AI Credit Decisioning: A Beginner’s Guide
Feb. 17, 2025 · Authored by Terrence A. Thomas
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AI can support credit decisions, but policy, customer treatment, reasons, authority, and evidence remain the lender’s responsibility. This guide helps banking teams understand the system and its controls before choosing technology.
Purchase AI Credit Decisioning: A Beginner’s GuideInside the guide
The guide organizes the discussion around five connected paths:
- Decision roles
- Distinguish eligibility, scoring, pricing, limits, fraud, document processing, routing, and human-support uses.
- Data path
- Identify sources, permissions, quality, derived fields, sensitive attributes, and feedback loops.
- Model path
- Understand training, validation, thresholds, reasons, uncertainty, monitoring, and change.
- Customer path
- Map application, review, adverse action, dispute, correction, appeal, and complaint handling.
- Governance path
- Assign owners, approvals, vendor oversight, testing, logs, issues, and stop authority.
Put the guide into practice
- Learn: Map one current decision without adding AI.
- Frame: Define the proposed AI role and a bounded success measure.
- Challenge: Ask what can fail, who is affected, and how the bank will know.
- Pilot: Use constrained volume with manual review and complete evidence.
Review reason accuracy, manual-review disagreement, outcome drift, and customer corrections and complaints. The publication provides management questions and an action sequence; it does not promise a particular approval lift, latency, or return on investment.
