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.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals Combining Regression and Clustering for Financial Analysis

2020 ◽  
Vol 2 (1) ◽  
pp. 109-118
Author(s):  
Andreea Ioana Chiriac
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Business Processes ◽  
Financial Analysis ◽  
Two Dimensions ◽  
Machine Learning Algorithms ◽  
Financial Ratio ◽  
Research Perspective ◽  
Systems Learning

Abstract Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead. Though it might seem like we’ve come a long way in the last ten years, which is true from a research perspective, the adoption of AI among corporations is still relatively low. Over time it became possible to automate more tasks and business processes than ever before. The benefit of using artificial intelligence is that does not require to program every step of the process, predicting at each step what could happen and how to resolve it. The algorithms decide for themselves in each case how the problems should be solved, based on the data that is used. I apply Python language to create a synthetic feature vector that allows visualizations in two dimensions for EDIBTA financial ratio. I use Mean-Square Error in order to evaluate the success, having the optimal parameters. In this section, I also mentioned about the purpose, goals, and applications of cluster analysis. I indicated about the basics of cluster analysis and how to do it and also did a demonstration on how to use K-Means.

scholarly journals The place of artificial intelligence in the risk management process

2021 ◽  
Vol 120 ◽  
pp. 02013
Author(s):  
Petya Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Risk Identification ◽  
Management Process ◽  
Whole Process ◽  
Risk Management Process ◽  
Problematic Issue ◽  
The Individual

In recent years, there has been increasing talk of the rapid entry of artificial intelligence into risk management. All the benefits it would bring over the whole process are often commented on: real-time results, processing large amounts of data, more complete risk identification, more accurate risk assessment, etc. There are also negative moods that make various experts feel threatened by their need to be replaced by artificial intelligence. Another problematic issue that arises is related to the transparency of algorithms and the increase in cyber risks [6]. This material aims to identify the individual elements at the stages of risk management in which artificial intelligence (AI) can and should be applied alone, in combination with expert opinion or not. Here it is shown that because of the use of AI the efficiency of the whole process is significantly increased, first of all by conducting in-depth analyses, and the decisions are made by the risk management experts. This proves its usefulness and increases the confidence of experts in it.

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 Automation of business processes of entrepreneurial activity in the field of real estate based on artificial intelligence technologies

2021 ◽  
Vol 18 (5) ◽  
pp. 150-159
Author(s):  
A. N. Asaul ◽  
◽  
G. F. Shcherbina ◽  
M. A. Asaul ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Business Processes ◽  
Learning Technologies ◽  
Entrepreneurial Activity ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Entrepreneurial Activities

The article refines the concept of «business process», the essence of business processes` automation in entrepreneurial activities is considered through the use of artificial intelligence and machine learning technologies for IT integration in the real estate sector. Based on the market analysis, the state of development of artificial intelligence and machine learning in Russia, its significance and prospects for implementation in business activities in the real estate sector are studied.

scholarly journals Risk management of logistic department of electricity company

INOVASI ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 98
Author(s):  
Erdiyan Krisnadi Hasda ◽  
Erman Sumirat
Keyword(s):  
Risk Management ◽  
Business Processes ◽  
Management Process ◽  
Electricity Transmission ◽  
Management Standard ◽  
Risk Management Process ◽  
Iso 31000 ◽  
Work Accident ◽  
Hierarchy Process ◽  
Standard Framework

This study is conducted to carry out the risk management process in the logistics department of the electricity company unit, which has the main duties in managing electricity transmission assets, controlling investment and logistics transmission, and maintaining transmission assets. The risk management process in this study was prepared as a step in shaping the risk profile of business processes in the logistics field to avoid the failure of business processes that resulted in unavailbility of logistics material, which could impact the electricity transmission. This study uses the AS/NZS ISO 31000:2009 Risk Management Standard framework. Calculation of risk priorities is using Analytical Hierarchy Process, based on a questionnaire to experts in the field of company logistics. From the calculation using AHP, Work Accident (HR2) has been identified as the most vulnerable risk among others risk factors.

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>

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 IT RISK IDENTIFICATION PROCESS ANALYSIS / IT RIZIKOS IDENTIFIKAVIMO PROCESO ANALIZĖ

2013 ◽  
Vol 5 (1) ◽  
pp. 46-52
Author(s):  
Rasma Janeliūnienė ◽  
Vida Davidavičienė
Keyword(s):  
Risk Management ◽  
Business Processes ◽  
Process Analysis ◽  
Risk Identification ◽  
Business Success ◽  
Success Factor ◽  
It Risk Management ◽  
Risk Management Process ◽  
Process Risk ◽  
It Risk

Business processes and business success that depends on information technology (IT) is now closely associated with IT risks, which is influenced by growing IT risk management and control needs. It is vitally important to identify, analyse and reduce systemic risk in order to avoid undesirable consequences, such as information loss, data leaks or damage. A critical success factor in this situation is the systematic and continuous IT risk management. This paper aims to analyse one part of the IT risk management process –risk identification. The article invoked the methods of literature analysis, synthesis, comparison, and generalization.Article in Lithuanian Santrauka Išaugusi verslo procesų, kartu ir verslo sėkmės, priklausomybė nuo informacinių technologijų (IT) šiuo metu yra glaudžiai susijusi su IT rizika. Tai daro įtaką augančiam IT rizikos valdymo ir kontrolės poreikiui. Nepaneigtina tai, kad identifikuota, išanalizuota ir sumažinta sistemos rizika leidžia išvengti nepageidaujamų pasekmių, tokių kaip informacijos praradimas, nutekėjimas ar duomenų sugadinimas. Pagrindinis sėkmės veiksnys siekiant užtikrinti organizacijos sėkmę valdant IT yra sistemingas ir tęstinis IT rizikos valdymas. Straipsnyje keliamas tikslas išanalizuoti vieną iš IT rizikos valdymo proceso etapų – rizikų identifikavimą. Straipsnyje pasitelkiami tokie metodai, kaip mokslinės literatūros analizė, sisteminimas, apibendrinimas.

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