Banking

Dynamic Risk in the U.S. Banking System: An Analysis of Sentiment, Policy Shocks, and Spillover Effects ArXiv ID: 2601.01783 “View on arXiv” Authors: Haibo Wang, Jun Huang, Lutfu S Sua, Jaime Ortiz, Jinshyang Roan, Bahram Alidaee Abstract The 2023 U.S. banking crisis propagated not through direct financial linkages but through a high-frequency, information-based contagion channel. This paper moves beyond exploration analysis to test the “too-similar-to-fail” hypothesis, arguing that risk spillovers were driven by perceived similarities in bank business models under acute interest rate pressure. Employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with 30-day rolling windows, a method uniquely suited for capturing the rapid network shifts inherent in a panic, we analyze daily stock returns for the four failed institutions and a systematically selected peer group of surviving banks vulnerable to the same risks from March 18, 2022, to March 15, 2023. Our results provide strong evidence for this contagion channel: total system connectedness surged dramatically during the crisis peak, and we identify SIVB, FRC, and WAL as primary net transmitters of risk while their perceived peers became significant net receivers, a key dynamic indicator of systemic vulnerability that cannot be captured by asset-by-asset analysis. We further demonstrate that these spillovers were significantly amplified by market sentiment (as measured by the VIX) and economic policy uncertainty (EPU). By providing a clear conceptual framework and robust empirical validation, our findings confirm the persistence of systemic risks within the banking network and highlight the importance of real-time monitoring in strengthening financial stability. ...

Modeling Bank Systemic Risk of Emerging Markets under Geopolitical Shocks: Empirical Evidence from BRICS Countries ArXiv ID: 2512.20515 “View on arXiv” Authors: Haibo Wang Abstract The growing economic influence of the BRICS nations requires risk models that capture complex, long-term dynamics. This paper introduces the Bank Risk Interlinkage with Dynamic Graph and Event Simulations (BRIDGES) framework, which analyzes systemic risk based on the level of information complexity (zero-order, first-order, and second-order). BRIDGES utilizes the Dynamic Time Warping (DTW) distance to construct a dynamic network for 551 BRICS banks based on their strategic similarity, using zero-order information such as annual balance sheet data from 2008 to 2024. It then employs first-order information, including trends in risk ratios, to detect shifts in banks’ behavior. A Temporal Graph Neural Network (TGNN), as the core of BRIDGES, is deployed to learn network evolutions and detect second-order information, such as anomalous changes in the structural relationships of the bank network. To measure the impact of anomalous changes on network stability, BRIDGES performs Agent-Based Model (ABM) simulations to assess the banking system’s resilience to internal financial failure and external geopolitical shocks at the individual country level and across BRICS nations. Simulation results show that the failure of the largest institutions causes more systemic damage than the failure of the financially vulnerable or dynamically anomalous ones, driven by powerful panic effects. Compared to this “too big to fail” scenario, a geopolitical shock with correlated country-wide propagation causes more destructive systemic damage, leading to a near-total systemic collapse. It suggests that the primary threats to BRICS financial stability are second-order panic and large-scale geopolitical shocks, which traditional risk analysis models might not detect. ...

Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis ArXiv ID: 2502.07806 “View on arXiv” Authors: Unknown Abstract The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA). By leveraging RTDPA, the proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation. While this work explores the potential of integrating quantum methods with classical deep learning for risk assessment, it focuses on the feasibility and performance of this hybrid framework rather than claiming transformative industry-wide impacts. The findings offer insights into how quantum techniques can complement traditional financial analysis, paving the way for further advancements in predictive modeling for credit risk. ...

Designing an attack-defense game: how to increase robustness of financial transaction models via a competition ArXiv ID: 2308.11406 “View on arXiv” Authors: Unknown Abstract Banks routinely use neural networks to make decisions. While these models offer higher accuracy, they are susceptible to adversarial attacks, a risk often overlooked in the context of event sequences, particularly sequences of financial transactions, as most works consider computer vision and NLP modalities. We propose a thorough approach to studying these risks: a novel type of competition that allows a realistic and detailed investigation of problems in financial transaction data. The participants directly oppose each other, proposing attacks and defenses – so they are examined in close-to-real-life conditions. The paper outlines our unique competition structure with direct opposition of participants, presents results for several different top submissions, and analyzes the competition results. We also introduce a new open dataset featuring financial transactions with credit default labels, enhancing the scope for practical research and development. ...

