.
Disparity runs without an alert
Catch approval-rate divergence across origination periods before it becomes an open finding.
Aggregate by design
Maintain oversight without storing raw applicant PII — decision-level and cohort-level records only.
| Prime cohort | DIR: 1.02 | Pass |
| Thin File cohort | DIR: 0.61 | Review recommended |
Disparity at the four-fifths threshold
Surface four-fifths threshold breaches with plain-language labels before they reach an examination packet.
Average monitoring latency
Decision coverage
Bias detection
Audit evidence
Frequently Asked Questions
Most teams complete data ingestion and see initial monitoring within one week. Scoping and integration planning typically take a single working session.
No. Avarent works from decision-level outputs and logs. It does not require API access to the model itself, model weights, or the underlying scoring engine.
Avarent calculates approval-rate disparity and the disparate impact ratio against the four-fifths threshold, with plain-language labels alongside technical outputs. Alerts surface when cohort metrics cross configured thresholds.
Avarent assigns protected-class cohorts from decision-level attributes already present in your lending data. Analysis runs on aggregate cohort records — not raw applicant PII.
Open findings, methodology references, investigation logs, and adverse action documentation are compiled into a structured evidence packet formatted for MRM and fair-lending exam teams.
Avarent stores decision-level and cohort-level records only. No raw applicant PII. No bureau data. No model weights.
Know your regulatory risk before your next examination.
See how Avarent monitors lending decisions in real time and helps your team maintain continuous compliance.