Consumer credit default rates exhibit complex temporal dynamics that challenge traditional risk monitoring frameworks. This research develops an optimization methodology for anomaly detection algorithms through advanced time-series feature extraction techniques applied to consumer credit default patterns. The proposed framework integrates statistical feature engineering with adaptive machine learning algorithms to identify aberrant default rate behaviors across multiple temporal scales. Experimental validation employs Federal Reserve consumer credit data spanning 2010-2024, encompassing credit card charge-offs, delinquency transitions, and macroeconomic indicators. The optimization strategy incorporates dynamic threshold adjustment mechanisms coupled with ensemble-based feature selection to enhance detection sensitivity while minimizing false positive rates. Comparative analysis demonstrates that the optimized Isolation Forest algorithm achieves 87.3% detection accuracy with a 0.923 AUC-ROC score, outperforming baseline methods by 18.7% in early warning capability. The framework successfully identified 92% of significant default rate mutations with an average lead time of 3.2 months before traditional statistical control charts. Implementation of adaptive feature weighting reduces computational complexity by 34% while maintaining detection performance. These findings establish a robust analytical foundation for real-time consumer credit risk surveillance systems.
