Data Audit

Edward H K Ng

Chapter 5 in Risk Analytics:From Concept to Deployment, 2021, pp 57-67 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Data are at the heart of analytics and models. The most advanced software cannot compensate for data deficient in critical aspects. The implications of data issues may not be apparent. For academic research purposes, they can be overlooked with little cost, but if million or even billion-dollar decisions are made based on the results generated, the gravity is of a different order. These deficiencies discussed here are based on actual work on real data. Before any risk analytics or modeling is carried out, data audit is a necessity to ensure that any results obtained are reliable.

Keywords: Risk; Modeling; Basel II; Quantification; Data Management; Data Integration; Decision Support; Online Analytical Programming (search for similar items in EconPapers)
JEL-codes: D81 G32 (search for similar items in EconPapers)
Date: 2021
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