Is Your ZIP Code Killing Your Credit Approval?

Scan for hidden proxy variables lenders use to deny approvals — ZIP code, education, shopping habits, and more.

Find out if lenders are using your ZIP code against you.

Your Credit Score Isn't the Problem. Your Data Profile Is.

2026 underwriting has shifted to black-box AI models and agentic credit decisions. These models don't need a race column — they infer protected traits from proxies.

Your ZIP code, education, and shopping behavior can leak protected characteristics into underwriting decisions. Regulators now treat proxy discrimination like direct discrimination.

ZIP code explains a large share of approval variance in many models

Cash-flow and shopping behavior frequently act as race proxies

Regulators now treat proxy discrimination like direct discrimination

Black-box models reject applicants without human-readable explanations

What Non-Credit Data Is Being Used Against You?

Select which proxy variables to scan. We only model how these variables can leak protected traits into underwriting decisions.

Select proxy variables to scan:

We don't need your full file. We only model how these variables can leak protected traits into underwriting decisions.

How the Bias Coefficient Works

This tool imitates SHAP-style thinking: how much each variable “pulls” a model toward decisions correlated with protected attributes like race, ethnicity, and income level.

Bias Coefficient = Sum of (Proxy Variable Weight x Correlation with Protected Traits)

Variable	Proxy Strength	Bias Contribution
ZIP Code	High	0.42
Education	Medium	0.21
Shopping Categories	Medium	0.18

What's Inside Your Bias Report

Proxy Variable Map

Which non-credit signals likely act as proxies in your underwriting profile.

Bias Coefficient Timeline

How your risk changes if you remove or adjust certain variables.

Conversation Blueprint

Talking points for disputes, complaints, or advocacy group conversations.

I understand this is an educational diagnostic, not a legal determination.

Why Regulators Care About Proxy Variables

Regulators now focus on effects (disparate impact) rather than explicit race columns. Proxies like ZIP code and education level are central to modern algorithmic redlining cases. The CFPB and FTC have made proxy discrimination a top enforcement priority for 2026.

What is algorithmic redlining?+

Algorithmic redlining occurs when AI-powered lending models systematically deny or penalize applicants from certain neighborhoods, demographics, or backgrounds — not through explicit race data, but through proxy variables that correlate with protected traits.

How can ZIP codes act as race proxies?+

ZIP codes are strongly correlated with racial demographics due to decades of housing segregation. When a model uses ZIP code as a feature, it effectively encodes race information without ever seeing a race column.

Does this tool access my credit file?+

No. This tool does not pull your credit report or access any bureau data. It models risk based solely on the variables you enter, simulating how those variables can leak protected traits into underwriting decisions.

Can I use this in a complaint to regulators?+

Yes. While this tool doesn't constitute legal evidence, the bias coefficient and proxy breakdown can help frame a CFPB complaint or support an attorney's disparate impact analysis.

Is this legal advice?+

No. This is an educational diagnostic tool. It models proxy risk but does not provide legal opinions. Consult a consumer protection attorney for specific legal guidance.

What is a bias coefficient?+

A bias coefficient measures how strongly your non-credit data correlates with protected attributes in typical underwriting models. Higher coefficients mean lenders are more likely using proxy variables that discriminate.

How do shopping categories become proxies?+

Where you shop reveals income level, neighborhood, and lifestyle patterns that correlate with race and ethnicity. Models trained on transaction data can infer protected traits from purchase patterns at specific retailers.

What should I do if my bias coefficient is high?+

Document every credit denial, request adverse action notices, file CFPB complaints citing proxy discrimination, and consult a consumer protection attorney. The bias report provides conversation blueprints for each of these steps.