Hasil untuk "Finance"

Michael R. Roberts, Toni M. Whited

D. Baleanu, K. Diethelm, E. Scalas et al.

Linda Smith Bamber, John (Xuefeng) Jiang, I. Wang

R. C. Hill, W. Griffiths, G. Lim

O. Blanchard

David H. Hsu

Joel A. C. Baum, B. Silverman

Toni M. Whited

T. Opler, S. Titman

Emir Karaosman, Advije Rizvani, Irdin Pekaric

Financial institutions face increasing cyber risk while operating under strict regulatory oversight. To manage this risk, they rely heavily on Cyber Threat Intelligence (CTI) to inform detection, response, and strategic security decisions. Artificial intelligence (AI) is widely suggested as a means to strengthen CTI. However, evidence of trustworthy production use in finance remains limited. Adoption depends not only on predictive performance, but also on governance, integration into security workflows and analyst trust. Thus, we examine how AI is used for CTI in practice within financial institutions and what barriers prevent trustworthy deployment. We report a mixed-methods, user-centric study combining a CTI-finance-focused systematic literature review, semi-structured interviews, and an exploratory survey. Our review screened 330 publications (2019-2025) and retained 12 finance-relevant studies for analysis; we further conducted six interviews and collected 14 survey responses from banks and consultancies. Across research and practice, we identify four recurrent socio-technical failure modes that hinder trustworthy AI-driven CTI: (i) shadow use of public AI tools outside institutional controls, (ii) license-first enablement without operational integration, (iii) attacker-perception gaps that limit adversarial threat modeling, and (iv) missing security for the AI models themselves, including limited monitoring, robustness evaluation and audit-ready evidence. Survey results provide additional insights: 71.4% of respondents expect AI to become central within five years, 57.1% report infrequent current use due to interpretability and assurance concerns and 28.6% report direct encounters with adversarial risks. Based on these findings, we derive three security-oriented operational safeguards for AI-enabled CTI deployments.

Rongkun Cui, Nana Zhang, Kun Zhu et al.

Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance. Such threats have become a significant web harm that erodes societal fairness and affects the well-being of online communities. However, existing detection methods based on graph neural networks (GNNs) struggle with two persistent challenges: (1) long-tailed data distributions, which obscure rare but critical fraudulent cases, and (2) fraud camouflage, where malicious transactions mimic benign behaviors to evade detection. To fill these gaps, we propose HIMVH, a Hippocampus-Inspired Multi-View Hypergraph learning model for web finance fraud detection. Specifically, drawing inspiration from the scene conflict monitoring role of the hippocampus, we design a cross-view inconsistency perception module that captures subtle discrepancies and behavioral heterogeneity across multiple transaction views. This module enables the model to identify subtle cross-view conflicts for detecting online camouflaged fraudulent behaviors. Furthermore, inspired by the match-mismatch novelty detection mechanism of the CA1 region, we introduce a novelty-aware hypergraph learning module that measures feature deviations from neighborhood expectations and adaptively reweights messages, thereby enhancing sensitivity to online rare fraud patterns in the long-tailed settings. Extensive experiments on six web-based financial fraud datasets demonstrate that HIMVH achieves 6.42% improvement in AUC, 9.74% in F1 and 39.14% in AP on average over 15 SOTA models.

Munazza Akhtar, Arshia Habib, Umer Javeid et al.

