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: Belief-Aware Agentic Reinforcement Learning for Web Decision Models under Multi-Cost and Failure Risk Constraints
items: Volution of Sino–US Bilateral Economic Relations under Trade Frictions
items: TailRisk-Trans: A Transformer-Based Dynamic Tail-Risk Prediction Model with Extreme-Event–Aware Attention for Financial Markets
items: AI-Integrated Decision Support System for Real-Time Market Growth Forecasting and Multi-Source Content Diffusion Analytics
items: Algorithm of Trust: The Causal Impact of AI Personalization on Consumer Adoption in Mobile Messaging Payments
items: Dynamic Spillover of Negative Public Opinion on P2P Liquidity Risk Based on Sentiment Analysis
items: Capital Scheduling Decisions in Digital Supply Chain Finance based on Multi-Stage Stochastic Linear Programming
items: Financial Report Entity-Relation Extraction Combining BiLSTM-CRF and FGM Adversarial Training
items: Reinforcement Learning Based Demand State Modeling and Dynamic Personalization for Digital Retail
items: Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization
items: The Awkward Longan
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

Belief-Aware Agentic Reinforcement Learning for Web Decision Models under Multi-Cost and Failure Risk Constraints
type: item
description: Web interaction is inherently partially observable, as critical task-relevant information is distributed across multiple pages, dynamic UI elements, and delayed system feedback. This study formulates web agent decision-making as a belief-space constrained MDP, where the agent maintains a probabilistic belief over hidden task states and latent failure conditions. A belief-aware agentic reinforcement learning model is proposed that jointly updates task belief and failure-risk belief while optimizing task success under multiple cumulative cost budgets, including interaction steps, latency, and external tool usage. Failure risk is modeled as a belief-dependent hazard that evolves with both observed UI transitions and unobserved system states. The policy is trained using belief-conditioned value estimation and cost-regularized returns. Experiments are designed on a benchmark of approximately 1,200 web tasks across 50 website templates with partial observability induced by delayed confirmations and hidden irreversible actions. Results are evaluated in terms of success rate, belief calibration error, average cost per success, and failure incidence under fixed budgets. The proposed framestudy demonstrates improved robustness in long-horizon tasks where incorrect belief updates frequently lead to catastrophic decisions.
subject: Articles
  topic: section
subject: Belief-space planning
  topic: keywords
subject: partially observable MDP
  topic: keywords
subject: agentic reinforcement learning
  topic: keywords
subject: web agents
  topic: keywords
subject: failure risk modeling
  topic: keywords
subject: multi-cost constraints
  topic: keywords
creator: Arjun K. Singh
creator: Priya Menon
creator: Karthik Raman
rights: Copyright (c) 2026 Arjun K. Singh, Priya Menon, Karthik Raman (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 191
ending page: 200
doi: 10.71465/fbf755

Volution of Sino–US Bilateral Economic Relations under Trade Frictions
type: item
description: This paper analyzes the evolution of China–US economic relations from complementarity to competition, with a primary focus on the 2017-2023 trade friction period. Methodologically, it combines a targeted literature synthesis with descriptive evidence on the pre-friction trade landscape (2002-2016) and a multi-indicator framework spanning 2007–2023. At the macro level, a four-dimension competitiveness system—GCSI, GCWI, GCOI, and GCTI—captures shifts in strengths, weaknesses, opportunities, and threats. At the industry level, three sectors—high-tech manufacturing (HTM), strategic resource industries (SRI), and modern services (MSI)—are evaluated via the Import Input Dependency Index (IIDI) and the Industrial Position Transfer Index (IPTI) to track dependence and positional dynamics in global value chains (GVCs). Results indicate a clear turning point in 2018–2020: China's competitiveness strengthened (higher GCSI, higher GCOI, lower GCWI) alongside rising external threats (higher GCTI), while the US remained relatively stable with marginal declines and elevated threat signals. The US exhibits consistently higher IIDI across all sectors—especially a pronounced rise in MSI—whereas China's HTM/MSI dependence declined and then stabilized. IPTI points to greater US dynamism in GVC repositioning and greater stability for China. Consistent with prior research, tariff shocks transmit through price pass-through, general-equilibrium reallocations, and GVC linkages, reducing welfare and straining exposed local labor markets, while catalyzing regionalization, diversification and nearshoring.
subject: Articles
  topic: section
subject: China-US trade frictions
  topic: keywords
subject: Global value chains
  topic: keywords
subject: Competitiveness
  topic: keywords
subject: Industrial dependence
  topic: keywords
subject: Supply chain resilience
  topic: keywords
creator: Chen Jin
creator: Haifeng Xu
rights: Copyright (c) 2026 Chen Jin, Haifeng Xu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-29
publication date: 2026-03-29
starting page: 183
ending page: 190
doi: 10.71465/fbf746

