This article examines how the design features of retail central bank digital currencies (CBDCs) influence the detection and prevention of crypto-enabled money laundering. Drawing on a comparative analysis of Russia, the European Union, the United States, and Malta, it evaluates the effectiveness of CBDC-integrated anti-money laundering (AML) mechanisms in addressing the three key stages of illicit finance: placement, layering, and integration. Using primary sources, including pilot program data, legislative texts, and policy consultations, alongside secondary academic literature, the Study explores how design elements such as ledger visibility, programmable transaction limits, sanctions screening, and tiered privacy structures can be embedded into CBDC infrastructure. The findings reveal significant variation in enforcement capacity, privacy protection, and governance transparency across jurisdictions, shaped by political economy, legal traditions, and technological architectures. The article argues that while CBDCs offer unprecedented opportunities to embed compliance at the core of payment systems, their legitimacy and adoption depend on the careful balancing of enforcement effectiveness with constitutional safeguards, civil liberties, and public trust. Policy recommendations emphasize jurisdiction-specific typology mapping, programmable safeguards, stakeholder engagement, tiered anonymity, cross-border interoperability, and independent oversight. The analysis concludes that CBDCs, if responsibly designed, can modernize AML frameworks and strengthen financial integrity without undermining democratic principles.
Ruba Islayem, Senay A. Gebreab, Walaa AlKhader, Ahmad Musamih · 7 authors
Traditional health insurance claim processing systems are plagued by inefficiencies and vulnerabilities, often resulting in significant financial losses due to fraudulent activities. Existing fraud detection methods are largely manual, time-consuming, and inadequate for handling the complexity and scale of modern fraudulent schemes. Moreover, the trust-based relationships between insurers and healthcare providers lack mechanisms to ensure data integrity and prevent manipulation. While several blockchain-based systems have been proposed to improve transparency and tamper resistance, they typically focus on structured data and predefined fraud types, offering limited adaptability and analytical insight. This paper proposes a novel solution leveraging blockchain technology and Large Language Models (LLMs) to transform fraud detection. The system uses Ethereum smart contracts (SCs) to securely store medical records and claim details on a decentralized, tamper-proof ledger that ensures data integrity, traceability, and accountability. This immutable data is accessed by an LLM via a Retrieval-Augmented Generation (RAG) system, which enables intelligent retrieval and analysis of relevant clinical information to detect fraud patterns and inconsistencies. To support complex scenarios involving free-text documents, unstructured clinical data, such as lab reports, are stored using decentralized off-chain storage and retrieved during LLM analysis. In addition, an LLM-powered chatbot also allows insurance providers to interact with the system in natural language for claim inquiries, explanations, and summaries. The architecture, sequence diagrams, and implementation algorithms outline the development process, while testing scenarios demonstrate the system's ability to detect fraud such as inflated costs, unnecessary treatments, and unrendered services. Evaluation using both synthetic and public clinical datasets showed strong performance, with the LLM achieving up to 99% fraud detection accuracy. Cost, security, and scalability analyses confirm the system's practicality and resilience, with the complete detection process executing in just 13 seconds. By overcoming the limitations of traditional systems, this framework offers a scalable and adaptable approach for healthcare and other domains. The SCs and source code are publicly available on GitHub.
In an era marked by increasingly sophisticated cyber threats and growing vulnerabilities in national critical infrastructure, this study explores the transformative role of confidential computing in defending against emerging cryptographic attacks and enabling secure threat intelligence sharing. Traditional cybersecurity measures, while effective for protecting data at rest and in transit, fall short in securing data during active processingan area exploited by advanced persistent threats, quantum computing, and side-channel attacks. This research investigates how hardware-based trusted execution environments (TEEs), homomorphic encryption, and zero-knowledge proofs embedded in confidential-computing platforms can preserve the confidentiality of sensitive operations even within potentially compromised environments. Through detailed case studies of major U.S. institutionsincluding PGandE, Exelon, JPMorgan Chase, Wells Fargo, and Kaiser Permanentethe paper demonstrates significant improvements in detection speed, false positive reduction, and operational efficiency. Furthermore, it proposes a scalable, privacy-preserving framework for collaborative cyber defense across critical sectors such as energy, finance, and healthcare. The findings underscore that integrating confidential computing with decentralized intelligence sharing networks not only enhances cybersecurity resilience but also yields substantial economic and regulatory benefits. This work advocates for a national, and eventually global, shift toward confidential-computing-enabled infrastructures to achieve robust, cooperative, and future-proof cyber defense ecosystems.
