scholarly journals The application of Artificial Intelligence in banks in the context of the three lines of defence model

2020 ◽  
Vol 94 (5/6) ◽  
pp. 219-230
Author(s):  
Alette Tammenga
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Second Line ◽  
New Techniques ◽  
Risk Framework ◽  
Fit For Purpose

The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques within banks is rising, especially for risk management purposes. The question arises whether the commonly used three lines of defence model is still fit for purpose given these new techniques, or if changes to the model are necessary. If AI and ML models are developed with involvement of second line functions, or for pure risk management purposes, independent oversight should be performed by a separate function. Other prerequisites to apply AI and ML in a controlled way are sound governance, a risk framework, an oversight function and policies and processes surrounding the use of AI and ML.

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.

AI in actuarial science – a review of recent advances – part 2

2020 ◽  
pp. 1-29 ◽  
Author(s):  
Ronald Richman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Actuarial Science ◽  
Interested Reader ◽  
Supplementary Appendix ◽  
New Techniques ◽  
Modern Approach ◽  
Mortality Modelling ◽  
Applications Of Ai

Abstract Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate, but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning”. This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning.

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>

AI in actuarial science – a review of recent advances – part 1

2020 ◽  
pp. 1-23 ◽  
Author(s):  
Ronald Richman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Actuarial Science ◽  
Interested Reader ◽  
Supplementary Appendix ◽  
New Techniques ◽  
Modern Approach ◽  
Mortality Modelling ◽  
Applications Of Ai

Abstract Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning.” This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning.

scholarly journals Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management

2021 ◽  
Vol 10 (3) ◽  
pp. 41-57
Author(s):  
Nenad Milojević ◽  
Srdjan Redzepagic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Global Financial Crisis ◽  
Positive Impact ◽  
Market Liquidity ◽  
Mitigation Measures ◽  
Successful Implementation ◽  
Banking Risk ◽  
The Impact

Abstract Artificial intelligence and machine learning have increasing influence on the financial sector, but also on economy as a whole. The impact of artificial intelligence and machine learning on banking risk management has become particularly interesting after the global financial crisis. The research focus is on artificial intelligence and machine learning potential for further banking risk management improvement. The paper seeks to explore the possibility for successful implementation yet taking into account challenges and problems which might occur as well as potential solutions. Artificial intelligence and machine learning have potential to support the mitigation measures for the contemporary global economic and financial challenges, including those caused by the COVID-19 crisis. The main focus in this paper is on credit risk management, but also on analysing artificial intelligence and machine learning application in other risk management areas. It is concluded that a measured and well-prepared further application of artificial intelligence, machine learning, deep learning and big data analytics can have further positive impact, especially on the following risk management areas: credit, market, liquidity, operational risk, and other related areas.

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