TailRisk-Trans: A Transformer-Based Dynamic Tail-Risk Prediction Model with Extreme-Event–Aware Attention for Financial Markets
type: item
description: Accurate modeling of tail risks such as market crashes, volatility spikes, and extreme downside events is crucial for portfolio management, quantitative risk control, and regulatory stress testing. Traditional econometric models, including GARCH and EVT-based hybrids, often struggle to capture the nonlinear dependencies and regime shifts inherent in modern high-frequency markets. To address these limitations, we propose TailRisk-Trans, a unified Transformer-based tail-risk prediction framework designed to dynamically forecast Value-at-Risk (VaR), Expected Shortfall (ES), and Conditional Value-at-Risk (CVaR). The model incorporates four key components: a financial data preprocessing layer that integrates microstructure features, derivative-implied risks, and macroeconomic factors; a Market Transformer Encoder capable of capturing long-range temporal dependencies; a Tail-Risk Prediction Head that jointly predicts multiple risk metrics; and an Extreme-Event-Aware Attention mechanism that adaptively increases sensitivity to volatility spikes and abnormal distributional shifts. Experimental evaluations on multi-market datasets-including equity indices, index futures, and volatility indices-demonstrate the superior performance of TailRisk-Trans under both normal and turbulent market regimes. Compared with the strongest baseline model, Transformer-TS, TailRisk-Trans reduces the 99 percent Value-at-Risk (VaR) violation rate from 4.12 percent to 3.47 percent , representing a 15.8 percent improvement in tail-event compliance. In addition, TailRisk-Trans attains the lowest quantile loss (2.684) and the most favorable Expected Shortfall score (3.892), confirming enhanced sensitivity to shifts in tail-risk distributions. These results highlight the practical applicability of TailRisk-Trans for quantitative trading strategies, real-time risk monitoring, and regulatory early-warning systems.
subject: Articles
  topic: section
subject: Tail Risk
  topic: keywords
subject: Transformer
  topic: keywords
subject: VaR
  topic: keywords
subject: ES
  topic: keywords
subject: CVaR
  topic: keywords
subject: Extreme Events
  topic: keywords
subject: Financial Risk Modeling
  topic: keywords
subject: Time-Series Forecasting
  topic: keywords
creator: Tangtang Wang
creator: Zishan Bai
rights: Copyright (c) 2026 Tangtang Wang, Zishan Bai (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 172
ending page: 182
doi: 10.71465/fbf742

AI-Integrated Decision Support System for Real-Time Market Growth Forecasting and Multi-Source Content Diffusion Analytics
type: item
description: The rapid proliferation of AI-generated content (AIGC) has reshaped the dynamics of digital marketing and online consumer behavior. However, predicting the diffusion trajectory and market impact of such content remains challenging due to data heterogeneity, non-linear propagation mechanisms, and evolving consumer interactions. This study proposes an AI-driven Decision Support System (DSS) that integrates multi-source data—including social media streams, marketing expenditure records, consumer engagement logs, and sentiment dynamics—using a hybrid Graph Neural Network (GNN) and Temporal Transformer framework. The model jointly learns the content diffusion structure and temporal influence evolution through a dual-channel architecture, while causal inference modules disentangle the effects of marketing stimuli on return on investment (ROI) and market visibility. Experiments on large-scale real-world datasets collected from multiple online platforms such as Twitter, TikTok, and YouTube advertising show that our system outperforms existing baselines in all six metrics. The proposed DSS enhances marketing decisions by providing interpretable real-time insights into AIGC driven content dissemination and market growth patterns.
subject: Articles
  topic: section
subject: AI-generated content (AIGC)
  topic: keywords
subject: Decision Support System (DSS)
  topic: keywords
subject: Graph Neural Network (GNN)
  topic: keywords
subject: Temporal Transformer
  topic: keywords
subject: Multi-source data integration
  topic: keywords
subject: Market forecasting
  topic: keywords
creator: Ziqing Yin
creator: Xuanjing Chen
creator: Xi Zhang
rights: Copyright (c) 2026 Ziqing Yin, Xuanjing Chen, Xi Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 162
ending page: 171
doi: 10.71465/fbf726

