AI Explainability Policy Toolkit: Practical templates for banking and credit decisioning
January 2026 · Developed by Cicrim AI & Compliance Practice
Banks are under pressure to deploy AI responsibly while also proving to examiners, customers and boards that models are explainable, fair and well-controlled. Policies often lag behind the technology: They describe high-level principles but provide little guidance for model owners, developers and compliance teams.
The Cicrim AI Explainability Policy Toolkit bridges that gap. It provides ready-to-tailor templates that translate supervisory expectations and industry best practices into concrete roles, processes and documentation for AI-enabled credit, marketing and fraud models.
The toolkit is designed for banks that are already using or evaluating explainable AI methods such as SHAP, feature importance and counterfactual explanations, and need those methods to be embedded in policy, not just in code.
Key outcomes
- Define what explainable AI means for your institution, by product and use case.
- Clarify responsibilities across model owners, risk, compliance, audit and vendors.
- Standardize documentation, monitoring and evidence for fair-lending and model risk.
- Give boards and committees clear, non-technical talking points and dashboards.
- Reduce rework during exams by front-loading the structure regulators expect to see.
Policy template
AI explainability & governance
A bank-ready policy outline that covers objectives, scope, definitions, minimum control requirements and links to your model risk framework.
Operational guides
From SHAP plots to decisions
Checklists and example language that explain how technical explainability artifacts feed into credit decisions, overrides and adverse action notices.
Evidence packages
Exam-ready documentation
Sample evidence structures for model files, testing logs, fair-lending assessments and governance minutes that can be adapted to your environment.