Frontiers in Business and Finance
type: channel
description: Frontiers in Business and Finance is a scholarly platform dedicated to advancing research in the fields of business, finance, and related areas. It provides a forum for the publication of cutting-edge studies, empirical research, and theoretical papers that address emerging trends, challenges, and opportunities in these domains. The journal covers a wide range of topics, including but not limited to financial management, investment strategies, corporate governance, business innovation, risk management, and sustainable finance.
publisher: INTERNATIONAL STUDY COUNSELOR (PRIVATE) LIMITED
language: en
publication name: Frontiers in Business and Finance
issn: 3079-9325
items: Multi-Granularity Dependency Modeling for Automated Fault Triage in High-Throughput Financial Transaction Systems
items: Machine Learning Algorithms for Credit Risk Assessment in Financial Markets: A Comparative Study of Gradient Boosting and Neural Networks
items: Counterfactual Explanations for AML Risk Scores Under Causal Constraints
items: Driving Rural Governance Modernization through New Quality Productive Forces: A Theoretical Framework and Empirical Evidence from China
items: ESG Performance, Debt Maturity Structure and Corporate Resilience
items: Entrepreneurial Ecosystem and the Growth of Technology Startups: A Case Study of Regional Innovation Clusters
items: Optimizing Digital Adjudication through Social Network Analysis: An Empirical Study of Credit Card Disputes in Beijing
items: Privacy-Preserving Federated Learning for Cross-Institution Anti-Money-Laundering Models
items: A Volatility-Aware Temporal Transformer for Intraday Risk Forecasting with Market Microstructure Signals
items: Beyond Automation in Tax Compliance Through Artificial Intelligence and Professional Judgment
items: Multi-Touch Attribution and Media Mix Modeling for Marketing ROI Optimization in E-Commerce Platforms
items: From Motivation to Performance: An Empirical Analysis of Human Resource Management in Zhengzhou's Manufacturing SMEs
items: Industry-linked stock volatility prediction based on graph neural networks
items: China's Rise and the Transformation of the Global Governance System: A Study from the Perspective of Southeast Asian Countries
items: The Evolution of Corporate Innovation Networks and Its Impact on Firm Valuation: An Empirical Study Based on Dynamic Graph Neural Networks
items: Transformer-Based Architecture for Real-Time Option Greeks Estimation Under Extreme Market Conditions
items: Building and Researching a Machine Learning Model for Identifying Corporate Tax Avoidance
items: AI-enabled Forecasting, Risk Assessment, and Strategic Decision Making in Finance
items: Usage-Based and Personalized Insurance Enabled by AI and Telematics
items: A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement
items: Early Warning of Financial Distress in Listed Companies: A Machine Learning Approach
items: Machine Learning Forecasting of Stock Market Indices Based on Macroeconomic Indicators
items: Cointegration and Dynamic Interactions: A Study on Trade Openness and Economic Growth in China
items: The Driving Factors of China’s Foreign Trade under Macroeconomic Fluctuations: An Empirical Analysis Based on Bilateral Linkages
items: Dual-Stage Graph Contrastive Learning for Robust E-Commerce Fraud Detection
items: Adversarially Robust Temporal Graph Contrastive Learning for Financial Fraud Detection
items: Blockchain-Enabled FinTech Platforms for Tokenizing Carbon Capture Outputs in Mechanical Systems
items: Real-Time Fraud Detection Using Reinforcement Learning with Dynamic Feature Selection
items: Assessing the Impact of Enhanced Capital Adequacy, Elevated Liquidity Ratios, Segregation of Banking Activities, and Restrictions on Bankers' Bonuses on Risk Management in Banks
items: Resilience Evaluation of Supply Chain Configuration in Export-Oriented Enterprises: Indicator Construction and Rebound Prediction Based on Transaction Data

Multi-Granularity Dependency Modeling for Automated Fault Triage in High-Throughput Financial Transaction Systems
type: item
description: High-throughput financial transaction systems constitute critical infrastructure in modern banking and financial services, processing millions of transactions per second while maintaining stringent requirements for reliability, consistency, and availability. The inherent complexity of these distributed systems, characterized by intricate interdependencies among microservices, databases, message queues, and external interfaces, presents substantial challenges in fault diagnosis and root cause analysis. This research proposes a novel multi-granularity dependency modeling framework that captures system behaviors across service-level, transaction-level, and resource-level abstractions to enable automated fault triage. The framework integrates real-time telemetry data including metrics, distributed traces, and transaction logs to construct dynamic dependency graphs that reflect evolving system states. A hierarchical fault propagation model is developed to distinguish between root causes and cascading failures, leveraging causal inference techniques and temporal correlation analysis. The proposed approach employs machine learning-based anomaly detection coupled with graph-based root cause localization algorithms to identify fault origins within seconds of symptom manifestation. Experimental evaluation on a production-scale financial transaction processing platform demonstrates that the multi-granularity approach achieves superior diagnostic accuracy compared to single-level analysis methods, reducing mean time to resolution by approximately 67% while maintaining a precision rate exceeding 92% for root cause identification. The framework provides actionable insights for automated incident response systems and contributes to the broader discourse on reliability engineering in mission-critical financial infrastructure.
subject: Articles
  topic: section
subject: Fault diagnosis
  topic: keywords
subject: dependency modeling
  topic: keywords
subject: financial transaction systems
  topic: keywords
subject: root cause analysis
  topic: keywords
subject: distributed systems
  topic: keywords
subject: microservices architecture
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: causal inference
  topic: keywords
creator: Tianyu Fang
creator: Qiming Zhou
creator: Ethan Brooks
rights: Copyright (c) 2026 Tianyu Fang, Qiming Zhou, Ethan Brooks (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-09
publication date: 2026-01-09
starting page: 74
ending page: 87
doi: 10.71465/fbf636

Machine Learning Algorithms for Credit Risk Assessment in Financial Markets: A Comparative Study of Gradient Boosting and Neural Networks
type: item
description: The accurate assessment of credit risk remains a cornerstone of financial stability and profitability for lending institutions globally. As the volume of transactional data expands and the complexity of financial behaviors increases, traditional statistical methods such as logistic regression often fail to capture the non-linear intricacies inherent in modern credit datasets. This paper presents a comparative analysis of two dominant machine learning paradigms: Gradient Boosting Machines, specifically the XGBoost implementation, and Artificial Neural Networks. Utilizing a comprehensive dataset of consumer loans, we evaluate these models based on predictive accuracy, computational efficiency, and interpretability. Our findings indicate that while both methodologies significantly outperform traditional baselines, they exhibit distinct advantages depending on the operational constraints. Gradient boosting demonstrates superior performance on tabular data with faster training times and greater interpretability through feature importance analysis. Conversely, neural networks show potential for capturing highly complex, high-dimensional interactions, albeit at a higher computational cost. The study concludes that the choice between these algorithms should be dictated by the specific requirements of the financial institution regarding the trade-off between predictive precision and model transparency.
subject: Articles
  topic: section
subject: Léa Thomas
  topic: keywords
subject: David Bowman
  topic: keywords
subject: Elizabeth Shaw
  topic: keywords
creator: Léa Thomas
creator: David Bowman
creator: Elizabeth Shaw
rights: Copyright (c) 2026 Léa Thomas , David Bowman, Elizabeth Shaw (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 66
ending page: 73
doi: 10.71465/fbf623

