The impact of machine learning and big data on credit markets

2021 ◽  
Author(s):  
Peter Eccles ◽  
Paul Grout ◽  
Paolo Siciliani ◽  
Anna Zalewska
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Credit Markets ◽  
The Impact

scholarly journals The Digital Transformation and Disruption in Business Models of the Banks under the Impact of FinTech and BigTech

2020 ◽  
Vol 14 (1) ◽  
pp. 294-305
Author(s):  
Virginia Mărăcine ◽  
Oona Voican ◽  
Emil Scarlat
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Models ◽  
Business Processes ◽  
Digital Transformation ◽  
Operating Costs ◽  
Continuous Increase ◽  
The Impact

AbstractThe explosive development of artificial intelligence, machine learning and big data methods in the last 10 years has been felt in the financial-banking field which has subjected to profound changes aimed at determining an unprecedented increase in the efficiency and profitability of the businesses they carry out. The tendencies of applying the concepts coming from AI, together with the continuous increase of the volume, complexity and variety of the data that the banks collect, store and process have acquired the generic names of FinTech, respectively BigTech. Five main areas exist where Fintechs and Bigtechs can provide improvements in business models for the banks: introducing specialized platforms, covering neglected customer segments, improving customer selection, reduction of the operating costs of the banks, and optimization of the business processes of the banks. We will present some of these improvements, and then we will show how the business models of the banks dramatically transform under the influence of these changes.

scholarly journals THE IMPLEMENTATION OF BIG DATA ANALYSIS IN REGULATING ONLINE SHORT-TERM RENTAL BUSINESS: A CASE OF AIRBNB IN BEIJING

2019 ◽  
Vol IV-4/W9 ◽  
pp. 79-86
Author(s):  
J. Li ◽  
F. Biljecki
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Housing Prices ◽  
Big Data Analysis ◽  
Price Sensitivity ◽  
Short Term ◽  
New Business ◽  
The Impact ◽  
The Relationship

Abstract. With the fast expansion and controversial impacts of short-term rental platforms such as Airbnb, many cities have called for regulating this new business model. This research aims to establish an approach to understand the impact of Airbnb (and similar services) through big data analysis and provide insights potentially useful for its regulation. The paper reveals how Airbnb is influencing Beijing’s neighbourhood housing prices through machine learning and GIS. Machine learning models are developed to analyse the relationship between Airbnb activities in a neighbourhood and prevailing housing prices. The model of the best fit is then used to analyse the neighbourhood price sensitivity in view of increasing Airbnb activities. The results show that the sensitivity is variable: there are neighbourhoods that are likely to be more price sensitive to Airbnb activities, but also neighbourhoods that are likely to be price robust. Finally, the paper gives policy recommendations for regulating short-term rental businesses based on neighbourhood’s price sensitivity.

The impact of machine learning on processing big data in high-performance computing

2021 ◽  
pp. 344-353
Author(s):  
Rohit ◽  
B. Gupta ◽  
K. K. Gola
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
The Impact ◽  
Performance Computing

Big Data Technology To Improve The Reliability Of Solutions When Choosing A Contractor

2019 ◽  
pp. 65-70
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Modeling And Analysis ◽  
Current Economic Situation ◽  
Contract Failure ◽  
Contract Implementation ◽  
The Impact

Considering current economic situation, the level of competition among different companies is great. In order to gain a higher position in the ratings, to attract more new customers, to predict the demand for products, and finally to protect themselves from wrong decisions, companies are increasingly turning to big data analytics. In the sphere of construction an opportunity to foresee the probability of contract implementation before its conclusion is always relevant. The higher the probability, the more attractive the contractor and lower the risks of the customer. Developing the topic of applicability of machine learning methods to the problem of determining the probability of successful completion of the contract, the authors are experimenting with a set of analyzed indicators assessing the impact of each of them on the decision on the possibility of contract failure. The article considers in detail the stages of data preparation for modeling, direct modeling and analysis of the results obtained. The authors tested the adequacy of the models on actual data and set the metrics by which it is possible to customize and improve the models for the needs of a particular organization. The prognostic models with a predictive power, based on machine learning algorithms, such as logistic regression, decision tree, random forest, developed by the authors, have the potential for practical use in construction organizations at the stage of contract conclusion.

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

scholarly journals The Impact of Big Data Processing Framework for Artificial Intelligence within Corporate Marketing Communication

2018 ◽  
Vol 7 (4.34) ◽  
pp. 384
Author(s):  
Muhamad Fazil Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Positive Impact ◽  
Marketing Communication ◽  
Short Term ◽  
The Impact ◽  
Processing Framework

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.   

scholarly journals Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 495 ◽  
Author(s):  
Jesús López Belmonte ◽  
Adrián Segura-Robles ◽  
Antonio-José Moreno-Guerrero ◽  
María Elena Parra-González
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
English Language ◽  
Web Of Science ◽  
Large Data ◽  
Explanatory Models ◽  
Scientific Production ◽  
Scientific Publications ◽  
The One ◽  
The Impact

Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning.

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