Concerning Neural Networks Introduction in Possessory Risk Management Systems

Abstract

The increasing rate of implementation of machine learning and artificial intelligence is currently a key component of development of organization’s possessory risk management systems. Owners and managers of major, medium, and small entities strive to have improved and more efficient analytical mechanisms to improve management systems as well as systems for collection, structuring, and analysis of the increasing volumes of data in statutory regulation and of other unstructured data for compliance with the requirements of legislation and financial risk management. It is also obvious that the use of neural networks both in core business processes and in organization management systems has become an important means of economic competition. In terms of the innovative advantage created using machine learning in possessory risk management systems, two preliminary conclusions can be made. First, an important competitive advantage is the fact that machine learning methods enable analysis of large data volumes providing a high level of detail and the depth of predictive analysis, which makes it possible for possessors to obtain additional opportunities for analysis in risk management and compliance with statutory regulation in finance. This article is devoted to the analysis of such opportunities and advantages as well as trends of neural networks implementation in entity’s possessory risk management systems.