Journal of Business Data Science Research

Application of Artificial Intelligence in Financial Services

Mohsen Hajian, Amir Hajian — Tue, 05 Mar 2024 00:00:00 +0330

In the fourth industrial revolution; Technologies and computer science are progressing and are present in all sectors of different industries. Artificial intelligence in financial services reduces costs and increases the quality of services; Which is why the use of artificial intelligence is common in many well-known financial companies of the world. Artificial intelligence in financial services has four forms of application. The first one is Artificial Neural Networks, which have underlying rules and act and makes decisions like the human brain. The second case is Expert System, which makes the best decision in financial services based on past experiences and events. The third case is the Genetic Algorithm, which is a simple and practical tool by which the ma-chine can simulate the mechanism of natural selection. The fourth case is fuzzy logic, which reduces errors and makes reliable decisions in financial services due to the high accuracy of the output.

Categorizing Online Advertisements with Machine Learning Models

Tue, 05 Mar 2024 00:00:00 +0330

Advertising segmentation, also known as audience or market segmentation, constitutes a critical facet of marketing. It involves subdividing a large target population into more manageable groups or segments. By employing advertising grouping, businesses can tailor content and keywords to each group of advertisements, thereby enhancing the effectiveness of their marketing efforts. This project explores two machine learning models for advertising grouping, achieving an impressive efficiency rate of over 90%. The study encompasses a dataset of 2000 online advertisements divided into five advertising groups, with 1600 data points used for training and 400 for testing. The results indicate that the Support Vector Classifier (SVC) and Linear Regression (LR) models perform well in the categorization of advertisements.

Machine Learning based Analysis and Effective Visualization of Mutual Funds Through CUSUM and Clustering

Aditya Maheshwari , Vaidehi Shah, Aakansha Sawhney, Eugene Pinsky — Tue, 05 Mar 2024 00:00:00 +0330

This research paper aims to present an extensive analysis of Large-cap class-A mutual funds spanning a period of 25 years. Using this historical data, the study presents the readers with the observed patterns and trends in these mutual funds. The data has been sourced from two major data repositories for mutual fund and finance data - WRDS (Wharton Research Data Services) and YF (Yahoo Finance). The study uses statistical methodologies like Sharpe Ratio and Volatility and analytical methodologies like CUSUM and Clustering. Along with this quantitative analysis, the paper also encompasses qualitative data like Assets Under Management, Turnover Ratio, and Management Information for all the funds used. This helps gain insights into the influence of these factors on the performance of the mutual funds. This paper mainly discusses how all the aforementioned factors influence the mutual fund trajectories with the help of effective visualizations and machine learning-based analysis over 25 years, hence developing an efficient pipeline.

Artificial Intelligence and the Great Transformation in the Tourism Industry Revolutionizing Travel Services

Abodol Hojat Moghaddasnian — Tue, 05 Mar 2024 00:00:00 +0330

Artificial Intelligence (AI) has become a transformative force in the airline industry, reshaping key aspects such as operational efficiency and customer engagement. This study examines AI's integration and impact in airlines using a mixed-method research design, incorporating exploratory, descriptive, and correlational analyses. Insights are drawn from an extensive review of industry reports, case studies, and expert interviews, highlighting AI's role in enhancing airline operations, customer service, and strategic decision-making. Findings reveal significant improvements in operational metrics and customer satisfaction post-AI integration, alongside advanced revenue management techniques. The research also addresses the challenges and ethical considerations of AI implementation, while forecasting future trends and potential investment areas. Empirical evidence and comparative analyses emphasize AI's strategic importance in airline business management, pinpointing disparities in AI adoption and the need for scalable solutions. Conclusively, this study provides actionable insights for airline industry practitioners, outlining strategies for AI integration, operational improvement, ethical AI frameworks, and readiness for emerging technologies. It underscores AI's vital role in driving innovation, efficiency, and sustainability, offering both academic contributions and practical guidance for the airline sector.

The Artificial Intelligence and Data Science Future

Ebrahim Rasti Borazjani Faghat — Tue, 05 Mar 2024 00:00:00 +0330

In recent years, the fields of data science and artificial intelligence (AI) have become increasingly intertwined, shaping the future of technology and society. This article explores the evolution of AI and its implications for the future of data science. By examining current trends, challenges, and opportunities, we offer insights into how AI advancements will influence the practice of data science in the coming years.