Digest Finance

Predictive and prescriptive analyses: Theoretical considerations

Vol. 24, Iss. 3, SEPTEMBER 2019

PDF  Article PDF Version

Received: 22 May 2019

Received in revised form: 4 June 2019

Accepted: 17 June 2019

Available online: 30 September 2019


JEL Classification: G30, G32

Pages: 281–289


Kogdenko V.G. National Research Nuclear University MEPhI, Moscow, Russian Federation


Subject The article discusses theoretical considerations of predictive and prescriptive analyses.
Objectives The research summarizes algorithms and aspects of predictive, prescriptive analysis and identifies points of corporate growth triggered by the use of digital analytics.
Methods The research employs general principles and methods of research, such as analysis and synthesis, grouping and comparison, abstraction, generalization.
Results The article characterizes predictive and prescriptive analyses, modeling algorithms and identifies six focal points for analysis. I focus on key algorithms for modern analysis, i.e. setting trends and regression models, clusterization and classification of data, detection of data deviations and association analysis. The article reviews the algorithm used to build models, which involves training and test datasets. As part of each analysis, I find key aspects and points of corporate performance growth.
Conclusions and Relevance The article provides solutions for better business performance resulting from the use of digital analytics, i.e. adapting a product and marketing to customers’ needs, reduction in the cost of business processes, articulation of the effective HR policy, making preventative decisions on fraudulent transactions, optimization of business model. The findings may be useful to analysts.

Keywords: predictive analytics, prescriptive analytics


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ISSN 2311-9438 (Online)
ISSN 2073-8005 (Print)

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Vol. 24, Iss. 3
September 2019