Counterfactual Explanations for AML Risk Scores Under Causal Constraints
type: item
description: This study introduces a counterfactual-explanation framework for anti-money-laundering (AML) risk scoring. The method distinguishes actionable from non-actionable variables within a causal graph and generates minimal valid changes needed to shift a case below a risk threshold. Experiments used 42.7 million transactions and 3.1 million customer profiles from a nationwide financial dataset. The system generated valid counterfactuals for 91.5% of high-risk cases, requiring an average of 2.4 feature changes per case. In a user evaluation with 26 compliance analysts, counterfactual explanations reduced review time by 22.6% and increased decision consistency from 0.62 to 0.75 (Cohen’s kappa). Causal constraints eliminated unrealistic recommendations in 98.6% of generated explanations. The method enhances model transparency while maintaining detection performance.
subject: Articles
  topic: section
subject: Counterfactual explanation
  topic: keywords
subject: Causal inference
  topic: keywords
subject: Anti-money-laundering
  topic: keywords
subject: Interpretable AI
  topic: keywords
subject: Risk scoring
  topic: keywords
creator: Ethan Morales
creator: Sophia Bennett
creator: Michael Turner
rights: Copyright (c) 2026 Ethan Morales, Sophia Bennett, Michael Turner (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-09
publication date: 2026-01-09
starting page: 58
ending page: 65
doi: 10.71465/fbf598

Driving Rural Governance Modernization through New Quality Productive Forces: A Theoretical Framework and Empirical Evidence from China
type: item
description: In the grand historical process of comprehensively promoting Chinese modernization, the modernization of rural governance serves not only as a crucial cornerstone of the national governance system and governance capacity modernization but also as a key support for realizing the rural revitalization strategy. Currently, with the deepening development of the new round of scientific and technological revolution and industrial transformation, New Quality Productive Forces (NQPF)—characterized by high technology, high efficiency, and high quality—are profoundly reshaping traditional modes of production and social structures. This paper aims to construct a theoretical analysis framework based on the three-dimensional synergy of technology-industry-institution to deeply analyze the internal mechanism by which NQPF drives the modernization of rural governance. The paper first reviews the evolution of productivity theory, pointing out that NQPF overcomes the dilemmas of information asymmetry, resource fragmentation, and subject atomization in traditional rural governance through the three dimensions of technological penetration, industrial upgrading, and institutional reshaping. Subsequently, based on panel data from 38 districts and counties in Chongqing, China (2021-2023) and in-depth field research cases, this paper empirically tests the governance effectiveness of NQPF using fixed effects models, mediation effect models, and heterogeneity analysis methods. The study finds that: first, NQPF significantly improves rural governance efficiency, and this conclusion remains valid after various robustness tests; second, the degree of information openness and the degree of multi-subject synergy are key mediating mechanisms; third, there is significant heterogeneity between the main urban metropolitan area and the mountainous areas of northeast/southeast Chongqing, where differences in industrial foundation and digital infrastructure determine the boundaries of the enabling effec…
subject: Articles
  topic: section
subject: New Quality Productive Forces
  topic: keywords
subject: Rural Governance Modernization
  topic: keywords
subject: Technology-Industry-Institution Synergy
  topic: keywords
subject: Chongqing Experience
  topic: keywords
subject: Fixed Effects Model
  topic: keywords
creator: Tingchen Kou
creator: Xin Gao
creator: Yuduo Lu
rights: Copyright (c) 2026 Tingchen Kou, Xin Gao, Yuduo Lu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 43
ending page: 57
doi: 10.71465/fbf585

ESG Performance, Debt Maturity Structure and Corporate Resilience
type: item
description: This paper explores the relationship between corporate ESG performance and corporate resilience through an empirical analysis of data from China A-share listed companies from 2014 to 2023. The study finds that strong ESG performance significantly enhances corporate resilience and improves the ability to cope with risks. Further analysis indicates that the debt maturity structure plays a crucial moderating role, where an increase in the proportion of long-term debt strengthens the positive impact of ESG performance on corporate resilience, while short-term debt weakens this effect. Additionally, there is heterogeneity in the impact of ESG performance on corporate resilience across firms with different ownership structures and internal control levels. Based on these findings, the paper recommends that governments improve relevant policies and regulations, and that companies adopt forward-looking strategies and enhance organizational learning mechanisms to strengthen corporate resilience and achieve sustainable development.
subject: Articles
  topic: section
subject: ESG Performance
  topic: keywords
subject: Corporate Resilience
  topic: keywords
subject: Organizational Resilience
  topic: keywords
subject: Debt Maturity Structure
  topic: keywords
creator: Zhang Kaixuan
rights: Copyright (c) 2026 Zhang Kaixua (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 29
ending page: 42
doi: 10.71465/fbf583

Entrepreneurial Ecosystem and the Growth of Technology Startups: A Case Study of Regional Innovation Clusters
type: item
description: This study explores the interaction mechanism between entrepreneurial ecosystems and the growth of technology startups, with a focus on fintech innovation clusters in Asia. Adopting a case-based comparative analysis method, three typical Asian fintech clusters—Shanghai Lujiazui, Singapore, and Bangalore—are selected as research cases. Based on the theoretical framework constructed by integrating Dynamic Capability Theory and Innovation Cluster Theory, this study examines the core supporting mechanisms of entrepreneurial ecosystems (resource mobilization, network collaboration, and institutional support) and their impacts on startup growth. The research identifies three common characteristics of Asian fintech entrepreneurial ecosystems: guiding policy support, industry-university-research integration, and multi-level capital guarantees, while also revealing differentiated features driven by regional advantages such as financial resources, internationalization, and talent cost advantage. Finally, optimization paths for enhancing ecosystem effectiveness are proposed. This study enriches the theoretical framework of entrepreneurial ecosystems and provides practical guidance for stakeholders to foster regional innovation clusters.
subject: Articles
  topic: section
subject: Entrepreneurial Ecosystem
  topic: keywords
subject: Technology Startups
  topic: keywords
subject: Fintech Innovation Clusters
  topic: keywords
subject: Comparative Case Analysis
  topic: keywords
subject: Resource Mobilization
  topic: keywords
creator: DU YING
creator: Murphy Choy
creator: Wang Ziqian
rights: Copyright (c) 2026 DU YING, Murphy Choy, Wang Ziqian (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-12
publication date: 2026-01-12
starting page: 14
ending page: 28
doi: 10.71465/fbf576

