Financial Sector Research Articles

AI security in different industries: A comprehensive review of vulnerabilities and mitigation strategies

Artificial intelligence has quickly and gradually spread across all sectors and fields, achieving exceptional performance in operations and decision-making. Yet, as industries process their tasks with the help of AI systems, new threats appear that require proper solutions. In the following section of this paper, the use of security solutions involving AI in various industries, which are finance, health, manufacturing, and government, as well as a discussion of risks involving artificial intelligence in particular fields and the corresponding measures to be taken against them will be discussed. AI is often applied to detect fraud and credit scoring in the financial sector but has potential risks, such as adversarial attacks and data manipulation. Similarly, the healthcare industry uses AI for predictive healthcare diagnostics and patient information protection; threats are data theft and adversarial examples attacking diagnostic models. In manufacturing, AI individuals contain predictive servicing and excellent control, though they are constantly exposed to industrial espionage and the sabotage of AI models. In retail, however, AI is used to improve marketing and predict consumer behavior. While these benefits exist, questions about using Algorithms that could be biased and privacy infringements continue to raise their ugly head. Defensive and governmental organizations that apply AI for surveillance or automatic operating systems are most vulnerable to adversarial intervention with key control systems and innate security systems. To counter such threats, this paper provides details of industry-specific measures, including adversarial training data sanitization, AI model audit, and privacy-preserving approaches. We illustrate examples linked to every one of these methods to show how they can be deployed in a variety of industries and to guard AI systems. To this end, the following review of the relationship between AI and cybersecurity demonstrates that security measures that rely on artificial intelligence must be regularly checked and updated. This reveals the call for proper, protective, and industry-specific measures to manage the risks and protect AI applications to ensure that the world’s modern, interconnected world is safe for AI.

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Product management: A multi-faceted approach to successful product development

Product management plays a pivotal role in ensuring the success of products across various industries, from digital applications to financial services. This paper examines the key aspects of product management, highlighting its strategic and tactical components. It also explores the role of the product manager, who must possess knowledge in marketing, design, technology, and data analysis. These areas are critical to managing the life cycle of a product and ensuring its success in a competitive market. The paper draws on real-world examples from the experience of Iuliia Nemudrova, a product manager with extensive experience in the financial sector.

Machine learning algorithms to prevent fraudulent transactions in real-time

In the rapidly evolving landscape of digital finance, the proliferation of online transactions has been accompanied by a significant increase in fraudulent activities. This paper explores the application of machine learning algorithms to detect and prevent fraudulent transactions in real-time, thereby enhancing the security and reliability of financial systems. We investigate various machine learning techniques, including supervised learning models such as Random Forest, Logistic Regression, and Neural Networks, as well as unsupervised methods like anomaly detection. By leveraging these algorithms, we aim to identify patterns and anomalies indicative of fraudulent behavior. Our approach integrates robust preprocessing techniques and real-time data analysis to ensure high accuracy and efficiency. The results demonstrate that machine learning models can significantly reduce the incidence of fraud, providing financial institutions with a powerful tool to safeguard their customers’ assets. This study underscores the potential of machine learning to transform fraud detection, offering a scalable and adaptive solution to one of the most pressing challenges in the financial sector

