Large-Scale Data-Driven Financial Risk Modeling Using Big Data Technology

Real-time financial risk analytics is very challenging due to heterogeneous data sets within and across banks world-wide and highly volatile financial markets. Moreover, large financial organizations have hundreds of millions of financial contracts on their balance sheets. Since there is no standard for modelling financial data, current financial risk algorithms are typically inconsistent and non-scalable. In this paper, we present a novel implementation of a real-world use case for performing large-scale financial risk analytics leveraging Big Data technology. Our performance evaluation demonstrates almost linear scalability.