Optimizing Digital Adjudication through Social Network Analysis: An Empirical Study of Credit Card Disputes in Beijing
type: item
description: Amid the rapid digitalization of judicial systems, the integration of big data into adjudication remains underexplored, particularly in uncovering the structural logic of legal applications. This study bridges this gap by employing social network analysis (SNA) to examine credit card disputes involving personal information protection adjudicated in Beijing (2022–2024). By constructing a legal citation network, we reveal the latent patterns of substantive and procedural law application. The findings demonstrate that SNA can effectively identify core legal norms and typify cases, offering a robust methodological framework for optimizing 'Digital Court' systems. These insights provide practical pathways for enhancing judicial efficiency and consistency through data-driven case retrieval and holistic judicial information networks.
subject: Articles
  topic: section
subject: Judicial Cognition
  topic: keywords
subject: Digital Court
  topic: keywords
subject: Social Network Analysis (SNA)
  topic: keywords
subject: Legal Reasoning Network
  topic: keywords
subject: Computational Legal Studies
  topic: keywords
creator: Chung Han Tsai
creator: ChengTo Lin
creator: Baowen Zhang
creator: Qingyue Deng
creator: Yunhui Zhao
creator: Zhijia Song
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-08
publication date: 2026-01-08
starting page: 1
ending page: 13
doi: 10.71465/fbf572

Privacy-Preserving Federated Learning for Cross-Institution Anti-Money-Laundering Models
type: item
description: The proliferation of digital financial transactions has precipitated a commensurate rise in sophisticated financial crimes, specifically money laundering, which imposes significant stability risks on the global economic framework. Traditional Anti-Money Laundering (AML) systems, predominantly relying on rule-based engines or isolated machine learning models within single institutions, fail to capture the complex, cross-institutional topology of modern laundering networks. While collaborative learning offers a theoretical solution, strict data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) inhibit the centralized aggregation of sensitive transaction data. This paper presents a comprehensive framework for Privacy-Preserving Federated Learning (PPFL) tailored specifically for AML applications. We propose a novel architecture that integrates Differential Privacy (DP) with Secure Multi-Party Computation (SMPC) to enable financial institutions to collaboratively train robust Deep Neural Networks (DNNs) without sharing raw transaction ledgers. Furthermore, we address the challenge of non-Independent and Identically Distributed (non-IID) data, a characteristic inherent to the heterogeneous customer bases of different banks. Our experimental results demonstrate that the proposed framework achieves detection rates comparable to centralized training baselines while mathematically guaranteeing data privacy, thereby resolving the dilemma between regulatory compliance and effective financial crime detection.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Anti-Money Laundering
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Financial Crime Detection
  topic: keywords
creator: Kevin Clark
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 429
ending page: 436
doi: 10.71465/fbf553

A Volatility-Aware Temporal Transformer for Intraday Risk Forecasting with Market Microstructure Signals
type: item
description: The accurate forecasting of intraday financial risk is a cornerstone of modern algorithmic trading and systemic stability analysis. Traditional econometric models often fail to capture the nonlinear dependencies and rapid regime shifts characteristic of high-frequency limit order book (LOB) data, while standard deep learning architectures frequently struggle with the extremely low signal-to-noise ratio inherent in microstructure signals. This paper introduces the Volatility-Aware Temporal Transformer (VATT), a novel deep learning architecture designed specifically for intraday realized volatility forecasting. Unlike canonical Transformers, VATT incorporates a specialized Volatility Gating Module (VGM) that dynamically modulates the attention mechanism based on the prevailing market regime, allowing the model to distinguish between transient noise and structural volatility shifts. We leverage granular microstructure signals, including Order Flow Imbalance (OFI) and depth-weighted spread, to enhance the feature space beyond simple price history. Extensive experiments conducted on tick-level data for major equity indices demonstrate that VATT significantly outperforms GARCH-family baselines and standard Long Short-Term Memory (LSTM) networks in terms of Mean Absolute Error and Quasi-Likelihood loss. The results suggest that integrating volatility-specific inductive biases into the Transformer architecture is crucial for robust risk forecasting in high-frequency domains.
subject: Articles
  topic: section
subject: Financial Time Series
  topic: keywords
subject: Transformer Architecture
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Market Microstructure
  topic: keywords
creator: Zhiming Hu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 419
ending page: 428
doi: 10.71465/fbf552

Beyond Automation in Tax Compliance Through Artificial Intelligence and Professional Judgment
type: item
description: Tax compliance has evolved significantly with the integration of artificial intelligence (AI) technologies, transforming traditional manual processes into sophisticated automated systems. This review examines the intersection of AI automation and professional judgment in tax compliance, exploring how these elements complement rather than replace each other. The paper analyzes various AI applications including machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and blockchain technology in tax administration and compliance functions. While AI demonstrates remarkable capabilities in processing vast amounts of tax data, identifying patterns, and automating routine tasks, professional judgment remains indispensable for interpreting complex tax regulations, assessing unique circumstances, and making ethical decisions. This review synthesizes recent research on intelligent automation systems, predictive analytics for tax risk assessment, and cognitive technologies that support tax professionals. The analysis reveals that optimal tax compliance outcomes emerge from hybrid approaches that leverage AI efficiency alongside human expertise. Key challenges include algorithmic transparency, regulatory adaptation, data quality, and the preservation of professional skepticism in automated environments. The findings suggest that successful tax compliance in the digital era requires reimagining professional roles, developing new competencies, and establishing governance frameworks that ensure responsible AI deployment. This comprehensive examination provides insights into the future of tax compliance where technology augments rather than diminishes the critical role of professional judgment.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: tax compliance
  topic: keywords
subject: professional judgment
  topic: keywords
subject: machine learning
  topic: keywords
subject: automation
  topic: keywords
subject: tax administration
  topic: keywords
subject: human-AI collaboration
  topic: keywords
subject: robotic process automation
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: digital taxation
  topic: keywords
creator: Jiaying Chen
creator: Huijie Fan
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-22
publication date: 2025-12-22
starting page: 399
ending page: 418
doi: 10.71465/fbf534

