Banking University

Integrating artificial intelligence (AI) into software engineering has revolutionized the software development lifecycle (SDLC). AI-driven tools like Codeium and Copilot have significantly improved developer efficiency by automating repetitive tasks, providing intelligent code suggestions, and enhancing collaboration. However, despite their benefits, these tools have certain limitations, including reliance on large datasets, security concerns, and resistance to adoption. This paper explores the advantages, industry constraints, cost-benefit analysis, and future implications of AI in software engineering, highlighting the transformative role of Codeium and Copilot. We also explore future directions, including scalability, accessibility, and regulatory frameworks, to ensure responsible AI adoption.

Law enforcement agencies face substantial difficulties tracking illicit money flow activities because these operations have become more complex and difficult to detect. Conventional detection methods often struggle to reveal advancing criminal financial networks effectively. To address this gap, this study proposes an innovative AI-driven reasoning mechanism that leverages advanced natural language processing, deep learning, machine learning, and human crowd intelligence. The suggested methodology exclusively incorporates automated reasoning capabilities with insights from expert human input, creating a forceful framework capable of discovering subtle patterns indicative of money laundering. By using innovative artificial intelligence tools and stylometric analysis, the reasoning mechanism increases the transparency, interpretability, and reliability of investigative processes. This research contributes to anti-money laundering investigations by supporting law enforcement agencies with a sophisticated analytical system that can detect complex money laundering activities while staying efficient and scalable.

ABSTRACT Global supply chains suffer billions in annual losses from unforeseen disruptions, emphasising the need for advanced risk management. This study explores Big Data’s role in mitigating such risks by analyzing 1,222 articles from 2003–2024, a period of rapid digital transformation. Findings show blockchain enhances real-time tracking and transparency, especially in logistics, while machine learning improves predictive analytics for demand forecasting and vaccine cold chain logistics. China and the United States contribute significantly due to strategic digital investments and international collaborations. Rather than listing authors, the study highlights key themes such as AI-enabled risk mitigation and blockchain-based transparency. It also maps institutional collaboration and examines how IoT and automation support real-time risk detection across supply chains. The study provides actionable insights for practitioners, including data-sharing strategies and predictive modelling, and suggests future research into ethical AI use, applications in low-resource settings, and emerging technologies like quantum computing.

This article examines the impact of technological innovations on employee retention in the manufacturing sectors, focusing on a case study of quality control. It considers how technology, by reducing the monotony of manual labor and increasing efficiency, can enhance employee experiences. The authors explore the application of innovative technology in the context of quality control, highlighting the challenges associated with its implementation, such as safety, costs, and the need for employee training. The article emphasizes how the proper use of technology can lead to increased job satisfaction, reduced employee turnover, and improved productivity, by presenting the case of a company that has successfully implemented technologies into its quality control processes. The authors note that the effective implementation of technological innovations requires a strategic approach that considers both technological and human aspects.

PurposeThis study aims to investigate whether stimulating a family business image can serve as a signal to enhance customer brand co-creation.Design/methodology/approachThe authors tested the proposed moderated serial mediation model utilising a sample of 521 experienced online customers. Using a scenario-based approach with an experimental design, the authors investigated how the stimulation of a family business image influences customers’ co-creation activities on social media.FindingsStimulating a family business image plays a significant role in fostering interactions among customers who respond to this signal. Furthermore, when there is an alignment between customers’ self-concept and their perception of brand authenticity, a stronger brand contribution is established. Nonetheless, this alignment does not translate into heightened involvement in brand co-creation activities, as active participation in co-creation necessitates empowerment derived from the co-creation process.Originality/valueBy taking a co-creation viewpoint, this study empirically demonstrates how stimulating a family business image can impact different levels of brand-related involvement. It also highlights the moderating influence of self-congruence in transmitting the signal, shedding light on individual differences in brand engagement. From a practical standpoint, the distinctive nature of a family business image should be recognised as an asset that enables managers to enhance customer responses and strengthen their market position.