Cryptocurrencies initially gained prominence by eliminating intermediaries in payment systems and later found applications in various business sectors. The crypto network, pioneered by Bitcoin, has spurred new business forms and organizational structures with diverse motivations. Bitcoin's emergence is technically dated to 2008. However, its ideological and technical roots trace back to the cyberpunk literature of the late 1970s and the cypherpunk movement that began in California in 1992. The cypherpunk manifestos significantly influenced cryptographic work, shaping Bitcoin's technical foundation. This study aims to explore Bitcoin's ideological origins through a qualitative content analysis of cypherpunk manifestos, Nakamoto's posts on the "Bitcointalk" forum, and "Cryptography Mailing List" correspondence. By examining these sources, the study identifies the historical dimensions of Bitcoin's technical structure and highlights the impact of ideological debates on its development. Findings reveal that while cryptographic research influenced Bitcoin's technical evolution, ideological discussions were relatively less significant. Nonetheless, Bitcoin's developers, particularly Nakamoto, incorporated a strong ideological emphasis on "privacy" despite the primary technical focus.
Abstract The advent of blockchain technology has achieved notable progress regarding security, particularly within the realm of e-commerce. The existing Web 2.0 framework, which employs inadequate security measures, exhibits vulnerabilities when compared to the robust security features of blockchain technology. The utilization of monitors, computers, and data storage exemplifies the functionality of blockchain technology, which upholds encrypted and distributed transaction records across multiple computers, consequently improving the reliability of the digital ledger. In a nation such as Bangladesh, where transaction data is susceptible to cyber threats and online fraud is prevalent within the e-commerce sector, this type of decentralized system has the potential to alter the landscape significantly. This requires the implementation of a more comprehensive security protocol. This research advocates for the adoption of smart contracts to enhance supply chain transparency and offers digital identification solutions aimed at preventing fraud, including issues related to non-delivery and counterfeit goods. This research utilizes Next.js for front-end development and facilitates backend integration through Solidity and Hardhat.js, specifically for the Solana Blockchain, deployed on an Amazon EC2 instance. This research commenced with an examination of the current e-commerce ecosystem, physical identification infrastructure, and consumer attitudes, ultimately presenting a strategic implementation plan for the adoption of blockchain technology to enhance trust and assurance within the e-commerce landscape of Bangladesh. It further delineates particular obstacles to adoption: technological limitations, regulatory challenges, socio-economic factors, and the expanding digital payments landscape, particularly concerning mobile financial services. This research enhances the current understanding of blockchain as a transformative force in emerging e-commerce markets and provides valuable insights into technology policies relevant to the developing economy of Bangladesh for policymakers, businesses, and technologists. Graphical abstract
Driven by blockchain technology, numerous industries are increasingly adopting smart contracts to enhance efficiency, reduce costs, and improve transparency. As a result, ensuring the security of smart contracts has become critical. Traditional detection methods often suffer from low efficiency, are prone to missing complex vulnerabilities, and have limited accuracy. Although deep learning approaches address some of these challenges, issues with both accuracy and efficiency remain in current solutions. To overcome these limitations, this paper proposes a symmetry-inspired solution that harmonizes bidirectional and generative semantic patterns. First, we generate distinct feature extraction segments for different vulnerabilities. We then use the Bidirectional Encoder Representations from Transformers (BERT) module to extract original semantic features from these segments and the Generative Pre-trained Transformer (GPT) module to extract generative semantic features. Finally, the two sets of semantic features are fused using a multi-attention mechanism and input into a classifier for result prediction. Our method was tested on three datasets, achieving F1 scores of 93.33%, 93.65%, and 92.31%, respectively. The results demonstrate that our approach outperforms most existing methods in smart contract detection.
