Publication type: Book part
Type of review: Editorial review
Title: Large-scale data-driven financial risk assessment
Authors: Breymann, Wolfgang
Bundi, Nils
Heitz, Jonas
Micheler, Johannes
Stockinger, Kurt
DOI: 10.1007/978-3-030-11821-1_21
Published in: Applied data science : lessons learned for the data-driven business
Editors of the parent work: Braschler, Martin
Stadelmann, Thilo
Stockinger, Kurt
Page(s): 387
Pages to: 408
Issue Date: 14-Jul-2019
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-11820-4
978-3-030-11821-1
Language: English
Subjects: Stress test; Big data; Simulation; Financial risk
Subject (DDC): 005: Computer programming, programs and data
332: Financial economics
Abstract: 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/4501
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Institute of Data Analysis and Process Design (IDP)
Published as part of the ZHAW project: Large Scale Data-Driven Financial Risk Modelling
Appears in collections:Publikationen School of Engineering

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