An Explainable AI framework for credit evaluation and analysis

This study develops a literature-driven conceptual framework for energy-burden-aware underwriting within the context of U.S. residential decarbonization finance. It addresses a critical and growing disconnect between the policy-driven urgency to decarbonize residential buildings and the financial constraints faced by households most in need of retrofit investments. Notably, those households that stand to benefit the most from energy-efficiency improvements are often underserved by traditional underwriting models. Conventional lending frameworks prioritize stable income streams, established credit histories, and property-based collateral signals, whereas energy-burdened households—characterized by high utility costs, inefficient housing conditions, and constrained liquidity—exhibit affordability dynamics that are poorly captured by these models but are highly relevant for socially beneficial investment. Drawing upon interdisciplinary literature spanning energy burden, building decarbonization, alternative data underwriting, explainable artificial intelligence, and algorithmic accountability, this paper proposes an AI-enabled underwriting framework that integrates utility-bill histories, housing attributes, retrofit cost-benefit dynamics, and consumer protection considerations. Importantly, the framework emphasizes transparency and interpretability, avoiding reliance on opaque risk-scoring mechanisms. Methodologically, the study employs a structured integrative review of academic and policy-oriented sources, synthesizing insights into a multimodal framework encompassing data governance, feature engineering, model development, explainability outputs, fairness diagnostics, and financing pathway design. The contributions of this research are threefold. First, it reconceptualizes utility payment data from a signal of financial distress into a meaningful input for transition finance. Second, it establishes a direct linkage between household energy burden and underwriting design principles. Third, it outlines governance and accountability conditions under which AI-assisted retrofit financing can advance decarbonization objectives without reinforcing systemic exclusion. Given the absence of original empirical data, the findings are presented as a conceptual architecture, evaluation framework, and forward-looking research agenda rather than as empirical validation. The proposed framework is intended to guide future implementation and testing by financial institutions, utilities, public green banks, and housing policy stakeholders. paper develops a literature-based conceptual framework for energy-burden-aware underwriting in U.S. residential decarbonization finance. The study addresses a widening disconnect between the climate-policy imperative to decarbonize homes and the financial reality that many households most likely to benefit from retrofit investment are also the least well served by conventional underwriting. Existing lending models privilege stable income, established credit files, and property value signals, whereas the households facing high utility costs, inefficient housing, and limited liquidity often present precisely the affordability patterns that make retrofit investment socially valuable. Drawing on scholarship on energy burden, building decarbonization, alternative-data underwriting, explainable artificial intelligence, and algorithmic accountability, the paper proposes an AI framework that uses utility-bill histories, housing characteristics, retrofit economics, and consumer-protection rules to assess payment feasibility without reducing underwriting to opaque risk scoring. Methodologically, the paper uses a structured integrative review academic and policy sources and synthesizes them into a multimodal framework comprising data governance, feature design, model development, explanation outputs, fairness diagnostics, and financing-channel selection. The paper’s contribution is threefold: it reframes utility bills from a distress signal into a transition-finance input; it links household energy burden to underwriting design; and it specifies governance conditions under which AI-assisted retrofit finance may advance decarbonization without reproducing exclusion. Because no original dataset is analyzed, the results are presented as a conceptual model, evaluation logic, and research agenda rather than empirical claims. The framework is intended to support future testing by lenders, utilities, public green banks, and housing-policy practitioners.

