Risk Analytics From Concept To Deployment
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Risk Analytics: From Concept To Deployment
Author: Edward Hon Khay Ng
language: en
Publisher: World Scientific
Release Date: 2021-10-04
This book is written to empower risk professionals to turn analytics and models into deployable solutions with minimal IT intervention. Corporations, especially financial institutions, must show evidence of having quantified credit, market and operational risks. They have databases but automating the process to translate data into risk parameters remains a desire.Modelling is done using software with output codes not readily processed by databases. With increasing acceptance of open-source languages, database vendors have seen the value of integrating modelling capabilities into their products. Nevertheless, deploying solutions to automate processes remains a challenge. While not comprehensive in dealing with all facets of risks, the author aims to develop risk professionals who will be able to do just that.
Risk Analytics
"Risk analytics has seen a spike in interest and demand with the quantification of risks and global regulatory requirements. Financial institutions like banks, in particular, have to show evidence of having measured credit, market and operational risks using numbers and models rather than qualitative judgments. These corporations already have massive databases but automating the process to translate data into risk parameters remains a desire in most of them. In the past, this was partly due to the lack of cost-effective tools to accomplish the task. Modeling was done using software with output codes not readily processed by databases. Data have to be manually extracted and run on the models with results input into the databases manually again. With the increasing acceptance of open source languages, database vendors have seen the value of integrating modeling capabilities into their products. That has made it possible to insert models developed using R, Python or other languages directly into SQL scripts used for database transactions. As R or Python are free, there is no additional cost involved. Nevertheless, deploying solutions developed to automate the process remains a challenge. While not comprehensive in dealing with all facets of risks, the author with his wealth of consulting experience, aims to contribute to the development of risk professionals who will be able to progress beyond theories and concepts to create solutions that can support planning and automated decision-making".
Risk Analytics
The 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth’s conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks. Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human and artificial intelligence interact with a data gathering and selection process that uses multiple sources to the delivery of guidelines to make decisions that include humans and machines. The RAS system is an integration of components, such as data architecture with diverse data, and a risk analytics process and modeling process to obtain knowledge and then determine actions through the new knowledge that was obtained. The use of data analytics is not only connected to risk modeling and its implementation, but also to the development of the actionable knowledge that can be represented by text in documents to define and share explicit knowledge and guidelines in the organization for strategy implementation. This book moves from a review of data to the concepts of a RAS. It reviews RAS system components required to support the creation of competitive advantage in organizations through risk analytics. Written for executives, analytics professionals, risk management professionals, strategy professionals, and postgraduate students, this book shows a way to implement the analytics process to develop a risk management practice that creates an adaptive competitive advantage under uncertainty.