Information Processing Steps and New Capabilities in the Enterprise Risk Management Setting

Tankiso Moloi () and Tshilidzi Marwala ()
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Tankiso Moloi: University of Johannesburg, Johannesburg Business School
Tshilidzi Marwala: United Nations University

Chapter Chapter 6 in Enterprise Risk Management in the Fourth Industrial Revolution, 2023, pp 59-66 from Springer

Abstract: Abstract It is proposed that the risk management field relies on information. For instance, to be in a position to identify, assess, and treat the risk, as well as monitor and report on the risk, one would need to have information about that particular risk. In this treatise, we propose three types of complexity. These include those caused by the architectural nature of the organisation, those that result from internal dynamics of the organisation, and those emanating from external shocks such as political, economic, and natural factors. In the literature, we identify nine generic steps that are involved in information processing. These include acquiring information, inputting information, validating information, manipulating information, storing information, outputing information, communicating information, retrieving information, and disposing information. The nine are thought to be inconsistent with the enterprise as envisioned in the fourth industrial revolution. Thus, we propose seven generic steps involved in the generic information processing in the enterprise risk management setting. They include acquisition of information from inside and outside the enterprise, preparation of information, exploration of information, categorisation of information, determination of the meaning of information per category, integration of categorised information, and utilisation of information to make predictions or to gain foresight. For an enterprise to be in a position to acquire the information from inside and outside the enterprise, prepare this information, explore, and determine its meaning, categorise the information, determine the meaning of information per category, integrate the categorised information, and then utilise the information to make predictions or gain foresight, it is argued that it must first develop capabilities to leverage technologies such as supervised, unsupervised, semi-supervised, and reinforcement machine learning, natural language processing, robotic process automation, augmented reality/simulation, and big data analytics.

Date: 2023
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DOI: 10.1007/978-981-99-6307-2_6

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