AI Small Business Lending Disparate Treatment Analysis Playbook

A bank's SBLO (Small Business Lending Officer) program has discretion to approve loans up to $250,000 without committee review. Internal data shows Hispanic-owned businesses are declined at a rate 31% higher than similarly-situated white-owned businesses in the same revenue and credit tier.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A bank's SBLO (Small Business Lending Officer) program has discretion to approve loans up to $250,000 without committee review.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Small Business Lending Disparate Treatment Analysis".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Small business loan application file (past 24 months, all decisions)
• Business owner demographic data (where collected)
• Business financial characteristics (revenue, years in operation, credit score)
• Loan officer identifier and approval/denial decision
• Bank's credit policy for the SBLO program

Attachments: Documents (Documents)

You are a fair lending analyst investigating disparate treatment in small business lending at a bank. I am attaching:

Work only from the attached source files. If a conclusion is not supported, say so.

Produce:
1. Control for all legitimate credit factors (business revenue, owner credit score, years in operation, industry, loan amount) and quantify the residual disparity in denial rates by race/ethnicity.
2. Identify whether the disparity is driven by loan officer discretion, credit policy application, or loan structuring differences (collateral requirements, pricing).
3. Flag specific loan officers whose denial rates for Hispanic-owned businesses are statistically anomalous compared to their peers.
4. Assess whether the SBLO's discretionary authority—no committee review under $250K—creates a fair lending governance gap.
5. Tell me the regulatory exposure under the Equal Credit Opportunity Act and what immediate policy changes reduce risk before the next examination.

Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.

What to expect

• Regression-controlled denial rate disparity
• Disparity driver analysis (discretion vs. policy)
• Loan officer anomaly flags
• SBLO governance gap assessment
• ECOA exposure and pre-examination remediation steps

Review before you act

• Validate this output against source files before relying on it: Control for all legitimate credit factors (business revenue, owner credit score, years in operation, industry, loan amount) and quantify the residual disparity in denial rates by race/ethnicity.
• Validate this output against source files before relying on it: Identify whether the disparity is driven by loan officer discretion, credit policy application, or loan structuring differences (collateral requirements, pricing).
• Validate this output against source files before relying on it: Flag specific loan officers whose denial rates for Hispanic-owned businesses are statistically anomalous compared to their peers.
• Validate this output against source files before relying on it: Assess whether the SBLO's discretionary authority—no committee review under $250K—creates a fair lending governance gap.
• Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
• Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
• Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.