Dear reader,

Back in 1996, the foreword to the first issue of Juridica International conceptualised JI as a special edition of Juridica, the Estonian-language law journal published by the Faculty of Law of the University of Tartu. The purpose of the international edition was articulated as ‘providing an overview of the Estonian legal system and reporting on developments in legal reform’. The concept and aim of Juridica International have evolved considerably since. Over the years, JI has developed into a well-established international law journal. Many globally renowned scholars and jurists have published pieces in Juridica International. This forum continues to provide insight into developments cultivated in Estonia’s legal system, thereby maintaining its de facto position as one of the most reliable sources of information on the Estonian legal order for an international audience. However, most of the works published in today’s Juridica International are analytical articles that clearly meet international research standards. Juridica International’s core aspects have remained unchanged over time. Faculty of Law members still are expected to publish quality papers in Juridica International. Doctoral students and other early-career researchers find publication in this journal to be both a key ambition and a worthy challenge on their path as scholars. While Juridica International is open to submissions from young researchers, it offers them no concessions; in fact, the peer-review process involved is often more stringent than that applied to their senior colleagues internationally. Furthermore, the noteworthy standing of the Faculty of Law’s doctoral students – who are typically leading legal practitioners in their respective fields – renders their contributions particularly compelling and inspiring. For their senior colleagues with the faculty, publishing research in Juridica International is a matter of professional pride. Consequently, one can justifiably conclude that JI has become an essential part of the institutional identity. In other words, it serves as a conditio sine qua non for the University of Tartu’s Faculty of Law. The volume you are now perusing continues to follow the journal’s solidly established direction. The articles range from legal analysis and case studies to interdisciplinary empirical research, with topics encompassing both traditional legal matters and various emerging challenges posed by technological advances. Some cover both. For instance, the pages of this issue provide an overview of empirical data on today’s handling of education-related exceptions to copyright protections. Additionally, readers will find two articles focusing directly on intellectual property, specifically the patent system and the author’s right to integrity in public contracts. Constitutional and human-rights issues are explored in connection with constitutions’ preambles and in discussion of the global garment industry. Several of the articles address highly pressing contemporary challenges, among them data-driven public administration and electronic signatures. Another prominent facet is EU law, particularly with regard to criminal liability and competition. In addition, the papers grapple with the complexities of soft law in the financial sector and public procurement. Drawing together several threads encompassed by this issue’s discussion, one article declares in its title that there are more questions than answers. That is a significant observation indeed. Often, identifying the problems, delineating them properly, and asking the right questions proves more difficult than offering technically correct answers. As Voltaire suggested, we should judge people by their questions rather than their answers. Juridica International No. 33 certainly raises many important and thought-provoking questions.

Predicting Market Share Patterns using LSTM

Forecasting stock prices is a significant challenge in the financial sector, marked by its complexity and the impact of numerous variables. In this paper, we undertake a comprehensive investigation into how machine learning algorithms can be effectively applied to address this challenge. Our main goal is to clarify the methodologies and strategies that can enhance the accuracy of stock market predictions. We explore the complex process of predicting stock values and scrutinize the various machine learning algorithms that have been suggested and employed for this purpose. By critically evaluating these algorithms, we aim to provide insights into their individual strengths and weaknesses, ultimately helping the reader make an informed decision about the most appropriate algorithm for their specific forecasting requirements. Beyond algorithm selection and attribute analysis, our review also considers external factors that can significantly influence stock prices. These factors include a broad range of variables, such as economic conditions, geopolitical events, corporate news, and market sentiment. Understanding the interaction between these external elements and stock market dynamics is essential for developing more robust and reliable prediction models.

The Advancements Blockchain has had on the Financial Services Industry

The financial services industry handles transactions amounting to trillions of dollars daily, necessitating a focus on cost-efficiency, transparency, and security. Prior to the integration of blockchain technology, intermediaries such as money transfer services, stock exchanges, and payment networks frequently encountered cybercrime. Blockchain technology, initially popularized by cryptocurrencies like Bitcoin, has since become a transformative force across various financial sectors. This technology enhances the industry by providing secure, transparent, and cost-effective transaction protocols through encryption and algorithms. This prose explores the significant advancements blockchain has brought to financial services, emphasizing its role in revolutionizing insurance, asset management, banking, and the stock market.

Data analytics as a driver of digital transformation in financial institutions

Data analytics plays a pivotal role in driving digital transformation within financial institutions, enabling organizations to leverage insights from vast amounts of data to enhance decision-making and optimize operational performance. This paper explores the multifaceted impact of data analytics on the financial services sector, highlighting how it can improve customer experience, streamline processes, and foster innovation. By implementing advanced analytics techniques, financial institutions can better understand customer behaviour, identify market trends, and personalize services to meet evolving customer needs. The study discusses effective strategies for integrating data analytics into decision-making processes and operational workflows, emphasizing the importance of creating a data-driven culture that encourages collaboration across departments. Case studies of leading financial institutions that have successfully integrated data analytics into their operations illustrate the transformative outcomes, including improved customer satisfaction and increased revenue growth. Additionally, the paper addresses challenges such as data silos, regulatory compliance, and the need for skilled personnel to analyse and interpret data effectively. To overcome these hurdles, best practices are outlined, including investment in robust data infrastructure, fostering cross-functional teams, and ensuring continuous training for employees. The findings indicate that a strategic approach to data analytics not only enhances performance but also positions financial institutions to adapt to rapidly changing market conditions. Ultimately, this paper underscores the critical role of data analytics in shaping the future of financial services and achieving sustainable growth in an increasingly competitive landscape.

