A Unified Global and Local Outlier Detection Framework with Application to Chinese Financial Budget Auditing

The identification of anomalous data objects within massive datasets is a critical technique in financial auditing. Most existing methods, however, focus on global outlier anomalies detection with less effective in contexts such as Chinese financial budget auditing, where local outliers are often more prevalent and meaningful. To overcome this limitation, a unified outlier detection framework is proposed that integrates both global and local detection mechanisms using k-nearest neighbors (KNN) and kernel density estimation (KDE) methodologies. The global outlier score is redefined as the sum of the distances to the k-nearest neighbors, while the local outlier score is computed as the ratio of the average cluster density to the kernel density—replacing the cutoff distance employed in Density Peak Clustering (DPC). Furthermore, an adaptive adjustment coefficient is further incorporated to balance the contributions of global and local scores, and outliers are identified as the top-ranked objects based on the combined outlier scores. Extensive experiments on synthetic datasets and benchmarks from Python Outlier Detection (PyOD) demonstrate that the proposed method achieves superior detection accuracy for both global and local outliers compared to existing techniques. When applied to real-world Chinese financial budget data, the approach yields a substantial improvement in detection precision-with 38.6% enhancement over conventional methods in practical auditing scenarios.