Proposes are offered Decentralized Ledger Journalism (DLJ) as a distinct and timely subfield within data journalism, emerging at the intersection of technological innovation and investigative practice. Drawing on the unique affordances of blockchain and other distributed ledger technologies (DLT), this approach positions public, immutable records not merely as supplementary datasets, but as primary sources for journalistic inquiry. From financial transactions and smart contract events to decentralized governance and identity systems, distributed ledgers offer a new evidentiary terrain - structured, transparent, and resistant to alteration. Beyond their utility as data sources, these systems provide native mechanisms for content authentication, including cryptographic timestamping, verifiable provenance, and censorship-resistant publication infrastructures. Such tools enable new methods of verification and preservation, allowing journalists to secure both the integrity of their sources and the durability of their outputs. By exploring the methodological and epistemological implications of blockchain-based journalism, this study outlines how decentralized ledgers can serve both as subject and substrate of inquiry. DLJ, we argue, offers a novel framework for enhancing journalistic integrity in a digital environment increasingly shaped by opacity, manipulation, and central control.
Kostiantyn Orobets, V. I. Shkolnikov, Tetiana Batrachenko, Тетяна Василівна Барановська · 5 authors
Introduction: The legal regime of cryptocurrency in different countries of the world is heterogeneous. In some, it is not defined at all, which leads to legal conflicts, including when qualifying crimes committed with cryptocurrency use. The situation is further complicated because such crimes can occur in the territories of several states where cryptocurrency has a different legal regime. Traditional legislation and mechanisms for combating money laundering and terrorist financing are practically ineffective in the landscape of crimes involving the use of cryptocurrency.Objectives: The aim of the study is to systematise the main patterns of crimes related to the use of cryptocurrency, as well as analyse existing vectors of their legal assessment, appropriate design and application of effective methods of combating these crimes.Methods: Based on the methods of analysis and synthesis, qualitative data analysis, using content analysis as the primary research tool, it is shown that the main problem in preventing the use of cryptocurrency in predicate crimes lies in the technical difficulty of identifying a person or group of persons who carry out cryptocurrency transactions for illegal purposes. Such goals may be aimed at legalising funds, i.e., concealing their illegal origin, making payments in a hidden network, organising various fraudulent schemes, financing terrorism, and other crimes.Results: The article argues that given the technical specifics of cryptocurrency transactions and the technical capabilities of "masking" the origin of cryptocurrency funds, it is necessary to develop methods for studying trace formation and develop an algorithm for establishing and consolidating forensically significant information for this type of crime. The results indicate that the future of law enforcement in the fight against cryptocurrency-related crime will require a multifaceted approach. Agencies must adopt a proactive approach by foreseeing emerging criminal strategies. To protect the public from crimes using digital assets, law enforcement must be flexible, progressive, and technologically savvy as cryptocurrencies continue to develop. The development of provisions on cryptocurrency also determines the theoretical significance of the work as an object and means of committing crimes, a surrogate means of payment during the commission of certain crimes.Conclusions: The practical significance of the work lies in the possibility of using its results to solve problems arising in the law-making activities of state authorities and law enforcement activities, as well as in developing recommendations for improving criminal legislation in the field of cryptocurrency-related crimes.
The rapid digital transformation being currently experienced the developing economies such as Zimbabwe has underlined the inefficiencies and vulnerabilities in security of traditional Know Your Customer (KYC) processes. These KYC processes and procedures are predominantly manual, slow and are prone to data breaches. This paper proposes a privacy preserving authentication model for KYC optimization using Zero-Knowledge Proof (ZKP) cryptography. This model addresses critical challenges which include prolonged customer onboarding times, high operational costs and data compliance risks. By means of leveraging ZKP the model enables secure identity verification without exposing sensitive data which ensures compliance with Zimbabwe Data Protection Act. A mixed-methods approach was adopted, combining qualitative and quantitative techniques to design, develop and evaluate the model. Experimental results have demonstrate significant improvements in data privacy and onboarding efficiency which has seen reduced onboarding time from 3 days to under 10 minutes. The model scalability and adaptability potential makes it suitable for diverse sectors which covers education, healthcare, e-commerce and government services therefore positioning Zimbabwe as a leader in secure digital transformation.
The fact that blockchain technology is decentralized, transparent, and immutable is transforming the face of such industries as finance, healthcare, and logistics. It is nonetheless, difficult in regulatory compliance, data privacy, and law enforcement, specifically blockchain forensics. Blockchain forensics is an activity of tracking transactions and members of illegal organizations like money laundering and cybercrime. Whereas the traceability of the blockchain technology with the transparency it possesses raises no more concerns on the legal issues, the pseudonymity of its participants, on the other hand, makes it quite difficult to identify them, respectively, creating issues within the scope of the data protection, as well as financial regulations. The paper will touch on the practice today of forensics, the regulation and morality of the balance that is there between privacy and criminal investigation. It ends with suggestions of a joint effort in creation of efficient legal frameworks to govern the same, and promotion of innovation.