Purpose: This study examines how export quality (EQI) interacts with institutional quality (IQI), GDP per capita (GDPPC), and urbanisation (URB) to shape greenhouse gas (GHG) emissions in Asia, concentrating on whether IQI controls the environmental impact of EQI and in what circumstances does export modernisation translate into cleaner production and lower emissions. Design: Based on a theory-driven framework, this research utilises an econometric approach suitable for heterogeneous Asian panels, embedding an EQI×IQI interaction term to measure moderation. A log–log transformation provides elasticities and enables interpretation of short- and long-run dynamics, while considering cross-sectional dependence and slope heterogeneity. Findings: Higher EQI mitigate GHG emissions when IQI is strong, via better environmental implementation, cleaner technology diffusion, and access to green finance; in contrast, weak IQI can temper or reverse this effect, allowing carbon-intensive upgrade paths. URB and GDPPC impact energy demand and technology integration, with urbanisation conceivably declining emissions under robust governance but boosting them when governance is weak. The study indicates that the marginal effect of EQI on GHG changes with IQI levels, evidencing a context-dependent technology-for-green policy channel in Asia. Originality: The paper establishes a conditional EQI–GHG mechanism moderated by IQI within an Asia-focused context, tests the EQI×IQI interaction in a log–linear ARDL/PMG-ARDL framework, and highlights sectoral/regional heterogeneity to inform policy design in diverse Asian economies.

Anna Aksamit, Kaustav Das, Ivan Guo et al.

We consider a continuum of carbon-emitting firms who seek to maximise their stock price, and a regulator (e.g., Government) who wishes for the economy to flourish, whilst simultaneously punishing firms who behave non-green. Interpreting the regulator as a major player and the firms as the minor players, we model this setting through a mean field game with major and minor players. We extend the stochastic maximum principle derived by Carmona & Zhu [A probabilistic approach to mean field games with major and minor players. Annals of Applied Probability, 2016, 94, 745--788] by relaxing the assumptions on the forms of the minimisers for the Hamiltonians, allowing them to depend on more arguments. This allows the major and representative minor player to interact in a more natural fashion, thereby permitting us to consider more realistic models for our green and sustainable finance problem. Through our stochastic maximum principle, we derive explicit Nash equilibria for a number of examples.

Kiran Kamble, Melisa Russak, Dmytro Mozolevskyi et al.

We propose a new long-context financial benchmark, FailSafeQA, designed to test the robustness and context-awareness of LLMs against six variations in human-interface interactions in LLM-based query-answer systems within finance. We concentrate on two case studies: Query Failure and Context Failure. In the Query Failure scenario, we perturb the original query to vary in domain expertise, completeness, and linguistic accuracy. In the Context Failure case, we simulate the uploads of degraded, irrelevant, and empty documents. We employ the LLM-as-a-Judge methodology with Qwen2.5-72B-Instruct and use fine-grained rating criteria to define and calculate Robustness, Context Grounding, and Compliance scores for 24 off-the-shelf models. The results suggest that although some models excel at mitigating input perturbations, they must balance robust answering with the ability to refrain from hallucinating. Notably, Palmyra-Fin-128k-Instruct, recognized as the most compliant model, maintained strong baseline performance but encountered challenges in sustaining robust predictions in 17% of test cases. On the other hand, the most robust model, OpenAI o3-mini, fabricated information in 41% of tested cases. The results demonstrate that even high-performing models have significant room for improvement and highlight the role of FailSafeQA as a tool for developing LLMs optimized for dependability in financial applications. The dataset is available at: https://huggingface.co/datasets/Writer/FailSafeQA

Divij Chawla, Ashita Bhutada, Do Duc Anh et al.

We assess whether AI systems can credibly evaluate investment risk appetite-a task that must be thoroughly validated before automation. Our analysis was conducted on proprietary systems (GPT, Claude, Gemini) and open-weight models (LLaMA, DeepSeek, Mistral), using carefully curated user profiles that reflect real users with varying attributes such as country and gender. As a result, the models exhibit significant variance in score distributions when user attributes-such as country or gender-that should not influence risk computation are changed. For example, GPT-4o assigns higher risk scores to Nigerian and Indonesian profiles. While some models align closely with expected scores in the Low- and Mid-risk ranges, none maintain consistent scores across regions and demographics, thereby violating AI and finance regulations.

