Against the complex characteristics of the Ethereum transaction network and the limitations of existing graph embedding methods based on random walks, which fail to effectively capture transaction temporal dynamics and the flow of funds, we propose a fraud detection algorithm for Ethereum, ETX2Vec (Ethereum Transactions (TX) to Vector), which improves upon transaction subgraph construction and random walk strategies. First, in terms of transaction subgraph construction, we extract the first-order predecessor and successor neighboring nodes of the target node to reconstruct the transaction subgraph, enabling the random walk to effectively capture the complete flow of funds. Second, in the design of the random walk strategy, we introduce two key improvements: (1) the next node is selected based on the non-decreasing principle of transaction timestamps, effectively capturing the temporal dynamics of transactions within the network, and (2) a biased random walk strategy is designed based on both transaction timestamps and amounts, with a parameter α introduced to control the weighting of these factors when calculating transition probabilities. Experimental results show that ETX2Vec achieves an average performance of 96.04% in downstream node classification tasks, outperforming the best model in similar studies by 3.74%, and even surpassing neural network models such as GAT and GCN. This demonstrates that ETX2Vec is more effective at understanding and processing the Ethereum transaction network, leading to the learning of high-quality node embedding vectors.
Traditional consensus mechanisms, such as Proof of Stake (PoS), increasingly reveal an excessive dependency on large liquidity providers. Although the Proof of Liquidity (PoL) mechanism serves as a critical paradigm for incentivizing sustained liquidity provision and ensuring market stability, its transition from asset staking to active liquidity management significantly increases the complexity of underlying smart contract economic models and interaction logic. This renders hidden liquidity logic flaws difficult to detect via traditional methods, seriously threatening the system stability and user asset security of mainstream DeFi and emerging PoL ecosystems. To address this, we propose the LiquiLM framework, which integrates Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN). By establishing a dynamic interaction between liquidity-critical contracts and flaw descriptions, the framework effectively bridges the semantic gap between underlying code implementations and high-level liquidity intents. We evaluate the performance of LiquiLM on 1,490 validation contracts (covering precision, recall, specificity, and F1-score). The results show that it achieves significant effectiveness in auditing and explaining liquidity flaws: in experiments using Gemini 3 Pro and GPT-4o as backbone models, respectively, the F1-scores both exceed 90%. Furthermore, through an in-depth audit of 1,380 real-world PoL and Ethereum economic contracts, LiquiLM successfully identifies 238 high-risk contracts and assists in discovering 10 vulnerabilities that have received CVE certification.
O presente artigo examina as distinções entre finalidade probabilística e finalidade absoluta em sistemas blockchain, bem como suas implicações para o desenho e a operação de aplicações financeiras que visam a replicar ou substituir infraestruturas tradicionais de liquidação. Em cadeias que operam sob finalidade probabilística – modelo historicamente associado a protocolos baseados em Prova de Trabalho (Proof-of-Work) – o grau de irreversibilidade de uma transação cresce à medida que novos blocos são adicionados sobre o bloco que a contém, de modo que a probabilidade de reversão tende assintoticamente a zero sem, contudo, alcançar garantia determinística, o que justifica a prática de mercado de aguardar múltiplas confirmações antes de considerar a liquidação efetivamente concluída. Em contrapartida, cadeias dotadas de finalidade absoluta – também denominada finalidade instantânea – usualmente implementadas sobre protocolos de tolerância a falhas bizantinas (BFT) ou em arquiteturas híbridas que combinam Prova de Participação (PoS) e BFT, oferecem irreversibilidade assim que um bloco é atestado por um superconjunto qualificado de validadores, aproximando-se das expectativas de definitividade inerentes a sistemas de liquidação financeira tradicionais. A metodologia adotada combina revisão conceitual das diferentes acepções de finality em mecanismos de consenso, análise de documentação técnica de protocolos BFT – a exemplo de Tendermint, IBFT e QBFT – e discussão de relatórios recentes sobre risco de liquidação e finality aplicáveis à tokenização de ativos do mundo real (Real World Assets – RWA) em infraestruturas on-chain. Os resultados obtidos sinalizam que, embora a finalidade probabilística se mostre adequada a pagamentos de varejo e transferências de valor moderado, aplicações financeiras de maior montante, processos de tokenização de ativos e infraestruturas de mercado requerem, na prática, garantias mais robustas de irreversibilidade, com frequência combinando finalidade técnica e mecanismos jurídicos de mitigação de risco de liquidação. Conclui-se que a opção entre os dois modelos de finalidade encerra trade-offs relevantes entre segurança, velocidade de confirmação, complexidade de protocolo e conformidade regulatória, e que o desenho de aplicações financeiras em ambiente Web3 deve considerar explicitamente essas diferenças ao definir janelas de liquidação, políticas de gerenciamento de risco e estratégias de integração com o sistema financeiro tradicional.