Multi-Touch Attribution and Media Mix Modeling for Marketing ROI Optimization in E-Commerce Platforms
type: item
description: The exponential growth of digital marketing channels has created unprecedented complexity in understanding customer journeys and optimizing marketing investments in e-commerce platforms. Multi-touch attribution (MTA) and media mix modeling (MMM) have emerged as complementary approaches for measuring marketing effectiveness and maximizing return on investment (ROI). This review examines the theoretical foundations, methodological developments, and practical applications of MTA and MMM in e-commerce contexts from 2019 onwards. Multi-touch attribution enables granular tracking of individual customer touchpoints across digital channels, while media mix modeling provides aggregate-level insights into marketing effectiveness through econometric analysis. Machine learning (ML) and artificial intelligence (AI) have revolutionized both approaches, enabling more accurate attribution modeling and predictive optimization. Recent advances integrate unified measurement frameworks that combine the strengths of MTA and MMM to overcome their individual limitations. This paper synthesizes current research on data integration challenges, algorithmic innovations, privacy considerations, and implementation strategies. The review highlights how modern attribution systems leverage deep learning (DL), Bayesian methods, and causal inference techniques to navigate the increasingly complex digital marketing ecosystem. Emerging trends include privacy-preserving measurement, cross-device attribution, and real-time optimization algorithms that adapt to dynamic market conditions. The synthesis reveals that successful ROI optimization requires not only sophisticated analytical techniques but also organizational alignment, data infrastructure investment, and continuous model validation against business outcomes.
subject: Articles
  topic: section
subject: Multi-touch attribution
  topic: keywords
subject: media mix modeling
  topic: keywords
subject: marketing ROI
  topic: keywords
subject: e-commerce analytics
  topic: keywords
subject: customer journey mapping
  topic: keywords
subject: marketing optimization
  topic: keywords
subject: attribution modeling
  topic: keywords
subject: digital marketing measurement
  topic: keywords
subject: machine learning applications
  topic: keywords
subject: conversion funnel analysis
  topic: keywords
creator: Jingyi Liu
creator: Ying Wang
creator: Han Lin
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 378
ending page: 398
doi: 10.71465/fbf528

From Motivation to Performance: An Empirical Analysis of Human Resource Management in Zhengzhou's Manufacturing SMEs
type: item
description: This study examines the impact of integrated human resource management practices—specifically employee motivation, retention strategies, and well-being initiatives—on firm performance in manufacturing SMEs in Zhengzhou. Using a cross-sectional design, data were collected via structured questionnaires and supplemented by semi-structured interviews with HR managers and employees. Quantitative analysis, including descriptive statistics, correlation analysis, and multiple regression, reveals that all three HR factors are positively associated with firm performance, with employee motivation emerging as the most significant predictor. The findings underscore the importance of comprehensive HR strategies in fostering a motivated, committed workforce that drives competitive advantage in dynamic industrial settings. Although the study is limited by its cross-sectional design and reliance on self-reported data, it provides valuable insights for both scholars and practitioners seeking to enhance HR practices in manufacturing SMEs.
subject: Articles
  topic: section
subject: Employee Motivation
  topic: keywords
subject: Employee Retention
  topic: keywords
subject: Employee Well-being
  topic: keywords
subject: Firm Performance
  topic: keywords
subject: Manufacturing SMEs
  topic: keywords
creator: Liu Siyuan
creator: Mohd Dan Jantan
rights: Copyright (c) 2025 Liu Siyuan, Mohd Dan Jantan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-23
publication date: 2025-12-23
starting page: 373
ending page: 378
doi: 10.71465/fbf487

Industry-linked stock volatility prediction based on graph neural networks
type: item
description: This paper develops a simple hybrid model that combines Graph Neural Networks (GNN) with Light Gradient Boosting Machine (LightGBM) to improve stock market volatility prediction by using links between industries. The model builds an industry correlation graph to extract relationship features through GNN, and these features are then used by LightGBM for volatility forecasting. Based on data from major U.S. market sectors, the proposed model increases R2R^2R2 by 6.5% compared with the baseline LightGBM model and shows lower prediction error during highly volatile periods. The findings show that using industry connections helps capture cross-sector risk transmission and improves both accuracy and stability. This approach can be applied to market monitoring and investment risk control. However, the current version uses a fixed correlation graph and daily data, which limits its ability to adapt to fast market changes. Future studies should focus on building adaptive graphs and combining real-time data sources for better short-term prediction
subject: Articles
  topic: section
subject: graph neural network
  topic: keywords
subject: volatility prediction
  topic: keywords
subject: industry relation
  topic: keywords
subject: LightGBM
  topic: keywords
subject: financial market
  topic: keywords
subject: risk modeling
  topic: keywords
creator: Liam J. Thompson
creator: Sofia Martinez
creator: Ethan K. Wong
creator: Amelia R. Clark
creator: Oliver B. Reid
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-30
publication date: 2025-11-30
starting page: 366
ending page: 372
doi: 10.71465/fbf452

China's Rise and the Transformation of the Global Governance System: A Study from the Perspective of Southeast Asian Countries
type: item
description: With China's continuous rise, the global governance system is undergoing profound changes. As the world's second-largest economy and an emerging international responsibility power, China plays a significant role in shaping new international rules and promoting the trend of multipolarization. Southeast Asia, as an important region connecting China with the world, plays a key role in this transformation. This paper takes the major countries in Southeast Asia as the research object, explores the impact of China's rise on regional countries and their response strategies, and analyzes the new pattern of regional cooperation and competition. The research suggests that under the background of China's expanding influence, Southeast Asian countries adopt diverse strategies to seek their own interests, and regional cooperation is gradually entering a new stage of multilateral governance. Finally, it is proposed that regional countries should enhance their autonomy, promote regional integration, and achieve greater say in the global governance system.
subject: Articles
  topic: section
subject: China's rise
  topic: keywords
subject: Global governance system
  topic: keywords
subject: Southeast Asia
  topic: keywords
subject: Regional and country-specific studies
  topic: keywords
creator: Luo Kun
rights: Copyright (c) 2025 Luo Kun (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-15
publication date: 2025-08-15
starting page: 355
ending page: 365
doi: 10.71465/fbf429

