Large-Scale Data-Driven Financial Risk Assessment

Breymann, Wolfgang; Bundi, Nils; Heitz, Jonas; Micheler, Johannes; Stockinger, Kurt

doi:10.1007/978-3-030-11821-1_21

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…As another example, in some templates, required information (e.g., client's income) needs to be extracted from a paragraph in the body text, while, in some other templates, the same information is found inside a table. Therefore, although a regulatory body can provide guidelines about some general structure of SoA, according to Breymann et al there is no standard governing the finer level structure of SoA [8]. The practice of using domain-specific vocabularies by RFAs further complicates the use of a fully automated process for information extraction from SoA documents.…”

Section: Background and Related Workmentioning

confidence: 99%

SRAuditor: An Automated Assessment Tool for Statement of Advice Documents

Kang¹,

Forkan²,

Jayaraman³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Financial advice is given by a registered financial adviser (RFA) in the form of a statement of advice (SoA) document. To limit liability, financial advisor groups periodically assess SoA documents for compliance with legal regulations, internal policies, and best practices. However, this is a manual process that is often subjective, time-consuming and tedious. In this paper, we propose, implement and evaluate SoA Risk Auditor (SRAuditor), a natural language processing (NLP) framework to automatically assess and audit SoA documents. SRAuditor consists of two major components. The first one is a SoA transformer (SoA-T), a tool that automatically transforms and maps SoA document (generally a PDF). The other one is a question-answering engine (QA-R) that recommends legally compliant answers based on rule-based approaches for given SoA audit questions to assess and audit SoA documents. We validate the accuracy of SRAuditor's ability by evaluating it against assessments conducted by domain experts (i.e., financial advisors, lawyers). Experimental results using real-world SoA documents provided by our industry partner, Fourth Line Pty Limited indicates that SRAuditor has a high potential to be used for automatically assessing and auditing SoA documents.

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Section: Background and Related Workmentioning

confidence: 99%

SRAuditor: An Automated Assessment Tool for Statement of Advice Documents

Kang¹,

Forkan²,

Jayaraman³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…However, Big Data technologies have not yet been used for simulation approaches to financial risk measurement that are based on the cash flows generated by individual financial contracts. While building the core to existing analytical platforms [28] and, on a conceptual level, to novel regulatory infrastructure [29], up to now the ACTUS approach has only been tested on a small scale [30]. A large scale test of this approach is the missing element needed in order to build a scalable financial simulation and analysis environment that gets to the core of the financial systems and provides the means to flexibly assess all kinds of risks combined with the capability of flexible, situation-based analytics.…”

Section: Related Workmentioning

confidence: 99%

Scalable architecture for Big Data financial analytics: user-defined functions vs. SQL

et al. 2019

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Today, nearly all money exists in form of numbers in a computer, and finance can be considered as a special kind of IT application that represents the flow of money in form of cash flows between different participants. Thus, automated processing seems to be a natural choice and the financial sector should be expected to lead digitization and automation initiatives. It is all the more surprising that not only is this not the case but, on the contrary, the financial sector is lagging behind other sectors. In 2008, when Lehman Brothers went bankrupt at the height of the financial crisis, nobody-neither the big banks nor the regulatory authorities-had the structures and processes in place to systematically measure, even imprecisely, the systemic aspects of the risks inherent in the development of subprime lending, securitization, and risk transfer [1]. As a consequence, the top management did not have an adequate picture of these risks so that they could be denied during the build-up of the bubble and nobody was able to evaluate the implications of the failure of major financial institutions when the crisis eventually hit. The major shortcoming identified by the Basel Committee on Banking Supervision

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“…In particular, this is essential for large financial institutions with about 10 8 contracts and for Monte-Carlo simulation based analytics where each instrument has to be evaluated under up to 10 5 scenarios, thus resulting in a Big Data and Big Computation problem. Figure 1 shows the data flow of the ACTUS framework [4]. The data input required for the simulation step essentially consists of contract data and risk factor scenarios:…”

Section: Introductionmentioning

confidence: 99%

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

Stockinger¹,

Heitz²,

Bundi³

et al. 2018

2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT)

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Real-time financial risk analytics is very challenging due to heterogeneous data sets within and across banks worldwide 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 largescale financial risk analytics leveraging Big Data technology. Our performance evaluation demonstrates almost linear scalability.

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Large-Scale Data-Driven Financial Risk Assessment

Cited by 4 publications

References 5 publications

SRAuditor: An Automated Assessment Tool for Statement of Advice Documents

SRAuditor: An Automated Assessment Tool for Statement of Advice Documents

Scalable architecture for Big Data financial analytics: user-defined functions vs. SQL

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

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