The purpose of this article is twofold: first, we show how algorithms have become increasingly central to financial credit scoring; second, we draw on this to further develop the anthropological study of algorithmic governance. As such, we describe the literature on credit scoring and then discuss ethnographic examples from two regulatory and commercial contexts: the US and Denmark. From these empirical cases, we carve out main developments of algorithmic governance in credit scoring and elucidate social and cultural logics behind algorithmic governance tools. Our analytical framework builds on critical algorithm studies and anthropological studies where money and payment infrastructures are viewed as embedded in their specific cultural contexts (Bloch and Parry 1989; Maurer 2015). The comparative analysis shows how algorithmic credit scoring takes different forms hence raising different issues in the two cases. Danish banks seem to have developed a system of intensive, yet hidden credit scoring based on surveillance and harvesting of behavioural data, which, however, due to GDPR takes place in restricted silos. Credit scores are hidden to customers, and therefore there has been virtually no public debate regarding the algorithmic models behind scores. In the US, fewer legal restrictions on data trading combined with both widespread and visible credit scoring has led to the development of a credit data market and widespread use of credit scoring by ‘affiliation’ on the one hand, but also to increasing public and political critique on scoring models on the other.

Explainable AI (XAI) frameworks are becoming essential in many areas, including the medical field, as they help us to understand AI decisions, increasing clinical trust and improving patient care. This research presents a robust and comprehensive Explainable AI framework. To classify images from the BloodMNIST and Raabin‐WBC datasets, various pre‐trained convolutional neural network (CNN) architectures: the VGG, the ResNet, the DenseNet, the EfficientNet, the MobileNet variants, the SqueezeNet, and the Xception are implemented both individually and in combination with SpinalNet. For parameter analysis, four models, VGG16, VGG19, ResNet50, and ResNet101, were combined with SpinalNet. Notably, these SpinalNet hybrid models significantly reduced the model parameters while maintaining or even improving the model accuracy. For example, the VGG 16 + SpinalNet shows a 40.74% parameter reduction and accuracy of 98.92% (BloodMnist) and 98.32% (Raabin‐WBC). Similarly, the combinations of VGG19, ResNet50, and ResNet101 with SpinalNet resulted in weight parameter reductions by 36.36%, 65.33%, and 52.13%, respectively, with improved accuracy for both datasets. These hybrid SpinalNet models are highly efficient and well‐suited for resource‐limited environments. The authors have developed a dynamic model selection framework. This framework optimally selects the best models based on prediction scores, prioritizing lightweight models in cases of ties. This method guarantees that for every input, the most effective model is used, which results in higher accuracy as well as better outcomes. Explainable AI (XAI) techniques: Local Interpretable Model‐agnostic Explanations (LIME), SHapley Additive ExPlanations (SHAP), and Gradient‐weighted Class Activation Mapping (Grad‐CAM) are implemented. These help us to understand the key features that influence the model predictions. By combining these XAI methods with dynamic model selection, this research not only achieves excellent accuracy but also provides useful insights into the elements that influence model predictions.

The purpose of this study was to summarize our experience with off-the-shelf anterior shell carbon fiber ankle-foot orthoses (CFAFOs) prescribed to adult neuromuscular patients in an outpatient clinic. We studied ambulatory patients who were seen in Muscular Dystrophy Association or amyotrophic lateral sclerosis clinics between 2011 and 2014 and prescribed anterior shell CFAFOs. Charts were reviewed with attention to diagnosis, satisfaction with use, and reasons for acceptance or rejection. We included individuals who were currently using AFOs and those being prescribed AFOs for the first time. We were especially interested in reasons for acceptance or rejection of the orthosis. Two hundred eighty-three charts were reviewed. Of these, 109 of 123 (89%) patients were satisfied or extremely satisfied with the anterior shell CFAFOs, including 38 who had previously used other styles. Anterior shell CFAFOs should be considered for most neuromuscular patients with distal leg weakness. Muscle Nerve 55: 202-205, 2017.

Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A lot of emphasis has recently been paid to the development of methods for visualizing, explaining, and interpreting deep learning models. The situation is substantially different in safety-critical applications. The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case. Significantly, when single bad decisions can endanger human life and health (e.g., autonomous driving, medical domain) or result in significant monetary losses (e.g., algorithmic trading), depending on an unintelligible data-driven system may not be an option. This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer, e-commerce, or entertainment industries. Explainability is the term introduced in the preceding years. The AI model’s black box nature will become explainable with these frameworks. Especially in the medical domain, diagnosing a particular disease through AI techniques would be less adapted for commercial use. These models’ explainable natures will help them commercially in diagnosis decisions in the medical field. This paper explores the different frameworks for the explainability of AI models in the medical field. The available frameworks are compared with other parameters, and their suitability for medical fields is also discussed.

