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 Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

An Interview with Dr. Michael Zeller, Winner of ACM SIGKDD 2020 Service Award

2021 ◽  
Vol 22 (2) ◽  
pp. 6-7
Author(s):  
Michael Zeller
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Investment Company ◽  
Service Award ◽  
Virtual Conference ◽  
Learning Data

Michael Zeller, Ph.D. is the recipient of the 2020 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Zeller was honored for his years of service and many accomplishments as the secretary and treasurer for ACM SIGKDD, the organizing body of the annual KDD conference. Zeller is also head of AI strategy and solutions at Temasek, a global investment company seeking to make a difference always with tomorrow in mind. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 1999, what he learned from the first-ever virtual conference, his work at Temasek, and what excites him about the future of machine learning, data science and artificial intelligence.

scholarly journals SIAPKAH PARA DOSEN AKUNTANSI DI PERGURUAN TINGGI VOKASI DALAM MENYAMBUT DATANGNYA REVOLUSI INDUSTRI 4.0 ?

2020 ◽  
Vol 18 (3) ◽  
pp. 465
Author(s):  
Diana Rino Putri ◽  
Nurafni Eltivia ◽  
Ari Kamayanti ◽  
Jaswadi Jaswadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Distributed Processing ◽  
Current Level ◽  
Future Prospects ◽  
Vocational College ◽  
True Meaning ◽  
Learning Data

In developing countries such as Indonesia, a large number of academics are unfamiliar with the true meaning of terms such as Big Data, Exabyte, Petabyte, Brontobyte, Artificial Intelligence, Machine Learning, Data Mining, Data Warehousing, Distributed Processing, Grid Computing and Cloud Computing. In this paper, we report the results of a survey carried out to ascertain the current level of awareness regarding Big Data among academics in Vocational College. Respondents to a questionnaire formulated for this purpose. Results of the survey seem to indicate that there is a need for multi-faceted efforts aimed at creating awareness regarding Big Data, the related technologies, challenges and future prospects.

scholarly journals Crossing Point of Artificial Intelligence in Cybersecurity

2015 ◽  
Vol 2 (3) ◽  
pp. 121-128
Author(s):  
Praveen Kumar Donepudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Cyber Threats ◽  
Crossing Point ◽  
Insight Into ◽  
Distinct Approach ◽  
Learning Data ◽  
Applications Of Ai

There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.  

An Interview with Dr. Shipeng Yu, Winner of ACM SIGKDD 2021 Service Award

2021 ◽  
Vol 23 (2) ◽  
pp. 1-2
Author(s):  
Shipeng Yu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Professional Network ◽  
Service Award ◽  
Years Of Service ◽  
Learning Data

Shipeng Yu, Ph.D. is the recipient of the 2021 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Yu was honored for his years of service and many accomplishments as general chair of KDD 2017 and currently as sponsorship director for SIGKDD. Dr. Yu is Director of AI Engineering, Head of the Growth AI team at LinkedIn, the world's largest professional network. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 2006, the benefits and drawbacks of virtual conferences, his work at LinkedIn, and KDD's place in the field of machine learning, data science and artificial intelligence.

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.

Das Zeitalter der Künstlichen Intelligenz als Herausforderung für die Parlamente

2021 ◽  
Vol 52 (1) ◽  
pp. 159-181
Author(s):  
Arne Pilniok
Keyword(s):  
Artificial Intelligence ◽  
Digital Technologies ◽  
Digital Transformation ◽  
Processes Of Change ◽  
Parliamentary Democracy ◽  
Democratic Representation ◽  
Government Administration ◽  
Technological Developments ◽  
Different Dimensions ◽  
The Government

The digital transformation is permanently changing the government, administration, and society . This process is being intensified by the much-discussed technologies of artificial intelligence, and poses a variety of challenges for parliaments and indirectly for parliamen­tary studies . Their different dimensions have not been discussed comprehensively so far, although the technological developments affect all parliamentary functions and their prem­ises . This article systematizes and structures the various effects of the age of artificial intel­ligence on parliamentary democracy . Namely, the conditions of democratic representation change, the innovation-friendly regulation of digital technologies becomes a parliamentary task, parliamentary control has to be adjusted to the use of algorithms and artificial intelli­gence in government and administration, and possibly, the epistemological and organiza­tional structures of parliamentary work might have to be adapted . This provides starting points for future detailed analyses to adequately capture these processes of change and to accompany them from different disciplinary perspectives .

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Big Data Models and the Public Sector

Web Services ◽  
2019 ◽  
pp. 105-126
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

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