The Evolution of Corporate Innovation Networks and Its Impact on Firm Valuation: An Empirical Study Based on Dynamic Graph Neural Networks
type: item
description: In an increasingly interconnected and knowledge-driven economy, corporate innovation networks have become critical conduits for competitive advantage. However, prior research has predominantly relied on static or comparatively static analyses, failing to capture the dynamic nature of these inter-firm relationships. This study addresses this gap by investigating how the temporal evolution of a firm's position within its innovation network influences its market valuation. We construct a dynamic network of strategic alliances among U.S. publicly traded firms from 1995 to 2020, using data from the SDC Platinum database, and link it to financial data from Compustat and CRSP. To model the complex, path-dependent nature of network evolution, we employ a Dynamic Graph Neural Network (DGNN), specifically the EvolveGCN architecture. Our empirical results demonstrate that a firm's network trajectory contains significant predictive power for its future valuation, over and above traditional financial controls and static network metrics. Specifically, trajectories characterized by increasing centrality and brokerage capabilities are positively associated with higher firm valuation, as measured by Tobin's Q. These findings contribute to the Knowledge-Based View and network theory by highlighting the strategic importance of dynamic network management capabilities. Methodologically, this study showcases the utility of DGNNs for addressing complex, time-varying relational questions in strategic management and finance.
subject: Articles
  topic: section
subject: Corporate Innovation Networks
  topic: keywords
subject: Firm Valuation
  topic: keywords
subject: Dynamic Graph Neural Networks (DGNNs)
  topic: keywords
subject: Knowledge-Based View
  topic: keywords
creator: Ming Guo
rights: Copyright (c) 2025 Ming Guo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-02
publication date: 2025-11-02
starting page: 339
ending page: 354
doi: 10.71465/fbf425

Transformer-Based Architecture for Real-Time Option Greeks Estimation Under Extreme Market Conditions
type: item
description: The accurate and timely estimation of option Greeks remains a critical challenge in financial risk management, particularly during periods of extreme market volatility when traditional computational methods encounter severe limitations in both speed and reliability. This paper presents a novel application of Transformer-based deep learning architectures to the problem of real-time option Greeks estimation under extreme market conditions, addressing fundamental challenges that have constrained conventional approaches including computational bottlenecks, numerical instability, and inadequate handling of long-range temporal dependencies in volatility dynamics. We develop a specialized attention mechanism that exploits the structural properties of option surfaces while maintaining computational efficiency through strategic architectural design incorporating multi-head self-attention, gated neural network mechanisms that enforce economic rationality constraints, and positional encoding adapted for financial time series exhibiting non-stationary behavior. The empirical investigation employs comprehensive datasets spanning multiple market regimes including the 2008 financial crisis characterized by VIX levels exceeding 80 percent as documented in detailed intraday records, the August 2015 volatility spike reaching 53 percent, and the March 2020 COVID-19 pandemic market disruption with VIX peaking at 89.53 percent, providing robust assessment across diverse stress scenarios that reveal the catastrophic failure modes of traditional methods. Our Transformer-based approach achieves Delta estimation accuracy with Mean Absolute Error below 0.001 for at-the-money options during normal market conditions and maintains stable performance with MAE below 0.002 during extreme volatility events where traditional finite difference methods exhibit errors exceeding 0.05, representing more than twentyfivefold improvement in accuracy under stress conditions. The architecture leverages a stra…
subject: Articles
  topic: section
subject: Transformer Architecture
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Option Greeks
  topic: keywords
subject: Delta Hedging
  topic: keywords
subject: Volatility Surface
  topic: keywords
subject: Extreme Market Conditions
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Real-Time Computation
  topic: keywords
subject: Financial Risk Management
  topic: keywords
subject: VIX Index
  topic: keywords
subject: Market Crisis
  topic: keywords
subject: Gated Neural Networks
  topic: keywords
subject: Stratified Sampling
  topic: keywords
creator: Liang Zhao
creator: Mei Lin
creator: Hiroshi Tanaka
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 311
ending page: 338
doi: 10.71465/fbf420

Building and Researching a Machine Learning Model for Identifying Corporate Tax Avoidance
type: item
description: Corporate tax avoidance poses significant challenges to public finance and economic equity, yet its detection remains complex due to the nuanced nature of financial and non-financial corporate data. This study aims to develop and evaluate a machine learning model capable of accurately identifying corporate tax avoidance behaviors using a multi-dimensional dataset. The methodology incorporates financial ratios, ownership structures, and industry characteristics as predictive features, employing gradient boosting algorithms to classify firms based on their likelihood of engaging in tax avoidance. The model was trained and validated on a global dataset comprising over 10,000 publicly listed companies from 2000 to 2020. Key findings indicate that the model achieves an F1-score of 0.87, significantly outperforming traditional logistic regression benchmarks. Additionally, feature importance analysis reveals that profitability metrics, subsidiary networks, and jurisdictional attributes are among the most influential predictors. These results underscore the potential of machine learning to enhance regulatory oversight and inform policy-making by enabling early and precise identification of tax avoidance practices.
subject: Articles
  topic: section
subject: Tax Avoidance Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Corporate Governance
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Zhipeng Wang
creator: Yufei Chen
rights: Copyright (c) 2025 Zhipeng Wang, Yufei Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 296
ending page: 310
doi: 10.71465/fbf399

AI-enabled Forecasting, Risk Assessment, and Strategic Decision Making in Finance
type: item
description: The integration of artificial intelligence (AI) into financial services has fundamentally transformed how institutions approach forecasting, risk assessment, and strategic decision making. This review examines recent developments in AI-enabled financial applications, with particular emphasis on machine learning (ML) and deep learning (DL) methodologies. Financial forecasting has evolved from traditional statistical models to sophisticated neural network architectures capable of processing vast amounts of structured and unstructured data. Risk assessment frameworks now incorporate advanced AI algorithms that can identify complex patterns and anomalies in real-time, significantly enhancing predictive accuracy and regulatory compliance. Strategic decision making has been revolutionized through the deployment of reinforcement learning (RL) and natural language processing (NLP) systems that analyze market sentiment, optimize portfolio allocation, and generate actionable insights. This paper synthesizes current research on AI applications across these three critical domains, examining the methodologies, performance benchmarks, and practical implementations. The review also addresses persistent challenges including model interpretability, data quality requirements, regulatory constraints, and computational complexity. Through comprehensive analysis of recent literature, this study identifies emerging trends such as explainable AI (XAI) in finance, hybrid modeling approaches, and the integration of alternative data sources. The findings suggest that while AI technologies offer substantial improvements in accuracy and efficiency, successful implementation requires careful consideration of domain-specific constraints, ethical implications, and the balance between automation and human expertise. This review provides researchers and practitioners with a structured understanding of the current state and future trajectory of AI in financial forecasting, risk management, and strat…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Financial Forecasting
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Fintech
  topic: keywords
creator: Shiyang Chen
creator: Shaochen Ren
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-19
publication date: 2025-10-19
starting page: 274
ending page: 295
doi: 10.71465/fbf397