Algorithm of Trust: The Causal Impact of AI Personalization on Consumer Adoption in Mobile Messaging Payments
type: item
description: The convergence of artificial intelligence and mobile messaging platforms has created a new paradigm for digital financial services, yet the mechanisms through which AI-driven personalization influences consumer adoption of payment features remain poorly understood. This paper investigates the causal impact of personalized AI recommendations on user adoption of mobile messaging payment services, with a specific focus on the mediating role of consumer trust. We leverage a natural experiment from a major messaging platform's phased rollout of an AI payment assistant to implement a difference-in-differences research design. Using a panel dataset of fifteen thousand users observed over eighteen months, we estimate the treatment effect of AI personalization on payment adoption rates while accounting for heterogeneous responses across user segments. Our findings reveal that AI personalization significantly increases payment adoption by twenty-three point four percent, with trust-based mechanisms explaining approximately forty-one percent of this total effect. Critically, we uncover substantial heterogeneity in treatment effects: users with prior trust in the platform exhibit adoption increases of thirty-one point two percent, while those with low initial trust show muted responses of only eight point seven percent, suggesting that AI personalization amplifies existing trust rather than building it de novo. We also find that recommendation transparency moderates the effect, with explainable AI features enhancing adoption among skeptical users. These results provide causal evidence for the algorithm of trust hypothesis, demonstrating that AI personalization functions through trust channels and that its effectiveness is conditional on pre-existing user-platform relationships. The findings offer actionable insights for designing AI systems that foster financial inclusion while managing the heterogeneous trust dynamics inherent in digital payment adoption.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Personalization
  topic: keywords
subject: Consumer Trust
  topic: keywords
subject: Mobile Payments
  topic: keywords
subject: Difference-in-Differences
  topic: keywords
creator: Yi Han
rights: Copyright (c) 2026 Yi Han (Author) https://creativecommons.org/licenses/by/4.0/
date: 2026-02-20
publication date: 2026-02-20
starting page: 152
ending page: 161
doi: 10.71465/fbf718

Dynamic Spillover of Negative Public Opinion on P2P Liquidity Risk Based on Sentiment Analysis
type: item
description: The peer-to-peer (P2P) lending industry has emerged as a significant component of the alternative finance landscape, yet it remains plagued by inherent fragility and information asymmetry. This paper investigates the dynamic spillover effects of negative public opinion on the liquidity risk of P2P platforms, employing advanced sentiment analysis techniques and econometric spillover indices. By constructing a comprehensive daily index of negative sentiment derived from web-scraped data across major investor forums and news outlets, we analyze the transmission mechanisms through which adverse information impacts platform funding flows and withdrawal behaviors. We utilize textual mining to quantify soft information and apply a generalized vector autoregression framework to capture time-varying connectedness. Our findings reveal that negative public opinion exerts a substantial, immediate, and asymmetric impact on liquidity risk, with the intensity of the spillover varying significantly depending on the regulatory environment and the specific nature of the negative news. The results suggest that investor panic, fueled by social contagion, creates a self-fulfilling prophecy that exacerbates liquidity crunches. Furthermore, we identify a distinct lag structure where the volatility transmission from sentiment to liquidity persists even after the initial shock subsides. These insights provide critical implications for platform managers regarding crisis communication and for regulators aiming to stabilize the fintech ecosystem against systemic risks associated with information contagion.
subject: Articles
  topic: section
subject: P2P Lending
  topic: keywords
subject: Liquidity Risk
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Information Spillover
  topic: keywords
subject: Behavioral Finance.
  topic: keywords
creator: Javier Lopez
creator: Matteo Bianchi
creator: Robert Williams
creator: Sarah Davis
rights: Copyright (c) 2026 Javier Lopez, Matteo Bianchi, Robert Williams, Sarah Davis (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-26
publication date: 2026-02-26
starting page: 143
ending page: 151
doi: 10.71465/fbf695

