Comparison of IQR and Isolation Forest Methods for Outlier Detection in Insurance Claim Data at PT Askrindo Semarang Branch - Ramadhani, Jovita Wayan; Susanto, susanto (Jurnal Transformatika, 2026)
This study aims to compare two commonly used approaches for outlier detection, namely the statistical Interquartile Range (IQR) method and the machine learning–based Isolation Forest method, using a case study of insurance claim data from PT Askrindo Semarang Branch. The analyzed data include claim amounts and claim settlement duration. The univariate IQR method identified 9 claims classified as outliers, consisting of 5 claim amount outliers, 3 settlement duration outliers, and 1 combined outlier. Meanwhile, the multivariate Isolation Forest method with a contamination rate of 0.05 detected 6 anomalous claims, including extremely long settlement durations and unusual combinations of variable patterns. The results indicate that the IQR method is effective for rapid detection of extreme values, while Isolation Forest is more suitable for identifying complex anomalies across multiple variables. Therefore, the choice between IQR and Isolation Forest methods should be adjusted according to the analysis objectives and data characteristics.