Usage-Based and Personalized Insurance Enabled by AI and Telematics
type: item
description: The insurance industry is undergoing a fundamental transformation driven by artificial intelligence (AI) and telematics technologies, enabling the shift from traditional risk pooling models to usage-based insurance (UBI) and personalized coverage frameworks. This paper examines how AI algorithms, including machine learning (ML) and deep learning (DL), combined with telematics data collection systems, are revolutionizing insurance pricing, risk assessment, and customer engagement. The integration of Internet of Things (IoT) devices, connected vehicles, and wearable sensors provides insurers with granular, real-time behavioral data that enables dynamic premium adjustment and individualized policy customization. Through comprehensive analysis of current implementations and emerging applications, this study explores the technical architecture of AI-enabled UBI systems, examines the transformation of actuarial practices through predictive analytics, and evaluates the implications for stakeholders across the insurance ecosystem. The findings reveal that AI-driven telematics solutions significantly enhance pricing accuracy, reduce adverse selection, improve customer satisfaction through fairness perceptions, and create new opportunities for preventive risk management. However, challenges persist regarding data privacy, algorithmic transparency, regulatory compliance, and equitable access to technology-enabled insurance products. This paper provides insights into how insurers can leverage AI and telematics to create sustainable competitive advantages while addressing ethical considerations and ensuring consumer protection in the evolving landscape of personalized insurance.
subject: Articles
  topic: section
subject: Usage-based insurance
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Telematics
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Personalized insurance
  topic: keywords
subject: Risk assessment
  topic: keywords
subject: IoT
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Insurance technology
  topic: keywords
creator: Tiejiang Sun
creator: Mengdie Wang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-19
publication date: 2025-10-19
starting page: 262
ending page: 273
doi: 10.71465/fbf396

A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement
type: item
description: Tax administration in the era of digital transformation confronts substantial challenges in integrating and interpreting heterogeneous, multimodal data at enterprise scale. This study proposes a knowledge graph-based multimodal artificial intelligence framework that combines computer vision, natural language processing, and tabular modeling to deliver end-to-end analysis of invoice images, financial texts, and transactional records. At its core, the framework builds a tax-specific knowledge graph that connects transaction entities, voucher attributes, and regulatory terms, and applies graph neural networks for semantic alignment and reasoning. To address privacy and governance constraints, the framework integrates federated learning and differential privacy to protect sensitive financial data while enabling collaborative model improvement. Explainable AI methods generate warning signals and traceable evidence chains for auditors and accountants. We validate the framework using a simulated audit scenario of a medium-sized manufacturing enterprise in a remote, underdeveloped region of the United States that processes over 100,000 invoices annually. The results demonstrate automated linkage of invoice-level details to relevant regulations (for example, value-added tax deduction rules where applicable), extraction of textual anomalies such as duplicate deductions, detection of approximately 15 percent potential compliance risks under privacy protection, and visualized evidence traceability. Preliminary experiments show a 70 percent reduction in processing time and an error rate below 2 percent, indicating significant audit efficiency gains and the potential to reduce labor costs and disputes. The approach offers scalable implications for national tax administration and can be adapted to other industries to advance intelligent tax governance.
subject: Articles
  topic: section
subject: knowledge graph
  topic: keywords
subject: multimodal AI
  topic: keywords
subject: tax compliance
  topic: keywords
subject: differential privacy
  topic: keywords
creator: Tiantian Zhang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-14
publication date: 2025-09-14
starting page: 247
ending page: 261
doi: 10.71465/fbf384

Early Warning of Financial Distress in Listed Companies: A Machine Learning Approach
type: item
description: Financial distress prediction is critical for maintaining market stability and protecting investor interests. Traditional statistical models often fall short in capturing complex, non-linear patterns in financial data. This study aims to develop an early warning system for financial distress in listed companies by leveraging machine learning algorithms. Using a dataset of financial ratios and market performance indicators from publicly traded firms over a ten-year period, we applied several classification techniques, including logistic regression, support vector machines, and gradient boosting. The gradient boosting model demonstrated superior predictive accuracy, achieving an F1-score of 0.92, significantly outperforming traditional models. Key predictors identified include cash flow volatility, debt-to-equity ratio, and operating margin trends. The findings underscore the potential of machine learning in enhancing the timeliness and reliability of financial distress alerts, offering valuable insights for regulators, investors, and corporate management to take proactive measures.
subject: Articles
  topic: section
subject: Financial Distress Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Early Warning System
  topic: keywords
subject: Corporate Finance
  topic: keywords
creator: Yuchen Chen
creator: Rui Li
creator: Xiaolong Wang
rights: Copyright (c) 2025 Yuchen Chen , Rui Li, Xiaolong Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-04
publication date: 2025-10-04
starting page: 230
ending page: 246
doi: 10.71465/fbf378

Machine Learning Forecasting of Stock Market Indices Based on Macroeconomic Indicators
type: item
description: The accurate forecasting of stock market indices remains a significant challenge in financial research, given the complex interplay of economic variables and market sentiment. This study investigates the predictive power of macroeconomic indicators on major stock market indices using advanced machine learning (ML) techniques. The primary objective is to develop a robust forecasting model that leverages historical data on key macroeconomic variables—such as inflation rates, interest rates, GDP growth, and unemployment figures—to predict future index movements. Using a dataset spanning two decades, we trained and evaluated multiple ML algorithms, including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks. The results demonstrate that ensemble methods, particularly Gradient Boosting, achieve superior predictive accuracy compared to traditional time-series models. Notably, inflation and interest rates emerged as the most influential predictors. These findings underscore the potential of ML-driven approaches to enhance financial decision-making and risk management strategies for investors and policymakers.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Stock Market Forecasting
  topic: keywords
subject: Macroeconomic Indicators
  topic: keywords
subject: Financial Prediction
  topic: keywords
creator: Xiaowei Chen
creator: Yating Wang
creator: Minghao Li
rights: Copyright (c) 2025 Xiaowei Chen , Yating Wang , Minghao Li (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-04
publication date: 2025-10-04
starting page: 212
ending page: 229
doi: 10.71465/fbf376

Cointegration and Dynamic Interactions: A Study on Trade Openness and Economic Growth in China
type: item
description: This research examines the long-term equilibrium relationship between trade openness and China's economic growth, drawing on quarterly data spanning 2002 to 2021. The analysis applies a VAR framework alongside established econometric approaches—Johansen cointegration testing, Granger causality analysis, impulse response functions, and variance decomposition—to systematically assess dynamic interactions among GDP, export-import volumes, and exchange rate fluctuations. Findings empirically confirm three significant cointegration relationships, supporting a stable long-run equilibrium between trade expansion and economic development. The Granger causality tests identify mutual causality between GDP and exchange rate movements, alongside bidirectional effects between exports and the exchange rate. Meanwhile, a unidirectional causal link runs from exports to imports. Variance decomposition further reveals that both exports and imports increasingly account for economic variability over time, with imports demonstrating stronger explanatory power in the extended horizon. These results inform several policy recommendations focused on promoting export growth, refining industrial and trade configurations, and enhancing exchange rate risk frameworks to support sustained economic progress.
subject: Articles
  topic: section
subject: Trade Openness
  topic: keywords
subject: Economic Growth
  topic: keywords
subject: VAR Model
  topic: keywords
subject: Cointegration Analysis
  topic: keywords
subject: Impulse Response Function
  topic: keywords
creator: Wanting Xu
rights: Copyright (c) 2025 Wanting Xu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 206
ending page: 211
doi: 10.71465/fbf365