This article investigates the dynamic connectedness between non-fungible tokens (NFTs), cryptocurrencies and conventional currencies using an innovative approach to R 2 decomposition. We test volatility spillovers and the transmission of shocks across assets by decomposing the connectedness into its contemporaneous and lagged components. The database ranges from March 2020 to December 2023, considering several global situations: the COVID-19 pandemic, the 2021 cryptocurrency bubble, the war in Ukraine and the crash of cryptocurrencies in 2022. Our results suggest that the cryptocurrency and NFTs markets are the primary net volatility emitters, having a significant and immediate impact on the traditional currency market. In contrast, most traditional currencies act as net receivers, primarily adopting a shock-absorbing behaviour rather than shock transmission. The results show that the majority of volatility spillovers are contemporaneous, accompanied by small lagged effects. This research provides important insights into the increasingly essential role of digital assets in the global financial system, particularly regarding their influence on volatility transmission between markets.

This chapter begins by tracing Scotland’s early encounters with and reaction to evolution, starting with Darwin’s time as a medical student in Edinburgh. The multicultural nature of modern Scotland, its education system and the role of religion in that system are discussed. Scotland’s education system has long been independent of the rest of the UK. Biology, including evolution, has relatively recently become prominent in Scottish schools. Currently, evolution is introduced in compulsory General Science at secondary year three, but a deeper treatment is given in optional Biology in years 4-6. Unfortunately, evolution is absent from the optional Human Biology curriculum, a course much used by prospective medical students. Our survey of Scottish biology teachers showed that most were confident in their ability to teach evolution, but that a small minority did not accept the theory themselves. Most Scottish universities include evolution as part of their biology foundation courses, and some provide advanced courses in modern evolutionary biology. Our surveys of Glasgow University biology and medical students show low, but still worrying evolution rejection rates. We provide an analysis of students’ reasons for acceptance or rejection of evolution and comment on what education can do to improve the acceptance rate. We trace the influence of creationism in Scottish schools, especially links with the USA, and analyse the coverage of Earth history and species origins in the Scottish schools religious education curriculum.

This paper introduces a versatile system for analyzing loan approval datasets using Python libraries. The system integrates GUI components via Tkinter, enabling file selection anddataset loading. It incorporates data preprocessing techniques, transforming non-numeric data into numeric formats and visualizing loan status and rejection reasons through bar graphs. The datasetundergoes normalization and splitting, facilitating training and testing using a Random Forest Classifier from the Scikit-learn library. Performance metrics such as accuracy, precision, recall, and F1-score are calculated and displayed along with confusion matrices for insightful model evaluation. The system extends its functionality by allowing users to predict loan approval status and rejection reasons for new datasets. Furthermore, it integrates SHAP (SHapley Additive exPlanations) to offer explainable AI, visualizing the impact of features on loan approval predictions. This comprehensive system offers a holistic approach to loan approval analysis, providing prediction capabilities and interpretability through explainable AI techniques.

This article examines the integration of artificial intelligence in financial evaluation and the vital role of explainability in building trustworthy decision support systems. As AI transforms traditional financial evaluation from forecasting to portfolio management, the inherent opacity of sophisticated algorithms creates tension with the financial sector's transparency requirements. The discussion explores how Explainable AI techniques—particularly SHAP values and LIME—enable financial professionals to understand AI-generated insights while maintaining regulatory compliance. Through examining real-world implementations, the article demonstrates quantifiable benefits of explainable models in reducing false positives, improving analyst confidence, and accelerating regulatory approval. The evaluation extends to comprehensive Responsible AI frameworks encompassing fairness and bias mitigation, privacy-preserving techniques, and adversarial resilience mechanisms. The discussion addresses how generative AI assistants revolutionize document evaluation by automating summarization and data extraction while confronting critical security challenges, including prompt injection attacks, data leakage, and regulatory compliance complexities. The article emphasizes human-in-the-loop paradigms and tiered governance frameworks that successfully balance innovation with appropriate oversight, while examining real-time explainability challenges and monitoring requirements. Forward-looking perspectives on regulatory harmonization and the convergence of explainable, privacy-preserving, and robust AI systems demonstrate the evolution toward trustworthy financial AI implementations.