Purity Watetu Maina, Anett Parádi-Dolgos

Abstract Utilizing an event study methodology, the study examines the African Security Exchanges market returns reaction following their commitment to the United Nations Sustainable Stock Exchanges Initiative. While adoption of sustainability initiatives is informed by institutional, signaling and information asymmetry theories, the frameworks were developed in advanced economies with mature financial markets. Their applicability in emerging economies is still underexplored. Furthermore, the literature on effect of voluntary and mandatory sustainability initiatives on performance is still inconclusive. Through contextualizing the theories within Africa, this study provides empirical evidence on the effect of a voluntary initiative on market performance. The analysis conducted on different event windows, based on t test and random effects event study model, revealed an overall neutral reaction on market returns across African exchanges. Nonetheless, market liquidity exhibited a weak positive effect on the returns. Based on these findings, we recommend that African security exchanges adopt a balanced approach, combining voluntary and mandatory sustainability initiatives that are tailored to local context to enhance their effectiveness. Secondly, to enhance the credibility of voluntary sustainability commitment, exchanges should clearly communicate their implementation plans and regularly disclose measurable progress updates. The sample comprised of African security exchanges, institutions with distinct characteristics. While the model can be applied in other economies, generalization of findings to other contexts should be made with consideration of this limitation.

Rohan Kumar Mishra, Debidutta Pattnaik, M. Kabir Hassan et al.

This study aims to offer new quantitative and qualitative insights into transaction efficiency and conflict of interest among minority and controlling shareholders in related party transactions (RPTs). We utilize systematic literature review (SLR) and bibliometric techniques to analyse 218 published articles. Our analysis identifies significant contributors, publishing sources, research groups, and maps the evolution of RPT themes and their relationship to contemporary theoretical frameworks. Subsequently, we conduct a comprehensive network and content analysis. Our findings indicate that research in RPTs began evolving post-global financial crisis, particularly since 2008, with East-Asian researchers dominating the intellectual discourse. Most studies are non-collaborative and based on empirical evidence from a limited number of countries. Methodologically, many studies employ descriptive statistics or regression techniques. We identify six thematic clusters contributing to the growth narrative of RPT research. Furthermore, we identify potential avenues for future research in RPTs and corporate governance while highlighting progressive trends and dynamics within the selected themes.

Elif Hilal Nazlıoğlu, Dündar Kök, Uğur Soytaş

This study examines the interactions between stock markets, oil prices, and security of energy supply in 25 countries with the highest energy consumption from 1980 to 2018. We utilize bi- and multi-variate models with and without smooth structural breaks. The novel Fourier expansion to the Toda-Yamamoto causality procedure highlights the importance of smooth structural shifts in model specification. The new methodology uncovers significantly stronger impact of energy security on the causal link between energy and stock markets. Our results offer new and important implications for policymakers, investors, and researchers.

Chong Myung Park, Kimberly A. S. Howard, V. Scott H. Solberg

Abstract This longitudinal qualitative study examines the impact of the Invest in Girls (IIG) financial literacy program on young women's financial knowledge, behavior, and skills in the USA. We conducted 98 interviews over four years with program alumnae and analyzed the data using thematic analysis. The study reveals a clear progression in financial literacy skills as participants transitioned from high school through college. Key findings include the evolving importance of budgeting skills—from basic tracking methods to sophisticated digital tools—a progression from basic financial concepts to complex real-world applications, and a persistent need for practical, in-depth tax education. The study highlights the importance of adapting financial literacy programs to meet young women's changing developmental needs. These insights inform the design of developmentally appropriate financial literacy programs that can respond to participants' evolving educational needs. This paper adheres to the Consolidated Criteria for Reporting Qualitative Studies (COREQ).

Xiang Peng, Deheng Xiao

This study adopted the double difference method to study the effect of open government data (OGD) on city green land-use efficiency (CGLUE). It was found that opening government data had a significant promotional effect on CGLUE, and a number of robustness tests were the foundation for this finding. A mechanism analysis demonstrated that two key avenues via which government data openness can promote CGLUE are raising public awareness of environmental issues and strengthening urban green innovation potential. A heterogeneity analysis found that the effect of government data openness on CGLUE was more obvious in eastern cities, cities with higher levels of digital finance, and non-resource-based cities. In addition, open government data also reduced urban carbon emissions while improving CGLUE, contributing to China’s “double carbon” goal.