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Model Risk Management Systems-Back-End, Middleware, Front-End and Analytics

Authors: Hevel Jean-Baptiste; Meikang Qiu; Keke Gai; Lixin Tao;

Model Risk Management Systems-Back-End, Middleware, Front-End and Analytics

Abstract

Building an effective Model Risk Management (MRM) system requires advanced skills in mutliple fields, such as technology, econometrics, and business. It is a dilemma that MRM practitioners are struggling with. Very often the enterprises are seeking someone who can understand their business ideas and transform them to technology solutions or come up with technology solutions to support their MRM ideas. However, many quantitative analysts fail in the required software development skills, while they are critical in this type of the project. We are witnesses these struggles where some model risk leaders fail in finding competent technologists or econometricians to implement their risk management philosophy. Also we have cases where technologists or econometricians cannot receive proper requirements to implement the functionality that the business needs. In this paper, we propose a MRM framework based on a review of MRM systems. Also we present the methodology of producing an effective MRM system.

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Top 10%
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