Chunhong Liu, Zihang Sang, Li Duan, Jingxiong Wang · 6 authors
Security vulnerabilities in smart contracts can have severe economic consequences. Existing smart contract vulnerability detection methods rely primarily on rigid rules defined by experts and have difficulty in detecting unknown vulnerabilities. This article proposes a new Anomalous Smart Contract Detector, named ASCD, to effectively detect known and unknown vulnerabilities in smart contracts. This is achieved by interpreting unknown vulnerabilities as code anomalies and detecting them with an anomaly detection technique named DeepSVDD. This is also attributed to a new design of feature extraction, in which we compile smart contract source codes into opcodes, extract semantic features from opcode sequences, and control flow features from control flow graphs. By joining LSTM and GIN, the semantic and control flow features are fused to offer a comprehensive representation of smart contracts suitable for anomaly detection. Extensive experiments were conducted to verify the ASCD model, and more than 30,000 smart contracts were tested. The new model offers a significantly better F1-score than existing methods in detecting known vulnerabilities and achieves a high accuracy of 77% in detecting unknown vulnerabilities.
Rug pulls present a critical threat in Decentralized Finance (DeFi), causing substantial financial losses and eroding ecosystem trust. Despite research advances, effective detection remains hampered by fragmented taxonomies, limited datasets, and inadequate tool evaluations. Through systematic analysis of academic and industry sources, we develop a comprehensive taxonomy of 35 distinct rug pull types, including 9 previously undocumented variants. Our analysis reveals significant detection gaps: existing datasets cover only 20% of known types, leading us to create an enhanced dataset of 2,391 instances that increases coverage to 82.9%. Evaluation of 13 detection tools shows substantial capability variation (25.7% to 62.9%), with 9 types completely undetectable. Most critically, tool performance degrades significantly when facing complex attacks, with maximum detection rates dropping from 55.6% for single-vector cases to 31.3% for compound scenarios. These findings provide essential insights for developing more robust security testing approaches for smart contract vulnerabilities in decentralized systems.
With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access. By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning. ETrace implements pattern-matching to establish causal links between transaction behavior patterns and known attack behaviors. Furthermore, we validate the effectiveness of ETrace through preliminary experimental results.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini · 5 authors
In the world of cryptocurrencies, the public listing of a new token often generates significant hype. In many cases, the price of the token skyrockets in a few seconds, and timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify operations performed using sniper bots. Our findings reveal 352,413 sniping operations on Ethereum and 1,716,917 on BSC for a total turnaround of $155,630,184 and $137,548,859, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze possible countermeasures and mechanisms used in token smart contracts that can reduce the negative impact of sniper bots.
Nikos Papatheodorou, George Hatzivasilis, Nikos Papadakis
Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier (DID) standards. The architecture includes smart contracts for access control, decentralized storage using the Inter Planetary File System (IPFS), and long-term persistence via Web3.Storage. YouGovern enables users to register, share, and revoke identities while preserving privacy and auditability. The system supports role-based permissions, verifiable claims, and cryptographic key rotation. Performance was evaluated using Ganache and Hardhat under controlled stress tests, measuring transaction latency, throughput, and gas efficiency. Results indicate an average DID registration latency of 0.94 s and a peak throughput of 12.5 transactions per second. Compared to existing SSI systems like Sovrin and uPort, YouGovern offers improved revocation handling, lower operational costs, and seamless integration with decentralized storage. The system is designed for portability and real-world deployment in academic, municipal, or governmental settings.