This study examines the development and intellectual structure of fraud detection research through a bibliometric analysis. Using data extracted from a major scientific database and analyzed with bibliometric visualization tools, the study maps publication trends, influential contributors, and thematic evolution within the field. The findings reveal that fraud detection research is strongly centered on machine learning and increasingly shaped by advances in deep learning, neural networks, and data-driven approaches. At the same time, the field has expanded beyond traditional financial contexts into broader digital ecosystems, including cybersecurity, blockchain, and data privacy. The analysis also highlights a clear shift from conventional statistical methods toward more adaptive and complex models capable of handling large-scale and interconnected data. In addition, emerging themes such as predictive analytics, risk management, and decentralized finance indicate a growing orientation toward real-world application and decision-making. Overall, the study provides a comprehensive overview of the research landscape, identifies key trends and gaps, and offers directions for future research, particularly in integrating technological innovation with practical, ethical, and system-level considerations.
Financial Fraud has become increasingly common today due to the decentralized finance systems. It involves illegal activities that take over our finances without our knowledge, potentially causing huge losses and negatively affecting economic integrity. Financial fraud erodes trust among the general public, investors, and customers, destabilizing the financial system and hindering economic development. In this Research paper, we aim to explore methods for preventing these fraudulent activities using Artificial Intelligence. It studies the methods and tools we can use to reduce financial fraud. As technology advances, we now have artificial intelligence, which enables us to use modern techniques to combat fraud. We can use various Artificial Intelligence tools like Machine Learning, Deep Learning, Natural Language Processing, Anomaly Detection, Reinforcement Learning, Graph method, and various other tools to recognize the unidentified patterns in our financial transactions and save ourselves from financial fraud. Furthermore, it is essential to implement robust security systems within decentralized finance platforms. This study on enhancing security systems and preventing financial fraud will be helpful to future developers, Researchers, Investors, Individuals, Regulatory bodies, and Security Firms. The goal is to make decentralized finance systems more secure to mitigate the risk of financial fraud and to protect the economic integrity for sustained economic development. Based on this study we will able to upgrade the security system of our financial transactions by using various artificial intelligence tools and can reduce the number of frauds. While completely eliminating financial fraud is challenging, we can significantly reduce it through concerted efforts, creating awareness, utilizing artificial intelligence tools, and exercising vigilance.
Ziqiao Kong, Wanxu Xia, Chong Wang, Yi LU · 9 authors
Smart contracts govern billions of dollars in decentralized finance (DeFi), yet automated vulnerability detection remains challenging because many vulnerabilities are tightly coupled with project-specific business logic. We observe that recurring vulnerabilities across diverse DeFi business models often share the same underlying economic mechanisms, which we term DeFi semantics, and that capturing these shared abstractions can enable more systematic auditing. Building on this insight, we propose Knowdit, a knowledge-driven, agentic workflow for smart contract vulnerability detection. Knowdit first constructs an auditing knowledge graph from historical human audit reports, linking fine-grained DeFi semantics with recurring vulnerability patterns. Given a new project, a multi-agent pipeline leverages this knowledge through an iterative loop of specification generation, Proof-of-Concept (PoC) synthesis, PoC execution, and finding reflection, driven by a shared repository index. We evaluate Knowdit on 11 recent Code4rena projects with 84 ground-truth vulnerabilities. Knowdit detects all 21 high-severity and 90% of medium-severity vulnerabilities without false positives, fully covering eight projects, significantly outperforming all baselines. Applied to seven real-world projects, Knowdit further discovers 9 high- and 36 medium-severity previously unknown vulnerabilities, securing millions in liquidity and proving its outstanding performance.
