AI Risk and Liability in Debt Collection Practices

Identify FDCPA risks in automated collection systems and build defensible controls.

(Problem) Debt collection now runs largely on automated systems; dialers, messaging platforms, account-routing logic, scoring models, and reporting tools; yet many collection operations do not fully understand how those systems create liability that a human-run process would have avoided. Automated workflows can contact consumers too often or at the wrong times, send inaccurate or misleading messages at scale, omit required disclosures, and fail to recognize when a consumer has disputed a debt or asked the collector to stop. Under the FDCPA, each of those missteps can become a violation that repeats across an entire portfolio. (Solution) This program explains how federal debt collection requirements apply inside an automated environment and pinpoints where AI-driven collection systems most often generate risk. Attendees will see how a single automated error cascades into systemic violations, how regulators and courts view automation when they assess a collector’s conduct, and which oversight and documentation practices help build a defensible operation. The session then walks through practical, build-in safeguards;

human review checkpoints, monitoring and audits, and recordkeeping — that reduce regulatory and litigation exposure without sacrificing the efficiency that makes automation valuable. Compliance officers, collection operations leaders, and the attorneys who advise them will leave able to identify their highest-risk automated processes and implement controls that withstand scrutiny.

Learning Objectives

• You will be able to describe today’s automated collection environment.
• You will be able to explain FDCPA requirements for automation.
• You will be able to identify high-risk AI collection processes.
• You will be able to discuss how errors cascade into violations.