Abstract The stock market is always considered a highly potential investment channel for the public. However, unpredictable fluctuations often require investors to continuously monitor and analyze the market with a huge amount of data. This research was conducted to implement machine learning methods in automated stock trading based on indicators, aiding investors in evaluating the effectiveness of trading strategies based on these indicators and suggesting the most appropriate investment portfolios, thus minimizing the time and effort spent on data processing. Specifically, the application process we developed and implemented involves four steps: (i) data collection, (ii) automated trading based on indicators (SMA, Bollinger Bands, RSI, MACD), (iii) building an optimal investment portfolio based on automated trading results using the Sharpe ratio method, and (iv) testing and evaluating the trading results with new data. Using data collected from VN30 stocks, the study results demonstrate that trading based on indicators and proposing an optimal investment portfolio yields high-profit rates and minimizes investor risks.

W artykule przedstawiono koncepcję Teorii Y, jednej z charakterystycznych zasad zarządzania relacjami w XX wieku, wprowadzonej przez Douglas McGregor’a w książce „Ludzka strona przedsiębiorstwa” (1960). Teoria Y zakłada, że pracownicy są zmotywowani, są odpowiedzialni i nie odczuwają niechęci do pracy. Przedstawiono zalety i wady teorii Y, podkreślając jej potencjał w rozwijaniu kreatywności i utrzymywanie satysfakcji pracowników, a także możliwość eliminowania jej ograniczeń w określonych sytuacjach. Przedstawiono pomyślne przykłady wdrożenia Teorii Y w firmach takich jak Facebook i Google, a także potencjalne wady, takie jak nadużywanie wolności i brak kontroli organizacyjnej. Dodatkowo omówiono związek pomiędzy Teorią Y a jej odpowiednikiem, Teorią X, która reprezentuje bardziej tradycyjny, dyrektywny styl zarządzania. Analizując obie teorie, podkreślono znaczenie dokładnego rozważenia i dostosowania podejścia do zarządzania w oparciu o specyficzny kontekst i potrzeby organizacji.

Abstract This paper examines the trading behavior of institutional investors in Taiwan before, during, and after a manipulation event and determines whether institutional investors benefit from their trading behavior during the period from the year 2000 to 2020. We find that stocks with a low turnover and small market capitalization are the main targets of price manipulators. In addition, the stock price of manipulated firms increases from the start date of the manipulation event, peaks at the end of the event, then falls after the event. Foreign institutions collude with manipulators to exaggerate stock prices for self‐benefit. In contrast, securities dealers counter the trading behavior of manipulators and act as market stabilizers, causing them to suffer losses. Moreover, foreign institutions earn higher returns on stocks of manipulated firms with a low turnover during and soon after manipulation; however, they earn a higher return on stocks of manipulated firms with a high turnover in the long run after manipulation.

Being immersed in the general and inevitable trend of the times, our country is step-by-step transforming itself into the stage of industrialization and modernization. Everything is coming down to ease, convenience and, above all, accuracy. Therefore, digital transforming is a great opportunity to close the gap towards the goal of becoming a much more developed country. Cloud computing plays a huge role in creating a revolutionary change in a good part of the economy, including Vietnam's banking system. Basically, it helps change the way existing resources are used, the operating structure by storing, distributing and processing information. Cloud computing application is becoming an inevitable course as it allows banks to integrate tasks through shared tools and platforms. As a result, decisions in economic movements are efficiently and promptly determined. The article focuses on introducing cloud computing platforms, digital transformation structures, analyzing the current situation, thereby giving recommendations on using cloud computing in the digital transformation of Vietnamese banks.

Being immersed in the general and inevitable trend of the times, our country is step-by-step transforming itself into the stage of industrialization and modernization. Everything is coming down to ease, convenience and, above all, accuracy. Therefore, digital transforming is a great opportunity to close the gap towards the goal of becoming a much more developed country. Cloud computing plays a huge role in creating a revolutionary change in a good part of the economy, including Vietnam's banking system. Basically, it helps change the way existing resources are used, the operating structure by storing, distributing and processing information. Cloud computing application is becoming an inevitable course as it allows banks to integrate tasks through shared tools and platforms. As a result, decisions in economic movements are efficiently and promptly determined. The article focuses on introducing cloud computing platforms, digital transformation structures, analyzing the current situation, thereby giving recommendations on using cloud computing in the digital transformation of Vietnamese banks.