Bu çalışma, Bitcoin fiyat tahmininde mevsimsel ARIMA (SARIMA) modelinin öngörü performansını, basit bir Naive kıyas modeliyle açık biçimde karşılaştırarak yeniden değerlendirmeyi amaçlamaktadır. Analiz, 12 Mart 2021 ile 12 Mart 2026 dönemini kapsayan günlük Bitcoin kapanış fiyatlarına dayanmaktadır. Seri logaritmik forma dönüştürülmüş ve durağanlık özellikleri fark alma işlemleriyle incelenmiştir. İlk aşamada mevsimsel olmayan ARIMA modelleri tahmin edilmiş, ardından mevsimsel dinamikleri içeren alternatif SARIMA modelleri değerlendirilmiştir. Model seçiminde parametre anlamlılığı ile Ljung-Box tanı istatistikleri dikkate alınmış ve mevsimsel hareketli ortalama bileşeninin kısmen anlamlı olduğu görülmüştür. Bu çerçevede SARIMA(0,1,1)(0,1,1)[30] nihai mevsimsel aday model olarak belirlenmiştir. Tahmin performansı değerlendirmesi, yalnızca model uyumuna değil, örneklem dışı tahmin doğruluğuna odaklanmaktadır. Bu amaçla SARIMA modelinin performansı, ortalama mutlak hata (MAE) ve hata kareler ortalamasının karekökü (RMSE) ölçütleri kullanılarak Naive model ile karşılaştırılmıştır. Bulgular, örneklem dışı dönemde Naive modelin SARIMA modeline göre belirgin biçimde daha düşük tahmin hataları ürettiğini göstermektedir. Naive model için MAE 0.016011 ve RMSE 0.023173 iken, SARIMA modeli için bu değerler sırasıyla 0.23172 ve 0.28931’dir. Sonuçlar, Bitcoin gibi yüksek oynaklığa sahip finansal zaman serilerinde daha karmaşık mevsimsel yapıların her zaman daha üstün tahmin performansı sağlamadığını göstermektedir.
The landscape of e-commerce has witnessed a transformative shift in consumer behavior, driven by the rise of digital technologies and online platforms. As online purchases increase at an alarming rate, fraudulent activity has become a major concern for retailers and consumers alike. The objective of this research is to investigate methods for detecting fraudulent online transactions using machine learning algorithms. This paper proposes a Hybrid Agentic AI Architecture (HSAA) for edge-enabled e-commerce that incorporates intelligent agents and cryptographic security to enable real-time, trustworthy transaction processing. The architecture uses world-model distillation to enable efficient inference on edge devices. HSAA was tested on several large data sets such as a balanced credit card fraud set containing 2,952 transactions. The system scored 96.6% in detecting fraud, indicating very low false positives and high specificity. Negotiation exercises on 400 independent interactions were successful in 59%, with an average discount of 14.2%, using 1,142 zero-knowledge proofs that were verified with 100% validity. Some of the operational performance highlights include a throughput of 585 transactions per second, an average latency of 1.56 milliseconds, and a 81.9% reduction in bandwidth through selective state transfer. The findings support the argument that HSAA is a strong, secure, and high-performance edge-based e-commerce architecture, combining accuracy, efficiency, and reliability. Within HSAA, fraud detection functions as one of the core decision agents, while negotiation and secure execution mechanisms provide the broader operational context for trustworthy edge commerce. The architecture provides a solid basis for future studies in adaptive and autonomous AI-driven commercial systems.