Ai-Driven Big Data Transformation And Personally Identifiable Information Security In Financial Data: A Systematic Review

This systematic review explores the impact of adopting artificial intelligence (AI) to analyze and transform big data in financial and economic contexts, with a specific focus on the privacy and security of personally identifiable information (PII). By examining 37 articles spanning the latest advancements in AI-driven big data technologies, the review identifies both opportunities and challenges in safeguarding PII during financial data transformation. Key findings reveal that while AI enhances data processing capabilities—enabling faster insights and predictive accuracy in economic trends—PII faces increased risks due to sophisticated data aggregation and correlation techniques. The review categorizes major AI methodologies used, including machine learning algorithms, natural language processing, and predictive analytics, highlighting how each can affect data privacy. The findings suggest that, to maintain trust, organizations must adopt AI responsibly, integrating privacy-by-design principles and adhering to data governance standards. This review contributes to a clearer understanding of the interplay between AI and PII protection, offering practical insights for stakeholders in the financial sector aiming to harness AI while prioritizing ethical data handling.

AI and blockchain technology for secure and transparent financial transactions

The integration of Artificial Intelligence (AI) and blockchain technology presents a transformative approach to enhancing the security and transparency of financial transactions. Blockchain’s decentralized and immutable ledger system ensures that transaction records are tamper-proof and transparent, significantly reducing the risk of fraud and errors. AI, with its advanced data analytics and machine learning capabilities, further augments this ecosystem by providing real-time monitoring, predictive analytics, and automated decision-making processes. Together, these technologies enable more efficient and secure financial operations, from cross-border payments to trade finance and asset management. This paper explores the synergistic potential of AI and blockchain in revolutionizing the financial sector, highlighting key applications, benefits, and challenges. By leveraging the strengths of both technologies, financial institutions can achieve unprecedented levels of security, efficiency, and trust in their operations.

The Impact of Asset-Liability Management on the Profitability of Listed Commercial Banks in Vietnam

This paper investigates the effect of asset-liability management on the profitability of listed commercial banks in Vietnam, analyzing annual data from 2013 to 2023. The study uses VIF tests, Heteroskedasticity tests, Model Specification tests, and the Generalized Least Squares (GLS) model with the Modified Wald test to examine the data. The results reveal a positive correlation between bank asset management and annual GDP growth with profitability. Conversely, liability management and the year-on-year growth rate of total assets negatively impact profitability. Specifically, factors such as loans and advances, investments in securities on the asset side, and deposits and borrowings from other credit institutions, along with customer deposits on the liability side, are significant. Lending activities, in particular, are identified as key drivers of financial performance in the Vietnamese banking sector. However, the minimal impact of investment securities on profitability raises questions, given their traditionally significant role in banking profitability strategies globally. The study also highlights the negative effects of certain liabilities on bank profitability, stressing the need for careful liability management for banks in Vietnam. While GDP growth is generally linked with economic prosperity and financial sector growth, its impact on bank profitability in Vietnam appears limited, suggesting that other factors might play a more significant role.

Analysis of Public Sector Financial Performance Using Value for Money in Tangerang Regency

Public sector financial performance requires evaluation as a form of evaluation so that financial performance is more optimal. The financial performance of the Tangerang Regency Government in this study uses the Value for Money (VfM) approach with factors from three main factors, namely economy, efficiency, and effectiveness in 2014 to 2023. Based on the analysis of the public sector financial performance of Tangerang Regency, it is very economical with an average of 101.33% and very efficient with an average of 112.93%, but its efficiency is still low with an average of 98.54%. The results of this analysis can be used as evaluation material for the Tangerang Regency Government to carry out efficiency in public sector financial performance, so that the Tangerang Regency Government can optimize the quality of service to the local community.