Smart contracts underpin decentralized applications but face significant security risks from vulnerabilities, while traditional analysis methods have limitations. Large Language Models (LLMs) offer promise for vulnerability detection, yet adapting these powerful models efficiently, particularly generative ones, remains challenging. This paper investigates two key strategies for the efficient adaptation of LLMs for Solidity smart contract vulnerability detection: (1) replacing token-level generation with a dedicated classification head during fine-tuning, and (2) selectively freezing lower transformer layers using Low-Rank Adaptation (LoRA). Our empirical evaluation demonstrates that the classification head approach enables models like Llama 3.2 3B to achieve high accuracy (77.5%), rivaling the performance of significantly larger models such as the fine-tuned GPT-3.5. Furthermore, we show that selectively freezing bottom layers reduces training time and memory usage by approximately 10-20% with minimal impact on accuracy. Notably, larger models (3B vs. 1B parameters) exhibit greater resilience to layer freezing, maintaining high accuracy even with a large proportion of layers frozen, suggesting a localization of general code understanding in lower layers versus task-specific vulnerability patterns in upper layers. These findings present practical insights for developing and deploying performant LLM-based vulnerability detection systems efficiently, particularly in resource-constrained settings.
Abstract: The Financial Technology (Fintech) sector is changing at a swift pace, as artificial intelligence (AI) is extending its influence. Greater complexity and global linkages are going to demand from fintech the power to rethink the integrity of its cybersecurity mechanisms and fraud tactics that have gotten intense up to a growing extent. The paper argues for the necessity of an "Algorithmic Fortress," an AI-driven cybernetic system incorporating all possible technologies targeted at securing digital financial networks against cyber-attacks and acts of financial fraud. The article delves into AI/ML, deep learning, anomaly detection through generative adversarial networks, etc., scope to predict battle, detect and fight problems. It does address adverse effects of AI risk, threatened system independence through synthetic identity fraud, application of AI for fraud detection in decentralized finance, DeFi, as well as the threat-hunting models that need to become autonomous. Supervised learning, unsupervised learning, and reinforcement learning are examination methodologies that are being applied in taking high recourse to the preservation of cybersecurity amongst their uncertainties. Our analysis will involve different experimentations of Python-based simulated attack scenarios to compare the two forms of cybersecurity. Also brought in are SmartArt visual representations revealed in multi-tier defensive architectures, combined with some strategic recommendations destined to protect future-facing fintech infrastructures from doing illicit deeds of algorithms. This study sketches possible solutions for securing the future-ready, trustworthy, and resilient fintech ecosystems once assisted by AI-enhanced, digital fortresses.
Money laundering has long been a major issue for governments, law enforcement agencies, and financial institutions around the globe. As technology advances, so too do money laundering methods, presenting new challenges for authorities and financial entities. Organised Crime Groups (OCGs) are increasingly exploiting digital platforms, cryptocurrencies, and virtual assets to disguise illicit funds while maintaining anonymity and complicating their transactions. This article analyses the problem of cryptocurrency laundering by the OCGs and various tactics employed by the OCGs to cover their trails. This article also in-depth discusses the international instruments such as the United Nations Convention against Transnational Organised Crime and Financial Action Task Force recommendations on the prevention of cryptocurrency laundering. The special focus of this paper is on the legal framework regarding cryptocurrency laundering in the United States, European Union and Malaysia. The findings of the paper suggest that there is a regulatory framework present in these jurisdictions but their regulations are not subject specific and regulatory powers have been granted to the authorities that are not specialised and skilled to tackle the problem of combating cryptocurrency laundering by OCGs.
The prosperity of Ethereum has led to a rise in phishing scams. Initially, scammers lured users into transferring or granting tokens to Externally Owned Accounts (EOAs). Now, they have shifted to deploying phishing contracts to deceive users. Specifically, scammers trick victims into either directly transferring tokens to phishing contracts or granting these contracts control over their tokens. Our research reveals that phishing contracts have resulted in significant financial losses for users. While several studies have explored cybercrime on Ethereum, to the best of our knowledge, the understanding of phishing contracts is still limited. In this paper, we present the first empirical study of phishing contracts on Ethereum. We first build a sample dataset including 790 reported phishing contracts, based on which we uncover the key features of phishing contracts. Then, we propose to collect phishing contracts by identifying suspicious functions from the bytecode and simulating transactions. With this method, we have built the first large-scale phishing contract dataset on Ethereum, comprising 37,654 phishing contracts deployed between December 29, 2022 and January 1, 2025. Based on the above dataset, we collect phishing transactions and then conduct the measurement from the perspectives of victim accounts, phishing contracts, and deployer accounts. Alarmingly, these phishing contracts have launched 211,319 phishing transactions, leading to 190.7 million in losses for 171,984 victim accounts. Moreover, we identify a large-scale phishing group deploying 85.7% of all phishing contracts, and it remains active at present. Our work aims to serve as a valuable reference in combating phishing contracts and protecting users' assets.
Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Christos Klavdianos
The rapid growth of digital platforms has fundamentally reshaped network and viral marketing, profoundly transforming how information spreads across social networks and influences consumer behavior. This comprehensive review synthesizes theoretical, computational, and ethical perspectives into an integrated narrative, providing novel insights into the mechanisms driving information diffusion within contemporary interactive marketing. By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. A distinctive theoretical contribution of this study lies in its innovative combination of Big Data analytics with AI-based predictive modeling, explicitly revealing how real-time algorithmic personalization not only enhances marketing effectiveness but also creates new ethical tensions surrounding misinformation, algorithmic bias, and consumer vulnerability. Addressing recent calls for greater theoretical originality and narrative coherence in interactive marketing research, this review explicitly highlights how these insights resolve critical theoretical puzzles and clarify contemporary ethical dilemmas. Additionally, the paper identifies emerging trends—including Web3 marketing, decentralized platforms, and neuroscience-driven targeting—offering clear future research directions. Through its integrative, narrative-driven framework, this study significantly advances interactive marketing theory, providing essential guidance for scholars and practitioners navigating the evolving complexities of digital influence.
Smart contracts represent a predefined set of rules invoked when specific conditions are met within blockchain networks, eliminating the need for centralized authority to validate transactions. The absence of central authority can potentially expose smart contracts to fraudulent behavior. Moreover, implementation flaws in smart contracts can be exploited to cause unintended behavior, resulting in security or financial risks. Traditionally, the identification of vulnerabilities in smart contracts has relied on methods such as pattern matching, data flow analysis, and input testing. While these techniques are foundational, they are constrained by human limitations and may not comprehensively address the full spectrum of potential issues. This necessitates more advanced approaches to ensure robust security and reliability. Therefore, in the literature, numerous researchers have leveraged different Machine Learning (ML) and Deep Learning (DL) techniques to classify normal and malicious smart contracts. However, existing literature either grapples with class imbalance issues or relies on conventional methods. Moreover, existing research often falls short of locating the exact location of malicious code within the smart contracts. Therefore, to address these gaps, this paper proposes a novel model called the Dual-Branch Encoder Siamese Network (DBESN) for detecting malicious smart contracts. Furthermore, this model is extended to precisely identify the region of the vulnerable code fragment within the smart contract using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Experimental results demonstrated a performance Accuracy of 98.62% and 99.30% F1-Score with an inference time of 0.296 seconds. Given the high performance coupled with the low inference time of the proposed DBESN model, it is suitable for deployment within blockchain networks to detect and identify malicious smart contracts effectively and efficiently.
In an increasingly digitalized and hyperconnected financial landscape, the complexity and frequency of cyber threats have grown exponentially, exposing financial institutions to real-time risks that conventional defense mechanisms struggle to mitigate.Traditional security frameworks, often reactive and siloed, lack the speed and contextual awareness required to protect dynamic finance ecosystems driven by automated trading, open banking, and decentralized financial services.This paper explores the emerging paradigm of Integrative Analytics for Autonomous Threat Response (IAATR)-a strategic synthesis of artificial intelligence (AI), behavioral modeling, and real-time analytics to secure business processes within finance ecosystems.From a broad perspective, the integration of AI into cybersecurity presents transformative possibilities.Machine learning models trained on network telemetry, user behavior, and transaction anomalies can detect threats proactively, adapt to novel attack patterns, and initiate countermeasures with minimal human intervention.The paper discusses how autonomous systemsrooted in deep reinforcement learning and explainable AI-enhance threat triage, isolate compromised processes, and orchestrate secure workflow rerouting to minimize systemic disruption.Narrowing the focus to finance-specific applications, the paper examines use cases including algorithmic fraud detection, insider threat mitigation in payment systems, and AI-enabled compliance monitoring.Emphasis is placed on the design of feedback loops between security intelligence layers and business process management (BPM) engines, ensuring that threat responses remain aligned with regulatory standards and operational continuity.The study concludes with a discussion on governance, ethical risks, and the role of digital trust in advancing AI-secured business environments.IAATR represents not just a technological leap, but a foundational shift toward anticipatory, resilient financial security architectures.