Arti Patle, Shubham Bora, Gaurav Salunke, Suyash Biradar
In the current cyber digital era, financial fraud has evolved into a sophisticated threat that often bypasses conventional detection systems. Fraudsters exploit fake accounts and unregulated payment gateways, making it challenging for legacy systems to keep up. To address these modern threats, Mule Trace offers an intelligent and real-time fraud detection framework. It extends its capabilities by integrating blockchain technology, specifically Ethereum, for logging suspicious activities, ensuring transparency and immutability of flagged data. Utilizing machine learning models such as Isolation Forest and Gaussian Mixture Models (GMMs), Mule Trace is capable of identifying irregularities in financial transactions with improved accuracy and minimal false positives. The platform operates in real time through a Web3.js interface, removing reliance on centralized systems and enhancing system resilience. Coupled with a React.js dashboard, users can visualize transactions, detect anomalies, and respond promptly to threats. Mule Trace thus provides a robust, scalable solution for modern financial institutions to combat illicit financial behaviors.
Decentralised Finance (DeFi) applications involve a large volume of funds and exhibit diverse user behaviours, including malicious activities such as smart contract exploits and financial scams. Existing approaches struggle to capture complex behaviours. To address this gap, we propose a general Blockchain User Behaviour Analysis (BUBA) pipeline for DeFi security. The pipeline presents an automated action formation process that takes blockchain transactions as inputs and outputs user actions. In addition, BUBA introduces a dual Graph Neural Network (GNN) model that jointly captures user action features, contract and token interactions, and heterogeneous graph structure information to produce rich behavioural embeddings, enabling effective clustering of semantically meaningful user behaviours. We evaluate the proposed pipeline on Uniswap V3, where it outperforms baseline methods in identifying and differentiating suspicious behaviours. A further case study on Sushiswap V2 demonstrates the generalisability of the pipeline across DeFi applications.
Modern financial security issues have arisen as a result of the rapid proliferation of decentralized financial systems and cryptocurrencies. One such issue is the identification of individuals who are laundering money in blockchain networks. Blockchain technology's distributed ledgers clarify matters; however, the anonymity of wallet addresses facilitates illicit financial transactions by criminals. It is crucial to have effective methods to identify these crimes, as over $82 billion in cryptocurrencies were associated with money laundering in 2025. This study demonstrates a method for detecting indications of money laundering in blockchain transaction networks through the use of machine learning. The proposed method for identifying unusual patterns in transactions involves the combination of supervised machine learning, graph-based feature extraction, and data cleansing. The system examines transaction graphs to identify unusual patterns that are associated with illicit financial activities by employing techniques such as Random Forest, Gradient Boosting, and Graph Neural Networks.
Jiahao Pei, Ning Duan, Gang Du, Kejia Zhang · 5 authors
Smart contracts are self-executing programs running on blockchain networks. Once deployed, they are immutable, making their security critically important. Reentrancy vulnerability is one of the most notorious security vulnerabilities in smart contracts, which allows attackers to repeatedly invoke target functions before the execution of contract functions is completed, thereby stealing funds or corrupting contract states, resulting in severe economic losses in recent years. Existing detection tools often suffer from insufficient path coverage and oversimplified detection rules. This paper proposes a static analysis approach based on smart contract bytecode that recovers execution paths by constructing a control flow graph (CFG), identifies all potential vulnerability paths using taint analysis, and detects reentrancy vulnerabilities through path matching rules. To validate the approach’s effectiveness, we compare it with mainstream detection tools on an annotated smart contract dataset. Experimental results demonstrate that the approach achieves a precision of 93.2%, outperforming other tools overall. Additionally, through analysis of 2023 real-world smart contracts deployed on Ethereum, 21 contracts are found to contain reentrancy vulnerabilities.
Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks. In this paper, we present TOKENSCOUT, the first temporal GNN-based framework for scam token early detection. TOKEN SCOUT formulates token transfer data as a dynamic temporal attributed multigraph and leverages the temporal graph learning model to learn graph representations. It also builds a graph rep resentation refining model based on contrastive learning to learn a more discriminative representation space for risk identification. We evaluated TOKENSCOUT using a comprehensive dataset of 214,084 standard ERC20 tokens from 2015 to February 2023. TOKENSCOUT achieves a balanced accuracy of 98.41%. Additionally, from March to May 2023, deploying TOKENSCOUT on Ethereum effectively identified 706 rugpulls, 174 honeypots, and 90 Ponzi schemes, thereby alerting to potential risks exceeding $240 million.
