Ensuring Data Integrity in Official Financial Statistics: A Review of Hybrid AI and XAI Methods in the Context of Market Efficiency and Value Investing

Abstract

The integrity and accuracy of financial data are prerequisites for market efficiency; however, data anomalies and quality issues severely compromise their “fitness for use” in sophisticated decision-making processes, such as value investing strategies. This article reviews the application of advanced artificial intelligence (AI) methods to enhance quality assurance, anomaly detection, and imputation within high-dimensional financial data streams. The paper critically evaluates both statistical-machine learning hybrids (e.g., ARIMA-LSTM) and deep learning combinations (e.g., autoencoder-based GANs), alongside Explainable Artificial Intelligence (XAI) techniques, assessing their utility against the strict auditability requirements of public trust institutions. The synthesized literature suggests that hybrid frameworks can potentially outperform monolithic approaches in detecting nonlinear manipulations and creating “high-fidelity” datasets. Furthermore, the study addresses the “black box” opacity challenge—a major barrier for regulatory and statistical agencies—discussing how methods like SHAP and LIME support, rather than independently ensure, the necessary interpretability of algorithmic decisions. Conclusions indicate that the synergy between the predictive power of advanced AI models and the transparency supported by XAI is a highly valuable component for modern market supervision, enabling effective data validation while supporting institutional accountability.