Cognitive Computing for Anticipatory Risk Analytics in Intelligence, Surveillance, & Reconnaissance (ISR): Model Risk Management in Artificial Intelligence & Machine Learning (Presentation Slides)

2018 ◽  
Author(s):  
Yogesh Malhotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Cognitive Computing ◽  
Model Risk

Tie me to the mast: artificial intelligence & reputation risk management

2018 ◽  
Vol 39 (1) ◽  
pp. 61-64 ◽  
Author(s):  
Peter Buell Hirsch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Thought Experiment ◽  
Early Stage ◽  
Stress Test ◽  
Risky Behaviors ◽  
Content Type ◽  
Early Signs ◽  
Reputation Risk

Purpose Artificial intelligence and machine learning have spread rapidly across every aspect of business and social activity. The purpose of this paper is to examine how this rapidly growing field of analytics might be put to use in the area of reputation risk management. Design/methodology/approach The approach taken was to examine in detail the primary and emerging applications of artificial intelligence to determine how they could be applied to preventing and mitigating reputation risk by using machine learning to identify early signs of behaviors that could lead to reputation damage. Findings This review confirmed that there were at least two areas in which artificial intelligence could be applied to reputation risk management – the use of machine learning to analyze employee emails in real time to detect early signs of aberrant behavior and the use of algorithmic game theory to stress test business decisions to determine whether they contained perverse incentives leading to potential fraud. Research limitations/implications Because of the fact that this viewpoint is by its nature a thought experiment, the authors have not yet tested the practicality or feasibility of the uses of artificial intelligence it describes. Practical implications Should the concepts described be viable in real-world application, they would create extraordinarily powerful tools for companies to identify risky behaviors in development long before they had run far enough to create major reputation risk. Social implications By identifying risky behaviors at an early stage and preventing them from turning into reputation risks, the methods described could help restore and maintain trust in the relationship between companies and their stakeholders. Originality/value To the best of the author’s knowledge, artificial intelligence has never been described as a potential tool in reputation risk management.

scholarly journals Holistic Security and Risk Intelligence: Are Current Risk Management Methods Leading to Breach?

2021 ◽  
Author(s):  
Chris Middleton ◽  
Harsha Kalutarage ◽  
Omar Al-kadri ◽  
Hatem Ahriz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Systematic Risk ◽  
Second Order ◽  
Management Problems ◽  
Risk Intelligence ◽  
Current Risk

How could we better prepare industry and governments against holistic, hybrid, or second-order attacks? <div>In this article we discuss the importance of addressing systemic and systematic risk management problems to provide holistic risk management and direct advances in technical security, utilising machine learning and artificial intelligence.</div>

scholarly journals USAGE OF MACHINE LEARNING IN THE PROCESS OF RISK-MANAGEMENT

2021 ◽  
Vol 9 (3) ◽  
pp. 126-130
Author(s):  
Sof'ya Svistunova ◽  
Sergey Muzalev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Business Processes ◽  
Corporate Structure ◽  
Learning Mechanisms ◽  
Economic Entity ◽  
Innovative Methods ◽  
Risk Management Process ◽  
Operational Activities

Background. Currently, artificial intelligence (AI) and machine learning are frequently implemented into the corporate structure and are aimed to transform the risk management system. Not only AI is useful for detection the interconnections between business processes, but also allows to accurately predict financial indicators and the reasons for possible deviations from standard values. Thus, the implementations of artificial intelligence and machine learning mechanisms makes it possible to increase the efficiency of operational activities and detect hidden risks. Method. The article discusses the main types of risks, identidication and minimization of which can be carried out using machine learning and also reveals key difficulties that arise while introducing innovative mechanisms into the structure of risk-management. The scientific novelty of the work lies in the relevance of using artificial intelligence mechanisms while minimizing the risks of an economic entity, as well as in identifying the main incentives for the efficient usage of machine learning in risk management. Result. As a result, the potential of introducing innovative methods into the structure of risk management to improve the efficiency of operating activities was revealed. Conclusion. In the process of the methodological study, the features of the application of machine learning methods in the risk management process were identified, moreover the article main limitations and possibilities of using artificial intelligence in order to minimize risks were revealed.