The accelerating digitization of financial services has transformed global economic ecosystems while simultaneously amplifying the scale, speed, and structural complexity of financial fraud. Real-time payments, open banking infrastructures, fintech platforms, and decentralized finance environments have expanded transactional connectivity, creating highly dynamic and interconnected risk landscapes. Conventional rule-based and standalone machine learning systems demonstrate limited effectiveness against adaptive adversaries, coordinated fraud rings, synthetic identity schemes, and cross-platform laundering networks. Advanced detection strategies require intelligent architectures capable of modeling temporal behavior, relational dependencies, and large-scale streaming data within production-grade environments. This chapter presents a comprehensive framework for next-generation financial fraud detection integrating Artificial Intelligence, Deep Learning, and Graph Analytics. The discussion synthesizes supervised, unsupervised, and semi-supervised learning approaches with sequential deep learning architectures, transformer-based models, and graph neural networks for network-aware inference. Emphasis is placed on hierarchical multi-stage detection systems, cloud-native deployment strategies, adversarial robustness, privacy-preserving computation, and real-world validation methodologies. Critical challenges such as extreme class imbalance, concept drift, scalability of graph processing, explainability under regulatory constraints, and cross-institution collaboration are systematically examined. A unified hybrid AI–graph intelligence architecture is articulated to address both transactional anomalies and coordinated fraud ecosystems. The chapter contributes a structured taxonomy of modern financial fraud, an integrated modeling perspective combining temporal and structural intelligence, and a deployment-oriented evaluation framework aligned with real-world financial operations. By bridging theoretical advancements with production-grade implementation considerations, this work establishes a rigorous foundation for scalable, interpretable, and resilient fraud detection systems within evolving digital financial infrastructures.
The rapid expansion of digital banking, mobile payments, decentralized finance, and cross-border electronic transactions has fundamentally transformed global financial ecosystems while intensifying exposure to sophisticated cyber threats, fraud networks, synthetic identity schemes, and money laundering operations. Conventional rule-based security infrastructures lack the adaptability required to counter dynamic and large-scale financial crimes. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative enablers of intelligent financial security, supporting real-time fraud detection, behavioral authentication, transaction risk scoring, and regulatory compliance automation. This chapter presents a comprehensive examination of advanced machine learning techniques—including deep learning, graph neural networks, anomaly detection models, and reinforcement learning—for securing digital transactions and identifying coordinated fraud rings within complex financial networks. Integration of AI with blockchain consensus mechanisms, cryptographic infrastructures, and Regulatory Technology (RegTech) platforms is analyzed to demonstrate how adaptive intelligence enhances network resilience, transparency, and operational efficiency. Emphasis is placed on explainable and fairness-aware AI frameworks to ensure ethical accountability, regulatory alignment, and bias mitigation in automated financial decision systems. Privacy-preserving approaches such as federated learning and secure multi-party computation are also explored to address data governance constraints in cross-institutional collaboration. The chapter consolidates emerging research directions, identifies persistent technical and ethical challenges, and proposes an integrated AI-driven security architecture for scalable and trustworthy digital financial ecosystems.
The rapid digitalization of financial ecosystems has transformed online payments, transaction processing, and investment management into highly interconnected, data-intensive infrastructures. This transformation has simultaneously expanded exposure to cyber fraud, money laundering, identity theft, and market volatility, necessitating intelligent and adaptive security mechanisms. Advanced artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and graph-based analytics, have emerged as critical enablers of secure payment processing, real-time fraud detection, and predictive financial forecasting. Intelligent architectures embedded within online payment systems facilitate dynamic risk scoring, anomaly detection, behavioral profiling, and automated decision-making under strict latency constraints. This chapter presents a comprehensive examination of intelligent system frameworks for digital finance, integrating scalable cloud-based deployment, blockchain-enabled transaction integrity, explainable AI for regulatory compliance, and synthetic data generation for fraud simulation. Reinforcement learning approaches for portfolio optimization and risk-aware forecasting are analyzed to highlight adaptive investment strategies in volatile markets. Emphasis is placed on addressing class imbalance, adversarial threats, model interpretability, privacy preservation, and governance challenges within automated financial infrastructures. Emerging research directions such as federated learning, decentralized finance intelligence, and AI-driven anti-money laundering systems are also discussed to outline future technological trajectories. The presented synthesis establishes a structured foundation for developing secure, transparent, and scalable intelligent financial ecosystems aligned with regulatory and operational requirements of modern digital economies.
