scholarly journals Application of Artificial Intelligence-Based Risk Management in Banking

2021 ◽  
Vol 12 (2) ◽  
pp. 01-09
Author(s):  
Mutiara Dewi Permatasari ◽  
Nisa Aurelya Salsabyla ◽  
Nurfitri Nurfitri
Keyword(s):  
Artificial Intelligence ◽  
Risk Management

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.

TRENDS IN THE DEVELOPMENT OF RISK MANAGEMENT

2020 ◽  
Vol 18 (Suppl.1) ◽  
pp. 417-421
Author(s):  
P. Biolcheva
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Industry 4.0 ◽  
Business Organizations ◽  
Main Subject ◽  
Scientific Publications

Awareness of the need for effective risk management is gaining ground, both among business organizations and in scientific publications in the field. The prospects for development in risk management must be tailored to the growing and transformative needs of Industry 4.0. The paper focuses on different directions, priorities of the Federation of European Riskmanagement Associations (Ferma). The main subject of research is placed on risk management through artificial intelligence. Both the risks of its introduction and the benefits of it have been identified.

Applying Artificial Intelligence to Financial Investing

2018 ◽  
pp. 1-14
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

scholarly journals Artificial Intelligence Based Commercial Risk Management Framework for SMEs

2019 ◽  
Vol 11 (16) ◽  
pp. 4501
Author(s):  
Gerda Žigienė ◽  
Egidijus Rybakovas ◽  
Robertas Alzbutas
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Academic Research ◽  
Building Blocks ◽  
Learning Technologies ◽  
Potential Contribution ◽  
Risk Management Framework ◽  
Development Goals ◽  
Risk Assessment And Management

Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

scholarly journals Blockchain in Healthcare: Insights on COVID-19

2020 ◽  
Vol 17 (19) ◽  
pp. 7167 ◽  
Author(s):  
Antonio Fusco ◽  
Grazia Dicuonzo ◽  
Vittorio Dell’Atti ◽  
Marco Tatullo
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Clinical Practice ◽  
Swot Analysis ◽  
Healthcare Management ◽  
Evidence Based ◽  
Clinical Workflow ◽  
Strategic Role ◽  
Digital Healthcare ◽  
Artificial Intelligence Systems

The SARS-CoV2 pandemic has impacted risk management globally. Blockchain has been increasingly applied to healthcare management, as a strategic tool to strengthen operative protocols and to create the proper basis for an efficient and effective evidence-based decisional process. We aim to validate blockchain in healthcare, and to suggest a trace-route for a COVID19-safe clinical practice. The use of blockchain in combination with artificial intelligence systems allows the creation of a generalizable predictive system that could contribute to the containment of pandemic risk on national territory. A SWOT analysis of the adoption of a blockchain-based prediction model in healthcare and SARS-CoV-2 infection has been carried out to underline opportunities and limits to its adoption. Blockchain could play a strategic role in future digital healthcare: specifically, it may work to improve COVID19-safe clinical practice. The main concepts, and particularly those related to clinical workflow, obtainable from different blockchain-based models have been reported here and critically discussed.

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 Explainable Machine Learning in Credit Risk Management

2020 ◽  
Author(s):  
Niklas Bussmann ◽  
Paolo Giudici ◽  
Dimitri Marinelli ◽  
Jochen Papenbrock
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Credit Score ◽  
Correlation Networks ◽  
Explainable Artificial Intelligence ◽  
Shapley Values ◽  
Financial Characteristics ◽  
The Empirical Analysis

Abstract The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.

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