KENOS — Kohenoor Operating System Official Description and Public Disclosure KENOS, the Kohenoor Operating System, is the unified digital operating environment of the Kohenoor ecosystem. It brings together artificial intelligence, Education 3.0, blockchain infrastructure, hybrid finance, business applications, development tools, governance controls and operational supervision within one coordinated ecosystem. <Explainer film added> The transition from KENHYFI Hub to the broader KENOS architecture reflects the continued expansion of the Kohenoor ecosystem. KENHYFI was originally developed as a hybrid-finance and ecosystem hub. However, the name and positioning of KENHYFI did not fully represent the wider capabilities that had developed around it, particularly: KAI — Kohenoor Artificial Intelligence, the ecosystem’s multilayered intelligence powerhouse and orchestration system. ProEdge, the Education 3.0, professional learning and workforce-development hub. Blockchain, development, commerce, security, governance and institutional-support applications extending beyond hybrid finance. For this reason, KENOS was established as the umbrella operating environment for the complete ecosystem. KENHYFI remains an important integrated hub within KENOS, but it no longer represents the entire ecosystem by itself. The relationship is therefore defined as follows: KENOS is the complete Kohenoor Operating System and umbrella ecosystem. KAI is the principal intelligence and orchestration powerhouse of KENOS. ProEdge is the principal Education 3.0 and professional-learning hub. KENHYFI is the integrated hybrid-finance and ecosystem-services hub within KENOS. Other applications and modules provide specialized capabilities in blockchain, commerce, development, security, finance and operational management. KENOS is built on three foundational pillars: Education 3.0 Artificial Intelligence Blockchain These pillars support the complete digital-economic journey: Learn → Plan → Build → Execute → Analyze → Supervise → Improve → Scale Artificial Intelligence Pillar KAI, Kohenoor Artificial Intelligence, serves as the principal intelligence powerhouse of KENOS. KAI is designed as a multilayered hybrid-intelligence and workflow-orchestration system rather than a conventional chatbot. It supports knowledge retrieval, document analysis, specialist-role activation, business intelligence, financial analysis, educational guidance, application planning, risk assessment, reporting, workflow coordination and Human-in-the-Loop escalation. Within KENOS, KAI connects users, knowledge, applications, workflows and authorized human decision-makers. Education 3.0 Pillar ProEdge serves as the principal learning and professional-development hub within KENOS. It supports practical education, workforce transformation, professional training, institutional capacity building and industry-linked learning in areas including: Artificial intelligence Blockchain and Web3 Business intelligence Cybersecurity Hybrid finance Digital transformation Communication and professional skills Software and application development Entrepreneurship and business execution ProEdge ensures that KENOS is not limited to providing technology. It also develops the human capability required to understand, manage and apply that technology effectively. Blockchain Pillar The blockchain pillar provides smart contracts, programmable assets, digital ownership, transparent records, settlement mechanisms, token utilities and verifiable ecosystem operations. Blockchain functions are designed to operate alongside KAI-supported intelligence, business rules, governance controls and authorized human supervision. Purpose of KENOS KENOS is designed to support individuals, professionals, businesses, educational institutions, developers, government entities and other organizations participating in the AI-powered digital economy. It connects learning with intelligence, intelligence with execution and execution with monitoring and supervision. KENOS may support: Education and professional development Artificial intelligence and business intelligence Financial and hybrid-finance services Blockchain and smart-contract development Digital commerce and procurement Application and software development Security and operational resilience Governance and institutional intelligence Reporting, monitoring and supervision Development Status At the time of this publication: KENOS is in the Early Beta phase. KENHYFI Hub is in the Alpha+ phase. Individual applications and modules may have different levels of development, testing and availability. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net Within the KENOS architecture: KAI serves as the principal intelligence and orchestration layer. KENHYFI Hub operates as an integrated hybrid-finance and ecosystem services hub. Education 3.0 platforms support learning, reskilling and professional development. Blockchain applications provide smart-contract, digital-asset, settlement and verification capabilities. Business and development modules support planning, commerce, procurement, software development, financial intelligence, security, reporting and operational management. KENOS is intended to serve individuals, professionals, businesses, educational institutions, developers, government organizations and other entities participating in the AI-powered digital economy. The architecture is modular and may support public web access, controlled organizational deployments, private-cloud environments, local installations, sovereign infrastructure and integration with existing enterprise systems. Governance remains a core element of KENOS. High-stakes activities are intended to remain subject to authorized human review, role-based permissions, validation controls, risk classification, activity logging and Human-in-the-Loop approval. At the time of this publication, KENOS is in the Early Beta phase, while KENHYFI Hub is in the Alpha+ phase. Applications and modules within the ecosystem may therefore have different levels of development, testing, availability and production readiness. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net This publication provides the official conceptual definition, ecosystem positioning, service scope, architectural relationships, development status, governance principles and public-disclosure framework of KENOS. Keywords: KENOS; Kohenoor Operating System; Kohenoor Technologies; KAI; Kohenoor Artificial Intelligence; KENHYFI; Education 3.0; artificial intelligence; blockchain; hybrid finance; digital economy; business intelligence; digital transformation; smart contracts; Human-in-the-Loop; ecosystem architecture; AI governance; Web3; enterprise AI; institutional intelligence Kohenoor Technologies remains committed to transparency, security, responsible disclosure, and continuous improvement of the KEN ecosystem. #kenhyfi #kai #hyfi #kohenoortechnologies #futureofeducation #futureoffinance #futureofai #kohenoorken #cryptocurrencies #kohenoorken #AI #actionai #agenticai #AGI #ArtificialGeneralIntelligenceAGI #AIAssistant #education3 #defi #hybridfinance #hyfi #cedefi #blockchain #innovation #settlements #auditreadycertificates #DASC #cybersecurity #web3 #businessintelligence #proedge #industrygradetrainings #quantumcomputing
Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisions through structured evidence: (1) DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks; (2) deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and (3) data-health and confidence gates constrain escalation before DeXposure-Claw emits auditable supervisory tickets with rationales. We further develop DeXposure-Bench, a six-axis evaluation harness, whose decision axis scores tickets against a regulator-aligned absolute-loss ground truth and an explicit false-intervention rate. Experiments on five years of weekly real data fully support our system. Code is at https://github.com/EVIEHub/DeXposure-Claw.