Francesco Salzano, Marco Guglielmi, Simone Scalabrino, Rocco Oliveto · 5 authors
This study examines the combined effect of fine-tuning and semantic prompt enrichment on Large Language Model-based vulnerability detection in Solidity smart contracts. We fine-tune ChatGPT-4o through a two-phase process aligned with the DASP Top 10 taxonomy—first to internalize theoretical vulnerability knowledge, then to specialize on labeled Solidity functions. We further enhance the fine-tuned model with automatically generated and human-validated code summaries as semantic enrichments to its prompts. The resulting model achieves an average F1-score of 0.58, a 66% improvement over the baseline (0.35), with the largest gains in Access Control ($+146 \%$), Denial of Service ($+353 \%$), and Reentrancy ($+35 \%$) detection. These results show that domain-aligned fine-tuning and semantic prompt enrichment jointly improve the precision and recall of LLM-based smart-contract auditing, offering a practical path toward AI-assisted security analysis.
The rapid growth of Ethereum smart contracts has attracted significant attention from both academia and industry, leading to the emergence of diverse commercial applications. However, the increasing prevalence of fraudulent activities such as phishing, bribery, and money laundering poses serious threats to the security and integrity of online transactions. To address these challenges, this study proposes a deep learning-based fraud detection framework enhanced with a novel metaheuristic optimization technique. Specifically, an Optimized Genetic Algorithm-Cuckoo Search (GA-CS) hybrid approach is integrated with a deep learning model to improve fraud classification accuracy. The proposed GA-CS algorithm leverages the global search capability of Cuckoo Search while employing Genetic Algorithm operations to overcome its inherent limitations and enhance convergence performance. Extensive experiments are conducted to evaluate the effectiveness of the proposed method against several widely used machine learning and deep learning classifiers, including Logistic Regression (LR), K-Nearest Neighbors (KNN), MultiLayer Perceptron (MLP), XGBoost, Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Classification (SVC), using a limited feature set. Experimental results demonstrate that the proposed GA-CS-optimized deep learning model outperforms most baseline methods and achieves superior detection accuracy. Although its performance is marginally higher than that of the Random Forest model, the proposed approach and the SVC model achieve the highest overall accuracy, confirming the robustness and effectiveness of the proposed framework for fraudulent transaction detection on the Ethereum platform.
Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AI’s long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AI’s potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape
Federated learning enables financial institutions to collaboratively develop credit risk models while maintaining data privacy, yet existing implementations prioritize accuracy and confidentiality over transparency and regulatory compliance requirements. Current federated approaches treat explainability as a secondary concern addressed through separate post-processing workflows, creating significant gaps in auditability and stakeholder trust that limit adoption in regulated environments. This article introduces the Explainable Update Auditing framework, which embeds transparency mechanisms directly into federated training protocols through local explanation bundles and privacy-preserving audit trails. The framework generates standardized, model-agnostic explanations that characterize how institutional updates influence global model behavior without exposing proprietary data or competitive information. Cryptographic attestation mechanisms verify compliance with fairness, stability, and governance constraints throughout training processes using zero-knowledge proof systems that maintain institutional confidentiality while providing mathematical assurance of appropriate collaborative behavior. The dual-layer trust mechanism addresses distinct information needs across multiple stakeholder groups, including participating institutions, regulatory authorities, internal governance bodies, and affected borrowers. Implementation considerations reveal computational overhead challenges, privacy-utility trade-offs, and cryptographic protocol efficiency requirements that must be addressed for practical deployment. The framework transforms federated learning from an opaque collaboration protocol into a transparent, auditable ecosystem that satisfies regulatory requirements while preserving privacy guarantees essential for cross-institutional partnerships in credit risk modeling applications.