The Driving Factors of China’s Foreign Trade under Macroeconomic Fluctuations: An Empirical Analysis Based on Bilateral Linkages
type: item
description: Since the launch of the reform and opening-up policy, China has steadily advanced its level of openness to the outside world, and its ties with the global economy have grown increasingly close. During this process, import and export trade has displayed the typical characteristics of market-oriented development. The country has reaped significant benefits from international trade, yet it has also faced adverse shocks from the external environment. With the deepening of global economic integration and China’s accession to the World Trade Organization, the interconnection between domestic and international markets has become stronger. This closer relationship has given rise to new conditions and contradictions, which are intertwined and exert multifaceted effects on national economic development. The specific manifestations include a persistent trade surplus, rising incidents of trade frictions, large inflows of international capital, and upward pressure on the renminbi exchange rate.
subject: Articles
  topic: section
subject: Trade Surplus
  topic: keywords
subject: Trade Frictions
  topic: keywords
subject: International Capital Inflows
  topic: keywords
subject: RMB Exchange Rate
  topic: keywords
creator: Wanting Xu
rights: Copyright (c) 2025 Wanting Xu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 199
ending page: 205
doi: 10.71465/fbf364

Dual-Stage Graph Contrastive Learning for Robust E-Commerce Fraud Detection
type: item
description: E-commerce fraud detection represents one of the most challenging problems in modern digital commerce, requiring sophisticated approaches that can capture complex relational patterns while maintaining robustness against evolving fraud schemes. This paper proposes a novel Dual-Stage Graph Contrastive Learning (DSGCL) framework that leverages graph convolutional networks and advanced contrastive learning techniques to achieve robust fraud detection in e-commerce environments. Our approach employs a two-stage architecture where the first stage utilizes graph neural networks with multi-hop neighborhood aggregation to capture local structural patterns, while the second stage implements contrastive learning with multiple view generation to learn discriminative representations that are robust to fraudulent behavior variations. The framework integrates message-passing mechanisms for effective feature aggregation across user-merchant-transaction networks and employs sophisticated view augmentation strategies to enhance the model's ability to distinguish between legitimate and fraudulent activities. Experimental results on large-scale e-commerce datasets demonstrate that DSGCL achieves 94.7% accuracy with 92.3% F1-score, representing a 7.8% improvement over state-of-the-art baselines while maintaining exceptional robustness against adversarial attacks and novel fraud patterns.
subject: Articles
  topic: section
subject: graph contrastive learning
  topic: keywords
subject: fraud detection
  topic: keywords
subject: e-commerce security
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: representation learning
  topic: keywords
creator: Carlos Ramirez
creator: Holly Peterson
rights: Copyright (c) 2025 Carlos Ramirez , Holly Peterson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-19
publication date: 2025-09-19
starting page: 184
ending page: 198
doi: 10.71465/fbf359

Adversarially Robust Temporal Graph Contrastive Learning for Financial Fraud Detection
type: item
description: Financial fraud detection has become increasingly challenging due to the sophisticated nature of modern fraudulent activities and the dynamic evolution of transaction patterns. This paper proposes a novel Adversarially Robust Temporal Graph Contrastive Learning (ARTGCL) framework that combines temporal graph neural networks with contrastive learning mechanisms to enhance fraud detection capabilities while maintaining robustness against adversarial attacks. Our approach leverages the temporal dynamics of financial transactions represented as evolving graph structures, where nodes represent entities and edges capture transaction relationships over time. The contrastive learning component learns discriminative representations by maximizing agreement between augmented views of the same temporal graph while minimizing similarity with different graphs. To address the vulnerability of graph neural networks to adversarial perturbations, we integrate adversarial training techniques that expose the model to carefully crafted perturbations during training, thereby improving its robustness in real-world scenarios. Extensive experiments on three large-scale financial datasets demonstrate that ARTGCL achieves superior performance compared to state-of-the-art methods, with improvements of 8.5% in F1-score and 12.3% in Area Under the Curve (AUC) while maintaining computational efficiency. The adversarial robustness evaluation shows that our Hybrid Machine Learning Framework (HMLF) sustains robust accuracy improvements from 45% to 85% and reduces attack success rates from 35% to 5% under various attack scenarios, significantly outperforming baseline approaches.
subject: Articles
  topic: section
subject: Temporal graph neural networks
  topic: keywords
subject: contrastive learning
  topic: keywords
subject: financial fraud detection
  topic: keywords
subject: adversarial robustness
  topic: keywords
subject: graph representation learning
  topic: keywords
subject: anti-money laundering
  topic: keywords
creator: Matthew O’Connor
rights: Copyright (c) 2025 Matthew O’Connor (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-19
publication date: 2025-09-19
starting page: 172
ending page: 183
doi: 10.71465/fbf352

Blockchain-Enabled FinTech Platforms for Tokenizing Carbon Capture Outputs in Mechanical Systems
type: item
description: The escalating urgency of climate change mitigation has created unprecedented demand for innovative financial instruments that can effectively monetize carbon capture and storage (CCS) technologies while ensuring transparent, verifiable, and tradeable environmental credits. This research presents a novel blockchain-enabled Financial Technology (FinTech) platform specifically designed for tokenizing carbon capture outputs from mechanical systems, addressing critical gaps in current carbon credit markets through distributed ledger technology (DLT), smart contract automation, and real-time monitoring integration. The proposed platform utilizes Ethereum-compatible blockchain infrastructure to create fungible carbon tokens representing verified CO2 sequestration quantities from industrial mechanical systems including Direct Air Capture (DAC) units, industrial scrubbers, and enhanced weathering reactors. Through comprehensive analysis of 47 pilot implementations across diverse industrial sectors, our platform demonstrates remarkable improvements in carbon credit verification speed by 78%, transaction cost reduction of 45%, and fraud prevention mechanisms that achieve 99.2% accuracy in detecting false carbon capture claims. The system integrates Internet of Things (IoT) sensors with smart contracts to provide automated verification of carbon capture quantities, eliminating manual auditing processes that traditionally required 3-6 months for credit issuance. Real-time tokenization capabilities enable immediate liquidity for carbon credits, with average settlement times reduced from weeks to minutes while maintaining full regulatory compliance with emerging carbon accounting standards. The platform's decentralized architecture supports global carbon market integration, processing over 2.3 million tons of verified carbon capture data across 12 countries with transaction throughput exceeding 10,000 operations per second. Economic analysis reveals substantial market efficiency …
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: FinTech Platforms
  topic: keywords
subject: Carbon Capture Tokenization
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Distributed Ledger Technology
  topic: keywords
subject: Environmental Credits
  topic: keywords
subject: Mechanical Systems
  topic: keywords
subject: IoT Integration
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Yao Ge
rights: Copyright (c) 2025 Yao Ge (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-19
publication date: 2025-09-19
starting page: 158
ending page: 171
doi: 10.71465/fbf66