Public Sector Financial Reforms and Performance of Government Entities in Nigeria

A few scholars have remarked that public agencies in Nigeria are typified by corrupt activities, as well as a slew of other issues. Surprisingly, despite the fact that all succeeding Nigerian politicians and bureaucrats also acknowledge that corruption is a barrier to national growth, the bulk of them assume office as doctors but exit as patients. As a result, Nigerian Government has introduced various economic reforms to boost the performance of government entities in the country. Therefore, this study examined the effects of public sector financial reforms on performance of government entities in Nigeria. Specifically, this study examined the effects of anti-graft reforms, information technology based reforms and financial planning reforms on performance of government entities in Nigeria, The study employed survey research design to collect data from 306 personnel across 188 Ministries, Departments and Agencies (MDA) in Nigeria. Ordered logistic regression and ordinary least square (OLS) regression were employed for data analysis. The study found that anti-graft reforms (Anti-Graft agencies), information technology based reforms (IPPIS and GIFMIS), and financial planning reforms (Budgetary control) significantly enhanced the financial performance of government entities in Nigeria. Likewise, the study revealed that anti- graft reforms (Anti-Graft agencies), information technology based reforms (IPPIS), financial planning reforms (Budgetary procedure) and financial reporting reforms (IPSAS) had significant positive effects on non- financial performance of government entities in Nigeria. Overall results revealed that anti-graft reforms (Anti-Graft agencies, anti-graft legal framework), information technology based reforms (IPPIS and GIFMIS), and financial planning reforms (Budgetary procedure) significantly boosted the financial performance of government entities in Nigeria. The study concluded that public sector financial reforms boosted the performance of government entities in Nigeria, and recommended that the Nigerian government strengthen the agencies implementing public sector financial reforms to boost the performance of government entities better.

A Study on the Construction of Environmental Image of Four Chinese Banks: Based on Metadiscourse Analysis

With the escalating global concern for environmental issues and China's commitment to achieving carbon peaking and neutrality goals, this study aims to investigate how four leading Chinese banks have constructed environmental images through their CSR reports from 2016 to 2021. Utilizing Hyland's (2005) Metadiscourse model and AntConc 3.5.9 for corpus analysis, we quantitatively and qualitatively examine the strategic use of Metadiscourse in these reports. Our findings reveal a dominance of interactive Metadiscourse, particularly transitions and frame markers, which facilitate coherent and structured environmental disclosures. Interactional Metadiscourse, featuring self-mentions and engagement markers etc., underscores the banks' authority and reader engagement. The banks' CSR reports demonstrate commitments to carbon reduction, green finance and credit, ecological agriculture support, and environmental risk management, and then portray proactive environmental images. This research provides insights into how Metadiscourse constructs sustainability discourse in the financial sector, informing ESG reporting and environmental communication strategies.

Cloud Migration Analysis for Core System Infrastructure in Financial Services: Study Case PT XYZ

Currently, cloud computing adoption is being utilized in the financial sector for its flexibility and innovative technologies. This study assesses PT XYZ's readiness to migrate its core systems to a cloud environment, which is crucial for maintaining competitive agility and robustness in Indonesia's financial services sector. Focusing on the diffusion of innovations (DOI) model and the technology-organization-environment (TOE) framework, the research examines ten factors influencing PT XYZ's migration strategy. The analysis identifies challenges related to PT XYZ's aging on-premises infrastructure, including server procurement delays and maintenance issues, which impact operational efficiency. Through semi-structured interviews with key IT personnel, this study explores perceptions of the benefits and challenges of cloud computing, as well as regulatory compliance, aiming to guide PT XYZ toward a successful cloud migration that ensures continuity, compliance, and enhanced operational capabilities. The findings contribute to understanding the relative advantages, such as cost savings and security concerns, complexity, compatibility, technology readiness, top management support, firm size, competitive pressure, and regulatory factors. PT XYZ’s decision to migrate aligns with all the factors for cloud computing adoption, with the relative advantage, complexity, compatibility, technological readiness, and top management support being high impacts on the migration decision, while regulatory compliance presents a challenge for the financial services industry in Indonesia].

Quantum Cryptography for Secure Communications and the Enhancement of U.S. Cybersecurity in the Quantum Age

This paper explores the critical role of quantum cryptography in enhancing U.S. cybersecurity, focusing on its potential to counteract the growing threats posed by quantum computing. As quantum computers advance, classical encryption methods like RSA and AES face unprecedented vulnerabilities. Quantum Key Distribution (QKD) offers a groundbreaking solution by leveraging quantum mechanics to secure data transmission. The paper evaluates current implementations and future challenges of quantum cryptography, emphasizing its importance for government, military, financial, and healthcare sectors in safeguarding sensitive communications in the quantum age.

