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Publication type: Conference paper
Type of review: Peer review (publication)
Title: Large-scale data-driven financial risk modeling using big data technology
Authors: Stockinger, Kurt
Heitz, Jonas
Bundi, Nils Andri
Breymann, Wolfgang
DOI: 10.1109/BDCAT.2018.00033
Proceedings: 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT)
Page(s): 206
Pages to: 207
Conference details: 5th International Conference on Big Data Computing, Applications and Technologies (BDCAT), Zurich, Switzerland, 17-20 December 2018
Issue Date: 2018
Publisher / Ed. Institution: IEEE
ISBN: 978-1-5386-5502-3
Language: English
Subjects: Big data; Data modeling; Parallel processing; Computational finance
Subject (DDC): 332.6: Investment
Abstract: 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.
Fulltext version: Published version
License (according to publishing contract): Not specified
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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