Company2Vec – German Company Embeddings based on Corporate Websites ArXiv ID: 2307.09332 “View on arXiv” Authors: Unknown Abstract With Company2Vec, the paper proposes a novel application in representation learning. The model analyzes business activities from unstructured company website data using Word2Vec and dimensionality reduction. Company2Vec maintains semantic language structures and thus creates efficient company embeddings in fine-granular industries. These semantic embeddings can be used for various applications in banking. Direct relations between companies and words allow semantic business analytics (e.g. top-n words for a company). Furthermore, industry prediction is presented as a supervised learning application and evaluation method. The vectorized structure of the embeddings allows measuring companies similarities with the cosine distance. Company2Vec hence offers a more fine-grained comparison of companies than the standard industry labels (NACE). This property is relevant for unsupervised learning tasks, such as clustering. An alternative industry segmentation is shown with k-means clustering on the company embeddings. Finally, this paper proposes three algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric peer-firm identification. ...

Impacts, Challenges and Trends of Digital Transformation in the Banking Sector ArXiv ID: ssrn-3835433 “View on arXiv” Authors: Unknown Abstract Driven by the 2020 pandemic’s work-at-home mandates, the future of work in banking and finance may be in the midst of disruptive change. The digital transformat Keywords: Digital Transformation, Banking, Work at Home, Future of Work, Financial Services, Banking / Financial Services Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper discusses broad digital transformation trends and impacts in banking, lacking advanced mathematical formulas or quantitative models; empirical evidence appears to be descriptive rather than data-driven or backtested. flowchart TD A["Research Goal<br>Understand DT impacts on Banking<br>post-2020 pandemic"] --> B["Methodology<br>Literature Review &<br>Case Study Analysis"] B --> C{"Input Data"} C --> D["Financial Services Industry Reports"] C --> E["Remote Work / Digital Adoption Statistics"] C --> F["Employee & Customer Satisfaction Surveys"] D & E & F --> G["Analysis<br>Thematic & Comparative Analysis<br>of Trends & Challenges"] G --> H["Key Findings & Outcomes<br>1. Accelerated Digital Adoption<br>2. Hybrid Work Models<br>3. Cybersecurity Challenges<br>4. Future of Banking Workforce"]

Measuring Financial Inclusion: The Global Findex Database ArXiv ID: ssrn-2043012 “View on arXiv” Authors: Unknown Abstract This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 econom Keywords: Financial Inclusion, Global Findex, Banking, Emerging Markets, General (Financial Inclusion) Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper is an empirical analysis of a massive, newly collected survey dataset (Global Findex) across 148 economies, focusing on descriptive statistics and policy implications rather than advanced mathematical modeling or derivations. flowchart TD A["Research Goal<br>Measure & analyze global financial inclusion"] --> B["Data Collection<br>Global Findex Database<br>148 economies, ~150k adults"] B --> C["Methodology<br>Define indicators & stratified sampling"] C --> D["Computation<br>Statistical analysis of inclusion patterns"] D --> E["Key Findings<br>Usage gaps, barriers, & policy insights"]

Introducing Islamic Banks into Coventional Banking Systems ArXiv ID: ssrn-1007924 “View on arXiv” Authors: Unknown Abstract Over the last decade, Islamic banking has experienced global growth rates of 10-15 percent per annum, and has been moving into an increasing number of conventio Keywords: Islamic banking, Sharia-compliant finance, financial intermediation, ethical investing, growth rates, Banking Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is primarily a descriptive policy overview discussing regulatory and institutional steps for integrating Islamic banking, lacking advanced mathematical derivations or statistical analysis. flowchart TD A["Research Goal:<br>Assess Integration of Islamic Banks into Conventional Systems"] --> B["Key Methodology:<br>Comparative Analysis of Financial Stability & Performance"] B --> C["Data Inputs:<br>Global Growth Rates (10-15% p.a.) &<br>Sharia-Compliant Portfolios"] C --> D["Computational Process:<br>Statistical Modeling of Ethical vs. Conventional Intermediation"] D --> E["Key Findings:<br>Feasible Integration with Enhanced Stability &<br>Ethical Investment Outcomes"]