scholarly journals Machine Learning and AI for Risk Management

2018 ◽  
pp. 33-50 ◽  
Author(s):  
Saqib Aziz ◽  
Michael Dowling
Keyword(s):  
Machine Learning ◽  
Risk Management

Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

2019 ◽  
Vol 22 (03) ◽  
pp. 1950021 ◽  
Author(s):  
Huei-Wen Teng ◽  
Michael Lee
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Credit Risk ◽  
Portfolio Management ◽  
Credit Card ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Credit Risk Management ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

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 Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 9 ◽  
Author(s):  
Li-Chiu Chang ◽  
Fi-John Chang ◽  
Shun-Nien Yang ◽  
I-Feng Kao ◽  
Ying-Yu Ku ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Flood Risk ◽  
Modular Design ◽  
Data Access ◽  
Flood Risk Management ◽  
Flood Inundation ◽  
Warning Systems ◽  
Integration Platform ◽  
Flood Disasters

Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving online forecast capability and flood risk management. The holistic framework of the IHIP includes five layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters. The IHIP provides real-time flood-related data, such as rainfall and multi-step-ahead regional flood inundation maps. The interface of Google Maps fused into the IHIP significantly removes the obstacles for users to access this system, helps communities in making better-informed decisions about the occurrence of floods, and alerts communities in advance. The IHIP has been implemented in the Tainan City of Taiwan as the study case. The modular design and adaptive structure of the IHIP could be applied with similar efforts to other cities of interest for assisting the authorities in flood risk management.

AI and Machine Learning for Risk Management

2018 ◽  
Author(s):  
Saqib Aziz ◽  
Michael M. Dowling
Keyword(s):  
Machine Learning ◽  
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.

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