The ongoing COVID-19 pandemic has led to not only the loss of enormous lives, but the dramatic impact on global financial markets. By considering 29 global systemically important banks from four regions (North America, Europe, China, Japan), we employ the proposed truncated D-vine dynamic mixed copulas model to investigate the evolution of the systemic risk of global banking system during the COVID-19 pandemic period. From empirical results, as a worldwide shock, the COVID-19 pandemic does have increased the systemic risk of the global banking system. Specifically, the systemic risk level of the global banking sector was moderate during the period when the COVID-19 pandemic burst only in China, and increased rapidly when the virus spread over the world, then cooling down when emergency actions were taken by countries. In addition, the systemic risk contribution of banks in most regions (like North America, Europe, and Japan) under the similar epidemic situation during the COVID-19 period, seem to be not impacted by the evolution of the panic (as well as the systemic risk level), while the systemic risk contribution of Chinese banks kept falling due to its opposite situation to others in this period.

The quest to explain the output of artificial intelligence systems has clearly moved from a mere technical to a highly legally and politically relevant endeavor. In this paper, we provide an overview of legal obligations to explain AI and evaluate current policy proposals. In this, we distinguish between different functional varieties of AI explanations - such as multiple forms of enabling, technical and protective transparency - and show how different legal areas engage with and mandate such different types of explanations to varying degrees. Starting with the rights-enabling framework of the GDPR, we proceed to uncover technical and protective forms of explanations owed under contract, tort and banking law. Moreover, we discuss what the recent EU proposal for an Artificial Intelligence Act means for explainable AI, and review the proposal’s strengths and limitations in this respect. Finally, from a policy perspective, we advocate for moving beyond mere explainability towards a more encompassing framework for trustworthy and responsible AI that includes actionable explanations, values-in-design and co-design methodologies, interactions with algorithmic fairness, and quality benchmarking.

During the economic crisis of 2008, the global economic system came under tremendous stress due to unusually high activity in the risk spread and volatility in the financial asset valuation. The spread between the US T-bill rate and the London interbank offer rate (LIBOR), a proxy for perceived counterparty credit risk called the “TED spread,” also increased during the crisis period and impacted other economic variables. This study examines the impact of credit risk, as represented by the TED spread, on the daily returns of the ABAQ index, which represents community banks, and the KBW index, which represents the money center and other major banks. The findings, based on daily data from 2010–2020, are as follows: An increase in credit risk led to a decrease in returns for both community banks and money center banks; however, the significance of credit risk and its impact on the stock returns of community banks and money center banks diminished when controlled for other stock market movements. <b>Key Findings</b> ▪ The TED Spread is critical to investment valuation since it is an indicator of the price of money in the global banking system. ▪ As an indicator of the solvency of financial institutions, monetary liquidity, and perceived risk of the financial system, an increase in the TED spread impacts the daily returns of banks (community banks, money centers, and other major banks) negatively. ▪ An increase in the TED spread leads to the drying up of market liquidity that increases cost of funding and generates a negative reaction in the stock market. The study shows that stocks for both larger banks and community banks, while affected by the TED spread, are more impacted by general market movements.

