Banking Comprehensive Risk Management System based on Big Data Architecture of Hybrid Processing Engines and Databases

Banks are shifting from a simple credit risk management model to the comprehensive risk management model. Banking risks come from many channels and systems. Big data technology provides an innovative and effective solution for data management, and thus is suitable to be applied in the risk management scenarios that require high-quality data and complex data analysis. This paper firstly proposes big data architecture of hybrid processing engines and databases. This architecture uses Hadoop ecosystem with ETL and Spark processing engines, and using massive parallel processing databases (MPP), transactional databases, and HDFS. Then a banking comprehensive risk management system prototype based on the proposed big data architecture is implemented. Comparisons and evaluations clearly demonstrate that the proposed system has better performance.