Payments Leaders Require AI Controls Explainable in 24 Hours

Baran Ozkan, co-founder and CEO of Flagright, lays out a practical playbook for payments teams in a PYMNTS eBook and argues that AI controls must be explainable and defensible within 24 hours. He frames the core failure mode as an operating-discipline gap between a model output and the downstream business decision, not simply poor model accuracy.

Ozkan prescribes staged rollouts with explicit control checkpoints: version control, replayability of historical cases, and human override points. He emphasizes treating every third-party model, screening source, identity signal, or data provider as a governed dependency with clear requirements. Recommended controls include:

• •performance SLAs and change-notification rights for vendors
• •audit logs and evidence capture to reconstruct decisions
• •independent validation and local replays of third-party models
• •fallback plans and concentration-risk mitigation

Payments systems amplify failures because transaction volume and automation scale error propagation rapidly. The piece reframes common compliance language into operational instructions practitioners can implement: log the evidence that led to a decision, assign decision ownership, require rollback paths, and codify when humans must intervene. This is not theoretical governance; it is risk control that materially reduces regulatory, financial, and reputational exposure in high-throughput environments.

Teams should prioritize engineering work that enables replayability and explainability over aggressive performance optimization during initial deployments. Expect vendors to face rising demands for auditing features, change feeds, and local validation APIs as payments firms insist on governed dependencies.