In order to explain and defend AI-powered KYC decisions, banks should follow a 10-point checklist when picking and deploying any AI KYC tool: Model inventory: Transitioning to KYAI requires financial firms to integrate systems and processes that offer visibility into AI’s decision-making logic. Before that can happen, every AI model used within the organization must be cataloged. This inventory includes details like purpose, scope, input data, model design, and deployment status. Explainability: Explainable AI ensures that business users, regulators, and customers understand how outputs are generated. Whether through statistical metrics or visual explanations, the objective is to demystify the decision-making process. Risk assessment and classification: Risk assessment and classification provides the foundation for AI governance by systematically evaluating and categorizing AI systems based on their potential impact and regulatory requirements. This component enables institutions to allocate resources effectively and apply appropriate controls.Audit logs: Audit trails serve as the backbone of KYAI compliance. Every decision must leave breadcrumbs that regulators and internal stakeholders can trace. These logs should highlight data points, model iterations, and the reasoning behind predictions. Ideally, audits should be conducted pre-deployment and on an ongoing basis once the model is up and running. Validation and testing: Model validation and testing ensures ongoing model performance and reliability through comprehensive testing protocols, including back testing, stress testing, and challenger model frameworks. Real-time bias monitoring: KYAI ensures tools are in place to monitor for bias or anomalies in production models. For example, systems can flag when a fraud detection algorithm disproportionately targets transactions from certain regions. Model cards: Inspired by food nutrition labels, “model cards” summarize an AI model’s purpose, strengths, limitations, data sources, and potential biases. These concise documents provide an accessible overview for both regulators and team members. Updated governance frameworks: As AI models are adopted and integrated, it will be essential to continually revamp AI-specific governance policies into your existing structures. Define roles and responsibilities to monitor adherence to explainability, audit, and risk standards. Communicate with customers: Transparent decision-making builds greater customer trust. A client declined for a loan, for example, can be shown an objective explanation of why and how to improve their chances in the future. Monitor and evolve: KYAI is not a static, set it and forget it process. Teams should regularly monitor results and test accuracy, evaluate governance frameworks after new deployments and adjust processes in line with evolving regulatory requirements.