"Transaction fraud" complaint verification, i.e., verifying whether a transaction corresponding to a complaint is fraudulent, is particularly critical to prevent economic loss. Compared with traditional fraud pre-transaction detection, complaint verification puts forward higher requirements: 1)an individual tends to exhibit different identities in different complaints, e.g., complainant or respondent, requiring the model to capture identity-related representations corresponding to the complaint; 2)the fraud ways evolve frequently to confront detection, requiring the model to perform stably under different fraud ways. Previous methods mainly focused on fraud pre-transaction detection, utilizing the historical information of users or conduct message passing based GNNs on relationship networks. However, they rarely consider capturing various identity-related representations and ignore the evolution of fraud ways, leading to failure in complaint verification. To address the above challenges, we propose the mutual information based dual level graph neural network, namely MIDLG, which defines a complaint as a super-node consisting of involved individuals, and characterizes the individual over node-level and super-node-level. Furthermore, the mutual information minimization objective is proposed based on "complaint verification-causal graph" to decouple the model prediction from relying on specific fraud ways, and thus achieve stability. MIDLG achieves SOTA results through extensive experiments in complaint verification on WeChat Finance, one online payment service serving more than 600 million users in China.