Advertising operations (AdOps) include compliance review processes to ensure that advertisements adhere to applicable laws, platform policies, and industry guidelines. In particular, ads must avoid issues such as overclaiming effectiveness or making definitive assertions about uncertain outcomes. However, these reviews are typically conducted by experts, making them an operational bottleneck. In this paper, we propose the automated ad text compliance verification task and present our classifier-based system. We introduce AdCompliance, a dataset consisting of real pre-submission ad texts annotated by experts. Our experimental results demonstrate practical classification performance, suggesting that non-compliant ads can be effectively filtered while maintaining a low false positive rate. These results support practical integration into AdOps workflows.