The explosion of Internet of things (IoT) devices has led to pretty much saturated network infrastructures, thus congestion often becomes severe, especially in applications where users require low latency, high throughput and real-time responses. Hence, traditional congestion control mechanisms like static routing protocols, Active Queue Management (AQM) and TCP variants are not suitable for this dynamic and heterogeneous IoT environment as they are reactive, rigid and cannot adapt to dynamic changes. The contribution is to investigate the potential of AI paradigms—such as ML, DL, RL and hybrid models- to provide Intelligent and proactive congestion Control in IoT systems. The paper compares the strengths and weaknesses of each method, such as RL’s generalizability and DL’s ability to capture patterns, as well as limitations in terms of scalability, interpretability, and computational resource requirements. Moreover, it emphasizes important research gaps in model generalization process, evaluation criteria and cross-platform fusion. Next steps in research discussions on the future of research take into account lightweight AI architectures, Explainable AI (XAI) frameworks, scalability of FL, and standard benchmarking data sets. The hybrid-AI congestion prediction model built in this work is validated across simulation tools and real-data sets and is observed to result in a significant decrease in latency, packet loss and energy consumption. This paper paves the way for scalable, intelligent and secure AI-based congestion management solutions for various IoT networks.

Web accessibility stands as a fundamental requirement for inclusive financial services because people with disabilities need to access online banking and trading platforms and financial services. The W3C (2025) maintains WCAG 2.1 as a global standard which provides web designers with accessible design guidelines while U.S. and EU regulators enforce compliance through ADA guidance and the 2024 EU Accessibility Act . The majority of websites contain accessibility barriers because research shows that 95% of 1,000,000 websites fail to meet accessibility standards and financial websites become targets for legal actions and regulatory investigations because of their inaccessible design . The process of testing web applications for accessibility through manual methods and rule-based systems (WAVE and axe) proves to be time-consuming and restricted in its capabilities. The research introduces FinAccAI as an AI system which performs automated accessibility testing for financial web applications. The system design implements rule-based verification for WCAG requirements including alt-text and label presence alongside machine learning and deep learning content analysis. The system uses a conceptual algorithm to extract HTML data and perform CNN/NLP-based visual analysis of charts before identifying accessibility problems. The system includes an Explainable AI (XAI) framework which generates human-friendly explanations for each detected violation through LIME/SHAP feature attribution methods . The research evaluates three testing methods (rule-based and ML and DL) through their ability to detect problems and their scope of detection while previous research demonstrates AI systems can decrease human work requirements . The AI-enhanced testing method demonstrates potential to achieve expert-level results according to our conceptual assessment which draws from research on finance site auditing .The system requires additional research to address model bias and generative errors which affect its performance while maintaining open-source model availability for reproducibility. The proposed system creates essential components for automated accessibility testing in financial services which helps in both regulatory compliance as well as business development. The research demonstrates that AI-based accessibility testing identifies more accessibility problems than traditional tools while delivering fast and dependable compliance verification results. The FinAccAI system enables large-scale intelligent accessibility audits which support national digital inclusion initiatives while helping institutions meet regulations and minimizing their exposure to risks and expanding financial services to 10 million American users. The research creates essential foundations for upcoming AI-based accessibility testing research while establishing open accessible solutions which will benefit the U.S. financial and technological sectors.

Digital assets, including cryptocurrencies, non-fungible tokens, and digital tokens, are reshaping global economic systems by promoting financial inclusion, decentralized control, and technological innovation. However, their intangible, decentralized, and cross-border nature presents significant challenges to existing legal frameworks, leading to legal uncertainty, regulatory fragmentation, and enforcement difficulties. This study systematically analyzes the definitions, characteristics, and classifications of digital assets while examining the regulatory approaches of the United States, the European Union, the BRICS nations, and Singapore. By identifying key challenges such as security vulnerabilities, cross-border complexities, and market volatility, this paper proposes solutions including harmonized frameworks, enhanced consumer protections, technological innovations, and international collaboration. The findings emphasize the importance of a balanced regulatory approach that fosters innovation, ensures consumer protection, and supports market stability in the evolving digital asset ecosystem.