This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

The untapped potential of wave energy offers another alternative to diversifying renewable energy sources and addressing climate change by reducing CO2 emissions. However, development costs to mature the technology remain significant hurdles to adoption at scale and the technology often must...

This research article presents a comparative analysis between logistic regression as a traditional method, artificial neural networks (ANNs), and decision tree as machine learning techniques for predicting credit risk. It meticulously examines and evaluates these three methods, elucidating their...

Greenwashing is the misleading use of environmental claims to market non-eco-friendly products, a growing concern as environmentally conscious consumers seek eco-friendly options. This study aims to identify factors that allow the detection of "greenwashing" practices in Colombian companies...

Despite the cross-border availability of almost all goods and services online due to global Internet access, the domestic origin of sellers remains significant. This study examines the preferences for domestic versus cross-border goods and services in online purchases in the EU online market...

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer...

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success...

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data...

Feature selection identifies the relevant features and removes the irrelevant and redundant ones, intending to obtain the best-performing feature subset. This paper proposes a new feedback feature selection system with a reinforcement learning-based method to identify the best feature subset....

Mortgage default prediction is always on the table for financial institutions. Banks are interested in provision planning, while regulators monitor systemic risk, which this sector may possess. This research is focused on predicting defaults on a one-year horizon using data from the Ukrainian...

A constant in the business world is the frequent movement of customers joining or abandoning companies’ services and products. The customer is one of the company’s most important assets. Reducing the customer abandonment rate has become a matter of survival and, at the same time, the most...

A tourist recommendation system has been implemented based on python, Django framework and MySQL database. Firstly, crawler technology is used to crawl the ratings(reviews of customer experiences) information of TripAdvisor, and then the crawled ratings data is stored in MySQL. In the system,...

Japan's feed self-sufficiency rate is the lowest among developed countries. The Japanese government has introduced a feed rice subsidy, a flagship agricultural policy, to address this issue. However, unlike expectations, the perceived benefit of growing feed rice is low, and about 60% of farmers...

Analyzing financial accounting transactions is essential for gaining valuable hidden insights, optimizing performance, reducing expenses by identifying more efficient methods of conducting business operations, and improving profitability and bottom line. This study uses exploratory data analysis...

In pharmaceutical R&D, decision makers face a large portion of uncertainty in portfolio decisions. Existing optimization tools are mostly based on expected values, ignoring uncertainty, and most often deliver a single, "best" selection of projects. A decision tree approach can aid decision...

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production...

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine...

This article models the purchasing intention of young Indian consumers for branded smartphones by analysing their individual preferences for the different features of the smartphone. This article selects thirteen features for analysis, and the respondents are asked to rate, which are further...

Sports analytics has gained rapid popularity in recent years and will likely continue to evolve. In this study, we construct predictive analytics models to forecast the NFL games outcomes in a season using decision trees and logistics regression. Several variables are used as predictors...