Modeling study demonstrates adaptive fraud identification using multisource financial data, highlighting scalable anomaly detection across corporate and engineering systems.
Key Points
To develop an intelligent and adaptive fraud identification framework that combines big data mining and machine learning to capture complex, evolving financial fraud patterns.
Integrated multisource financial data, feature selection, feature engineering, and ensemble learning algorithms to build anomaly detection models.
Implemented dynamic model optimization, incremental updates, model interpretability enhancements, and human–machine collaborative feedback mechanisms.
Resolved operational challenges related to poor data quality, severe class imbalance, and low transparency in financial fraud detection.
Achieved robust, adaptive identification of hidden abnormal behaviors with cross-domain applicability to wireless sensor networks and signal analysis.