Real-Time Fraud Detection Using Reinforcement Learning with Dynamic Feature Selection
type: item
description: Real-time fraud detection systems require sophisticated approaches capable of adapting to evolving fraud patterns while maintaining high accuracy and minimal false positive rates under strict latency constraints. Traditional fraud detection methods rely on static feature sets and rule-based systems that cannot adapt effectively to new fraud techniques or changing transaction patterns. The challenge lies in developing systems that can continuously learn optimal feature selection strategies while processing high-volume transaction streams in real-time environments where detection decisions must be made within milliseconds. This study proposes a novel Dynamic Feature Selection Reinforcement Learning (DFS-RL) framework that integrates reinforcement learning algorithms with adaptive feature selection mechanisms to enable real-time fraud detection with continuously evolving feature importance patterns. The framework employs Deep Q-Network (DQN) agents to learn optimal feature selection policies while utilizing ensemble detection models that adapt to selected feature subsets dynamically. The integrated approach enables real-time processing of transaction streams while maintaining detection accuracy through intelligent feature adaptation that responds to changing fraud patterns and system performance feedback. Experimental evaluation using large-scale financial transaction datasets demonstrates that the proposed framework achieves 43% improvement in fraud detection accuracy compared to traditional static feature approaches. The DFS-RL method results in 37% reduction in false positive rates while maintaining average transaction processing latency under 50 milliseconds for real-time requirements. The framework successfully combines adaptive feature selection with high-performance fraud detection, achieving 41% better adaptation to new fraud patterns while supporting real-time transaction processing at scale.
subject: Articles
  topic: section
subject: Real-Time Fraud Detection
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Dynamic Feature Selection
  topic: keywords
subject: Deep Q-Network
  topic: keywords
subject: Financial Transaction Processing
  topic: keywords
subject: Adaptive Systems
  topic: keywords
subject: Machine Learning Security
  topic: keywords
subject: Stream Processing
  topic: keywords
creator: Rajat Ghosh
creator: Linh Truong
rights: Copyright (c) 2025 Rajat Ghosh, Linh Truong (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-13
publication date: 2025-08-13
starting page: 131
ending page: 143
doi: 10.71465/fbf326

Assessing the Impact of Enhanced Capital Adequacy, Elevated Liquidity Ratios, Segregation of Banking Activities, and Restrictions on Bankers' Bonuses on Risk Management in Banks
type: item
description: This study examines the effectiveness of four key regulatory measures—enhanced capital adequacy requirements, elevated liquidity ratios, separation of banking activities, and restrictions on bankers’ bonuses—in mitigating excessive risk-taking and strengthening risk management within the banking sector. Drawing on case studies such as Northern Rock, Bear Stearns, the Glass-Steagall framework, and post-crisis bonus regulations, the paper evaluates both the benefits and limitations of each approach. The findings indicate that while these measures contribute to financial stability by improving loss absorption capacity, reducing systemic risk, and promoting prudent behavior, they also entail potential downsides, including constrained lending, reduced profitability, and unintended risk migration to less-regulated sectors. The analysis underscores that no single policy is sufficient in isolation; rather, a comprehensive, balanced regulatory framework—integrating macro- and micro-prudential measures—is essential to safeguard financial stability without unduly hindering innovation and economic growth.
subject: Articles
  topic: section
subject: Capital adequacy ratio
  topic: keywords
subject: liquidity ratio
  topic: keywords
subject: separation of banking activities
  topic: keywords
subject: bankers’ bonuses
  topic: keywords
subject: risk management
  topic: keywords
subject: financial stability
  topic: keywords
subject: systemic risk
  topic: keywords
subject: banking regulation
  topic: keywords
creator: Yifei Lyu
creator: Liping Wang
rights: Copyright (c) 2025 Yifei Lyu, Liping Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 123
ending page: 130
doi: 10.71465/fbf292

Resilience Evaluation of Supply Chain Configuration in Export-Oriented Enterprises: Indicator Construction and Rebound Prediction Based on Transaction Data
type: item
description: Under the complex and dynamic international market environment, export-oriented enterprises are increasingly exposed to external disruptions, highlighting the critical role of supply chain configuration resilience. This study proposes a data-driven evaluation framework for supply chain resilience, constructed based on actual transaction records. Utilizing Principal Component Analysis (PCA) and Bayesian Structural Time Series (BSTS) models, the framework builds a set of indicators across four core dimensions: procurement concentration, order fulfillment cycle, contract breach response, and customer stickiness. The modeling focuses on two key aspects: the “rebound cycle” and the “configuration flexibility.” Through retrospective testing on order data from 537 export enterprises in the sectors of machinery manufacturing, consumer electronics, and light food processing from 2020 to 2023, the findings show that enterprises with high resilience levels recover from disruptions within an average of 17.4 days—significantly shorter than the industry average of 25.6 days. Furthermore, during periods of market volatility, these enterprises maintained a fulfillment reliability rate exceeding 92.3%. The proposed framework also supports dynamic stratification of supply chain configuration strategies based on rebound speed, offering practical guidance for differentiated strategy optimization. This contributes to enhancing the overall supply chain resilience of export-dominant enterprises.
subject: Articles
  topic: section
subject: Supply chain resilience
  topic: keywords
subject: transaction data analysis
  topic: keywords
subject: rebound cycle prediction
  topic: keywords
subject: configuration flexibility
  topic: keywords
subject: export fulfillment strategy
  topic: keywords
creator: Aiden Foster
creator: Lily Bennett
creator: Carter Hayes
creator: Zoe Mitchell
creator: Julian Rivera
rights: Copyright (c) 2025 Aiden Foster , Lily Bennett , Carter Hayes, Zoe Mitchell, Julian Rivera (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-06
publication date: 2025-08-06
starting page: 106
ending page: 114
doi: 10.71465/fbf291