Implementing Fair Lending Guardrails
As AI and GenAI accelerate decisioning and customer interactions in financial services, fair lending expectations remain non-negotiable. This workshop helps leaders understand fair lending obligations at a practical level, recognize where bias risk can emerge, and define governance and control best practices that support consistent, defensible oversight as AI use expands.
Leave with a clear understanding of fair lending guardrail best practices—and actionable next steps to strengthen oversight across AI-enabled lending initiatives.
Fair lending risk can increase when AI-driven decisions scale faster than governance and testing practices.
- Regulatory expectations are complex: Leaders need a clear, shared view of what fair lending obligations require in day-to-day decision-making.
- Bias risk hides in plain sight: Seemingly neutral policies, inputs, or processes can create uneven outcomes across customer groups.
- Controls lag innovation: Without repeatable assessments and governance routines, issues surface late—when fixes are costly and disruptive.
When fair lending guardrails aren’t explicit and repeatable, AI-enabled lending becomes harder to defend—and harder to scale responsibly.
We equip leaders with best practices and a practical approach to strengthen fair lending guardrails across AI-enabled lending programs.
- Fair lending obligations, made actionable: Translate regulatory expectations into clear oversight questions and decision standards leaders can apply consistently.
- Bias-risk mapping across the lending journey: Identify where risk can emerge across policies, decisions, communications, and exceptions.
- Input and feature scrutiny standards: Establish practical criteria for evaluating what information influences outcomes and where concerns may arise.
- Impact assessment and evidence expectations: Define what “good” looks like for evaluating outcomes, documenting rationale, and supporting defensibility.
- Governance and control operating rhythm: Align on roles, approvals, monitoring, and escalation paths that keep guardrails current over time.
- Recognize regulatory obligations related to fair lending and discrimination
- Identify risks of bias in AI models for credit decisioning and pricing
- Review data inputs and model features for fairness and disparate impact
- Simulate impact assessments using fairness audits and reporting tools
- Establish controls and governance practices to ensure ongoing model fairness in lending
Establish a shared understanding of fair lending guardrail best practices leaders can apply across AI-enabled lending initiatives
Prioritize a view of where bias risk is most likely to emerge—and what to address first
Apply a practical set of oversight questions and decision standards to guide approvals and reviews
Set clear expectations for impact assessment evidence, documentation, and accountability
Identify a set of actionable next steps to strengthen controls, monitoring, and escalation routines over time
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes