Financial

Scammers have siphoned away over \$1.03 trillion globally in the past year, emphasizing the urgent need for effective fraud detection systems. Fraud detection in telecommunication systems remains a significant challenge as fraudulent activities constantly evolve, resulting in financial losses and security risks. This paper proposes a fast and efficient machine-learning-based fraud detection system that analyzes phone call transcripts using Natural Language Processing (NLP) techniques.

The datasets include historical daily market information in the Chinese A-share stock markets, such as closing prices, opening prices, highest prices, lowest prices, trading volumes in terms of value, and trading volumes in terms of quantity, from 2015 to 2021.

This is the dataset of the paper Privacy-Preserving Federal Embedding Learning for Localized Retrieval-Augmented Generation.

Directed Acyclic Graph (DAG) blockchain applications represent an evolution from traditional linear blockchain solutions, addressing their inherent scalability issues. The analysis of the transactions fee market in these applications proves particularly insightful, given their demonstrated ability to manage very high transaction throughputs, and the crucial role transaction fees play in blockchain sustainability, efficiency and security, as a mechanism to incentivize validators and regulate network congestion.

This dataset integrates textual, financial, and macroeconomic indicators to support research on bank failure prediction and financial distress forecasting in Vietnam. It includes financial news from the BKAI News Corpus Dataset (2009–2023) and financial crisis data from "A Dataset for the Vietnamese Banking System (2002–2021)" (Tu Le et al., 2022), covering crisis-related events such as restructuring, special control, mergers, and acquisitions.

The growing intricacy of investment decisions in unpredictable economic conditions underscores the importance of comprehending the fundamental psychological factors that shape investor behavior and preferences. For this purpose, this research illustrates the impact of personal saving orientation in elucidating the impact of flow experience and social support, desire for learning on investment decisions. The research assesses a proposed model grounded in the Theory of Planned Behavior (TPB) by collecting questionnaire data from 516 investors using the PLS-SEM technique...

The growing intricacy of investment decisions in unpredictable economic conditions underscores the importance of comprehending the fundamental psychological factors that shape investor behavior and preferences. For this purpose, this research illustrates the impact of personal saving orientation in elucidating the impact of flow experience and social support, desire for learning on investment decisions. The research assesses a proposed model grounded in the Theory of Planned Behavior (TPB) by collecting questionnaire data from 516 investors using the PLS-SEM technique...

These are the daily closing prices of four stock indices including Shanghai Securities Composite Index (SSEC) and the Shenzhen Securities Component Index (SZI) from China, the Straits Times Index (STI) from Singapore, and the Standard & Poor 500 Index (SPX) from the United States. The SSEC data is from December 19,1990 to May 25, 2023. The data of SZI is from April 3, 1991 to May 25, 2023. The STI data is from December 3, 1990, to May 25, 2023, and the data of SPX is from December 3, 1990 to May 25, 2023.

These are the daily closing prices of four stock indices including Shanghai Securities Composite Index (SSEC) and the Shenzhen Securities Component Index (SZI) from China, the Straits Times Index (STI) from Singapore, and the Standard & Poor 500 Index (SPX) from the United States. The SSEC data is from December 19,1990 to May 25, 2023. The data of SZI is from April 3, 1991 to May 25, 2023. The STI data is from December 3, 1990, to May 25, 2023, and the data of SPX is from December 3, 1990 to May 25, 2023.

• The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
• Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.
• The dataset consists of 10 numerical and 8 categorical attributes.