The Impact of (In)Civility on Selected Individual and Organisational Outcomes in Financial Services

Orientation: Despite its ubiquitous impact, the South African financial services sector lacks understanding of the constructs of civility and incivility.Research purpose: The study investigated the impact of (in)civility on work engagement, organisational citizenship behaviour (OCB), and turnover intention in a South African financial services organisation using adapted (in)civility scales.Motivation for the study: The dearth of research within the South African financial services organisation on the constructs of (in)civility and its impact as well as the testing of an adapted tool, necessitated this research.Research approach/design and method: A non-experimental research design, with purposive sampling (N = 305) was used in this study. Specifically, we used latent variable modelling methods in a confirmatory manner to establish a measurement and structural model from which to make inferences.Main findings: The study found that (in)civility is related, can co-exist and is independent. The findings accentuated the need to further explore how (in)civility is conceptualised, experienced and measured. Results showed that civility specifically, yielded positive outcomes, while incivility had a negative effect. Work engagement serves as a mediator in the relationships between civility, OCB and turnover intention only.Practical/managerial implications: The study suggests that organisations deepen their understanding of civility and incivility, and focus on promoting positive behaviours. Cultivating a civil organisational climate leads to beneficial outcomes.Contribution/value-add: The study enhances South Africa’s understanding of workplace (in)civility by testing adapted scales and promoting construct alignment between definition and measurement.

Impact of COVID-19 on the Effectiveness of Financial Management of Insurance Companies in Mainland China

The COVID-19 pandemic starting in 2019 has brought significant disruption and turbulence to the global economy, specifically the financial sector. At the same time, the increased awareness of risk in businesses, governments, and individuals should alleviate this disruption to the insurance industry. However, the general trend of the Chinese insurance industry varies among companies, specifically into three types: companies with healthy financial conditions and positive subsidiary expansion, unhealthy financial conditions and negative subsidiary expansion, and unhealthy financial conditions and positive subsidiary expansion. By studying five listed prominent insurance companies in China using qualitative methods, researchers found a general relation between the financial metrics of an insurance company and the subsidiary expansion number, but this relationship isn’t inclusive. The cause of this variation is the difference in choosing a risky or prudent financial strategy and the adaptation to digital transformation.

Unveiling the drivers of green loan disclosures: a study of financial and governance determinants

PurposeThis study aims to investigate the factors influencing Indian banks’ choice of green loan disclosure practices. The study analyzes the effect of financial and governance variables to understand the sustainable reporting (through green lending) behavior of Indian banks.Design/methodology/approachThe data on green loan disclosure has been hand-collected from the annual reports using a content analysis approach. Using the data of 26 banks for 12 years (2012–2023), the study uses the panel regression method to control for cross-sectional heterogeneity and generalized methods of the moment to address potential endogeneity issues.FindingsThe empirical results depict that larger banks with sufficient risk capital and a strong corporate governance framework demonstrate greater disclosure of green loans. However, growth opportunities and higher market value impedes the reporting of green lending.Research limitations/implicationsThe findings of the study will enhance the extant literature on sustainability disclosure by integrating the financial sector companies in the context of an emerging economy. However, future research may include nonbanking finance companies as well.Social implicationsBanks use societal deposits to invest in productive avenues, and therefore, it is paramount to understand their social and environmental consciousness while evaluating a financing proposal. This research provides a thorough understanding of the sustainable reporting of banks through the lens of green lending.Originality/valueThis research provides unique evidence on the bank-specific determinants of green loan disclosure in an emerging economy context as against the extant literature which primarily focused on sustainable reporting of nonfinancial companies.

Private banking market developments: Evidence from Hungary

Private banking institutions serve the financial sector’s wealthiest clientele via a dedicated value proposition. Based on the relevant tendencies and statistics, a remarkable expansion can be outlined since the mid-1990s. The aim of this study is to elaborate the Hungarian private banking market’s development as a case study. The paper also intends to add to the literature on this unique segment of the financial market. Based on the available statistics, the analysis primarily focuses on the Hungarian private banking market’s rapid development process. This can be underpinned by the clientele’s savings, number of accounts and respective segmentation limits of the institutions. Referring to the amount of savings, a correlation analysis indicates significant co-movements with specific social and economic variables. The growth rate of the Hungarian clientele’s savings outperformed the respective indicator in Western Europe during the review time period (2007–2020). The current paper also includes a section that summarises general challenges that private banking managers need to address during the development process. Generally, the literature on private banking can still be considered scarce, whereas there is a lack of studies on the Central-Eastern European region. The analysis of the Hungarian sector’s development path can serve with relevant information to any financial expert in the field.