Capital Scheduling Decisions in Digital Supply Chain Finance based on Multi-Stage Stochastic Linear Programming
type: item
description: The rapid evolution of financial technologies has transformed traditional supply chain management into a digitally integrated ecosystem, enabling real-time visibility and data-driven decision-making. This paper addresses the complex problem of capital scheduling within digital supply chain finance, where liquidity management is subject to significant uncertainties regarding market demand, repayment behaviors, and interest rate fluctuations. Unlike traditional static approaches, we propose a Multi-Stage Stochastic Linear Programming model that dynamically adjusts financing and repayment decisions over multiple time horizons. By leveraging the granular data availability inherent in digital platforms, the proposed framework allows financial planners to optimize working capital allocation while mitigating liquidity risks. We construct a scenario tree to represent the evolution of uncertain parameters and employ a decomposition algorithm to solve the resulting large-scale optimization problem. The study demonstrates that digital integration, when coupled with stochastic optimization, significantly outperforms deterministic planning methods by reducing the cost of capital and improving solvency ratios under volatile market conditions. The findings provide theoretical contributions to the intersection of operations research and financial engineering, offering practical guidelines for liquidity providers and supply chain managers operating in uncertain environments.
subject: Articles
  topic: section
subject: Supply Chain Finance
  topic: keywords
subject: Stochastic Programming
  topic: keywords
subject: Capital Scheduling
  topic: keywords
subject: Liquidity Management.
  topic: keywords
creator: Aarav Sharma
creator: Sarah E. Thompson
rights: Copyright (c) 2026 Aarav Sharma, Sarah E. Thompson (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 133
ending page: 142
doi: 10.71465/fbf694

Financial Report Entity-Relation Extraction Combining BiLSTM-CRF and FGM Adversarial Training
type: item
description: The rapid digitization of financial markets has resulted in an explosion of unstructured financial data, primarily in the form of textual reports, earnings calls, and regulatory filings. Extracting structured knowledge from these documents is critical for automated risk assessment, quantitative investment strategies, and market surveillance. However, financial texts differ significantly from general domain corpora due to high terminological density, complex nested sentence structures, and subtle semantic dependencies. Traditional named entity recognition and relation extraction models often suffer from overfitting when applied to limited labeled financial datasets, leading to poor generalization on unseen data. This paper proposes a robust entity-relation extraction framework that integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a Conditional Random Field (CRF) layer, augmented by Fast Gradient Method (FGM) adversarial training. The BiLSTM layer captures long-range semantic dependencies, while the CRF layer ensures the validity of the predicted tag sequences. Crucially, the FGM adversarial training mechanism introduces perturbations to the embedding layer during training, effectively regularizing the model and enhancing its robustness against noise and data sparsity. Experimental results demonstrate that the proposed model achieves superior performance in terms of precision, recall, and F1-score compared to baseline methods, particularly in identifying complex financial entities and their interrelations.
subject: Articles
  topic: section
subject: Financial Text Mining
  topic: keywords
subject: Entity Extraction
  topic: keywords
subject: BiLSTM-CRF
  topic: keywords
subject: Adversarial Training
  topic: keywords
subject: Fast Gradient Method
  topic: keywords
creator: Robert J. Miller
creator: Sarah E. Hamilton
rights: Copyright (c) 2026 Robert J. Miller, Sarah E. Hamilton (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-20
publication date: 2026-02-20
starting page: 123
ending page: 132
doi: 10.71465/fbf693

Reinforcement Learning Based Demand State Modeling and Dynamic Personalization for Digital Retail
type: item
description: Personalization in digital retail involves repeated interventions whose effects unfold over time, with delayed purchase responses and diminishing returns caused by excessive contact. To address this setting, personalized strategy selection is formulated as a Markov decision process in which customer demand evolves across daily decision epochs. Multi-modal behavioral sequences—browsing, add-to-cart, purchase, return, and responses to marketing touchpoints—are encoded into latent demand states using representation learning to obtain compact state vectors from high-dimensional logs. Strategic actions are defined over promotion intensity, content type, and contact frequency, and the learning objective combines conversion revenue with explicit penalties for promotion cost and fatigue-related negative responses. Experiments use a 12-month dataset containing 960,418,327 behavioral records from 2,104,673 customers, trained offline and evaluated through online playback under identical operational constraints across baselines. Relative to rule-based and supervised alternatives, the learned policy improves conversion from 3.72% to 3.88% (+4.3% relative) and increases 30-day LTV from $12.41 to $13.25 (+6.8% relative). Ineffective outreach is reduced, with the share of contacted instances without attributed purchase decreasing from 78.6% to 74.1%. The study support sequence-level optimization of interventions when cost and fatigue must be controlled alongside revenue.
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Sequential Decision Making
  topic: keywords
subject: Demand State Modeling
  topic: keywords
subject: Dynamic Personalization
  topic: keywords
subject: Marketing Fatigue
  topic: keywords
subject: Customer Lifetime Value
  topic: keywords
subject: Offline Policy Learning
  topic: keywords
creator: Ying Lin
creator: Yao Yao
creator: Chao He
rights: Copyright (c) 2026 Ying Lin, Yao Yao, Chao He (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-26
publication date: 2026-02-26
starting page: 114
ending page: 122
doi: 10.71465/fbf654

Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization
type: item
description: The convergence of social networking and electronic commerce has given rise to the social e-commerce paradigm, where content creators serve as the primary drivers of consumer engagement and purchase decisions. However, this ecosystem faces a critical tension between the need for high-precision ad targeting to sustain monetization and the increasingly stringent requirements for user privacy preservation. Traditional centralized recommendation systems require the aggregation of massive user behavioral datasets, creating significant risks of data leakage and violating emerging regulatory frameworks. To address this challenge, we propose a novel framework titled Fed-ZKC (Federated Zero-Knowledge Creator). This architecture synergizes Federated Learning (FL) with Zero-Knowledge Proofs (ZKP) to enable privacy-preserving ad targeting while ensuring verifiable monetization attribution for creators. In our system, user preference models are trained locally on edge devices to prevent raw data transmission, while a cryptographic verification layer ensures that ad interactions are genuine without revealing user identities to the platform or the creators. Extensive experiments conducted on large-scale real-world datasets demonstrate that Fed-ZKC achieves recommendation accuracy comparable to centralized baselines while reducing privacy leakage risks by orders of magnitude. Furthermore, the implementation of succinct non-interactive arguments of knowledge (zk-SNARKs) introduces minimal computational overhead, making the protocol feasible for deployment on modern mobile processors.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Zero-Knowledge Proofs
  topic: keywords
subject: Social E-Commerce
  topic: keywords
subject: Ad Targeting
  topic: keywords
subject: Privacy-Preserving Computation
  topic: keywords
creator: Xiongsheng Yi
rights: Copyright (c) 2026 Xiongsheng Yi (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-24
publication date: 2026-02-24
starting page: 102
ending page: 113
doi: 10.71465/fbf653

The Awkward Longan
type: item
description: This article analyses the weakening market position of China’s longan industry in a context where the supply chain remains relatively stable. Over time, longan has relied on an established production base configuration and a supplementary mechanism of cross border trade, thereby forming a comparatively robust system of production and distribution. However, supply stability has not translated into sustained gains in market competitiveness. Instead, amid consumption upgrading and an intensified health orientation, longan has increasingly revealed a lack of alignment with demand side requirements. The challenges facing longan do not stem from insufficient production capacity, but from multiple mismatches triggered by a restructuring of consumer value systems. Within a health discourse that prioritises sugar control and low burden choices, its high sweetness image can readily induce risk avoidance. At the level of functional experience, its perceived value struggles to gain advantage across the dimensions of price, convenience, and perceived eatability. At the social level, the absence of salient category symbolism and a compelling gifting narrative constrains its role in identity expression and scenario based consumption. Meanwhile, existing research has largely focused on supply efficiency and industrial scale, paying insufficient attention to shifts in consumer preferences and meaning construction, and thus overlooking potential medium to long term demand risks. On this basis, this article argues that the longan industry should shift from a scale oriented approach to a value oriented approach. By reconstructing health oriented communication, optimising consumer experience, and reshaping category image, the industry can re establish fit with contemporary consumption logic.
subject: Articles
  topic: section
subject: Longan
  topic: keywords
subject: Supply Chain Stability
  topic: keywords
subject: Market Position Imbalance
  topic: keywords
subject: Demand Side Transformation
  topic: keywords
subject: Value Alignment
  topic: keywords
creator: Xiao Mengfei
creator: Gan Weixiang
rights: Copyright (c) 2026 Xiao Mengfei, Gan Weixiang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-15
publication date: 2026-02-15
starting page: 88
ending page: 101
doi: 10.71465/fbf644

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