Large-Scale Data-Driven Financial Risk Assessment
The state of data in finance makes near real-time and consistent assessment of financial risks almost impossible today. The aggregate measures produced by traditional methods are rigid, infrequent, and not available when needed. In this chapter, we make the point that this situation can be remedied by introducing a suitable standard for data and algorithms at the deep technological level combined with the use of Big Data technologies. Specifically, we present the ACTUS approach to standardizing the modeling of financial contracts in view of financial analysis, which provides a methodological concept together with a data standard and computational algorithms. We present a proof of concept of ACTUS-based financial analysis with real data provided by the European Central Bank. Our experimental results with respect to computational performance of this approach in an Apache Spark based Big Data environment show close to linear scalability. The chapter closes with implications for data science.
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One of the authors (W.B.) thanks the External Statistics Division of the European Central bank for having received him as visitor during his sabbatical. Without the ensuing collaboration this study could not have been carried out. We further thank Drilon Prenaja and Gianfranco Rizza for running the performance experiments as part of their bachelor thesis. The work is funded by the Swiss Commission for Technology and Innovation under the CTI grant 25349.1.
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