A supervised record linkage approach for anomaly detection in insurance assets granular data

Nowadays public authorities and research organizations compile and disseminate statistics based on granular data with rapidly increasing volumes. Efficient statistical methods for data quality management are essential to ensure high quality in the produced statistics and consequently in the policy decisions. In order to guarantee smooth data quality checks, such methods need to be automatic, especially during situations of constraints on human resources. This paper deals with an issue of anomaly detection in very granular insurance data which are periodically used by central banks to produce European statistics. Since 2016, insurance corporations have been reporting granular assets data in Solvency II templates on a quarterly basis. Assets are uniquely identified by codes that by regulation must be kept stable and consistent over time; nevertheless, due to reporting errors, unexpected changes in these codes may occur, leading to inconsistencies when compiling statistics and analysing balance sheets. The current work addresses the data quality issue as a record linkage problem and proposes different supervised classification models to detect anomalies in the data. Test results for the selected random forest model provide excellent performance metrics, robust to different periods and types of assets.