Artificial Intelligence and Investing

2011 ◽  
pp. 237-240 ◽  
Author(s):  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Asset Valuation ◽  
Stock Valuation ◽  
Knowledge Based ◽  
Processing Techniques

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.

scholarly journals Digital Transformation in the Concept of Internal Banking Risk Management

2020 ◽  
pp. 45-50
Author(s):  
D. V. Pasinitsky
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Risk Management ◽  
Digital Technologies ◽  
Digital Transformation ◽  
Targeted Analysis ◽  
Banking Risk ◽  
Learning Data

The article is devoted to a targeted analysis of promising displacements in the guiding ideas of managing internal banking risks. Based on the study, the author proposes to intensify the introduction of digital technologies in banking practice based on: artificial intelligence, machine learning, data mining.

scholarly journals Holistic Security and Risk Intelligence: Are Current Risk Management Methods Leading to Breach?

2021 ◽  
Author(s):  
Chris Middleton ◽  
Harsha Kalutarage ◽  
Omar Al-kadri ◽  
Hatem Ahriz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Systematic Risk ◽  
Second Order ◽  
Management Problems ◽  
Risk Intelligence ◽  
Current Risk

How could we better prepare industry and governments against holistic, hybrid, or second-order attacks? <div>In this article we discuss the importance of addressing systemic and systematic risk management problems to provide holistic risk management and direct advances in technical security, utilising machine learning and artificial intelligence.</div>

Cognitive Computing For Sustainable Agriculture

2019 ◽  
Vol 8 (3) ◽  
pp. 32-34
Author(s):  
T. Manjula ◽  
T. Sudha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sustainable Agriculture ◽  
Food Supply ◽  
Green Revolution ◽  
Modern Society ◽  
Cognitive Computing ◽  
Cognitive Computation ◽  
Ancient Times ◽  
Modern Technologies

Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase the per meter yield, So many techniques have been for seen in agriculture in terms of usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. Now the use of modern technologies such as artificial intelligence and cognitive computation is going to bring a new big revolution for sustainable agriculture. The present paper focuses on the problems faced by the modern society in agriculture and how the cognitive computation provides an ultimate solution to the problems. We also discuss some illustrations for the usage of cognitive technologies and machine learning in the field of agriculture.

The emergence of artificial intelligence in cardiology: current and future applications

2021 ◽  
Vol 17 ◽  
Author(s):  
Prashanth Kulkarni ◽  
Manjappa Mahadevappa ◽  
Srikar Chilakamarri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Patient Care ◽  
Current Evidence ◽  
Cognitive Computing ◽  
Cardiovascular Medicine ◽  
Artificial Intelligence Technology ◽  
Application Prospects

: Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.

scholarly journals Artificial intelligence - The promise for an agricultural revolution in new era

2020 ◽  
Vol 15 (4) ◽  
pp. 435-437
Author(s):  
Reshma J. Murugan ◽  
B. N. Bindhya ◽  
G. S. Sreedaya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Weather Conditions ◽  
Disruptive Technology ◽  
Cognitive Computing ◽  
Related Data ◽  
Agricultural Revolution ◽  
Adverse Weather ◽  
Optimal Mix

Agriculture is slowly becoming digital. The adoption of Artificial Intelligence (AI) and Machine Learning (ML) both in terms of agricultural products and in-field farming techniques are increasing. Artificial Intelligence in agriculture is emerging in three major areas, namely agricultural robotics, soil and crop monitoring and predictive analytics. The use of sensors and soil sampling techniques are increasing day by day which helps in gathering of data. In turn, this data is stored in farm management system which is better processed and analysed. Thus, the data available along with other related data paves a way to successfully deploy AI in agriculture. AI in agriculture is emergingin cognitive computing and it has all the scope to become the most disruptive technology in agriculture services as it is able to understand, learn and respond to different situations (based on learning) to increase efficiency. The areas where the use of cognitive solutions can benefit agriculture are growth driven by IOT, image-based insight generation, identification of optimal mix for agronomic products, health monitoring of crops and automation techniques in irrigation and enabling farmers. In addition, the drone based solutions have significant impact in terms of productivity gains, coping with adverse weather conditions, yield management and precision farming.The emergence of new age technologies like Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are creating an ecosystem for smart farming. Fusion of all this technology is enabling farmers achieve higher average yield and better price control.

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