With the rapid development of digital finance, the mode of enterprise credit risk assessment has changed, and now also requires methods that can handle large-scale, diverse data and smart computation. The old system of credit rating has been based on the results of past financial reports and is no longer suitable for evaluating the changes and risks in modern corporate finance. This paper proposes a multi-dimensional model for mining credit reports of mining enterprises and combines structured financial data, transaction information, operating indicators, and other unstructured auxiliary data such as social media presence, online communication, supply chain dynamics, etc. By building a relatively detailed credit report, the bank can gain some information on the risk of a company's credit and its repayment ability for a loan. Algorithms that use machine learning, deep learning, ensemble models and predictive analysis are also known as intelligent default risk assessment algorithms that enhance the accuracy and flexibility of credit assessment. The following are ways to discover abnormal or complex patterns in a large amount of data early on for risk early warning, online credit assessment and dynamic portfolio management. Interoperability of digital finance platforms can support lifelong learning, automation and scalable high-frequency financial data, and maintain security, privacy and regulatory compliance. Although the above have been achieved, there are still deficiencies in the quality of data, interpretability of models, adherence to regulations, and sufficient computational resources, especially for small and medium-sized enterprises and new market institutions. Future research directions include building explainable AI systems, continuous learning, integrating multiple types of data (multimodality), and decentralized finance (DeFi) based on blockchains. At this point, the above technologies are expected to help enterprises strengthen credit risk management in the age of digital finance and provide more accurate and timely credit evaluations.
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
The integration of machine learning into decentralized finance (DeFi) credit assessment is frequently undermined by opaque algorithms and severe methodological flaws regarding data leakage. This paper presents a rigorous, fully reproducible framework for explainable artificial intelligence (XAI) in invoice-backed default risk modeling. Utilizing a highly imbalanced dataset of 12,000 corporate loan originations, we engineer an XGBoost ensemble model that achieves an AUC-ROC of 0.89. We systematically eliminate the pervasive data leakage associated with the Synthetic Minority Over-sampling Technique (SMOTE) by implementing a dynamic crossvalidation pipeline, ensuring synthetic data generation is strictly isolated to training folds. To satisfy institutional accounting standards for expected loss (e.g., IFRS 9), we mathematically formulate and validate the Expected Calibration Error (ECE), achieving a highly calibrated probabilistic output of 0.08. Furthermore, we extract local explanations using SHAP (SHapley Additive exPlanations), imposing strict constraints on the background reference dataset to guarantee mathematical additivity and prevent stochastic approximation transitions. Our findings reveal that Days Payment Outstanding (DPO) and invoice age are primary default drivers, while on-chain reputation effectively mitigates perceived risk. Finally, we address critical privacy vulnerabilities, mathematically modeling Membership Inference Attacks (MIAs) on synthetic records. This work establishes a regulatory-compliant, structurally sound ML foundation for permissionless credit provision.
Predictive Churn Modeling and Proactive Service Using Customer Interaction Data Objectives: 1. To provide a comprehensive understanding of cloud-native architectures and middleware technologies used for designing scalable, resilient, and high-performance financial trading systems. 2. To explain the core concepts of microservices, containerization, orchestration, distributed messaging, and data management that power modern financial platforms and digital banking ecosystems. 3. To demonstrate the practical implementation of advanced technologies such as Kubernetes, Apache Kafka, Redis, gRPC, and AI-driven solutions for real-time trading and financial service delivery. 4. To equip software engineers, solution architects, researchers, and FinTech professionals with the knowledge required to build secure, fault-tolerant, low-latency, and highly observable trading infrastructures. 5. To explore emerging trends in financial technology, including serverless computing, WebAssembly, Artificial Intelligence, Machine Learning, and Decentralized Finance (DeFi), preparing readers for the next generation of cloud-native financial systems. Table of Contents CHAPTER 1 The Foundation of Customer Retention: Concepts and Definitions CHAPTER 2 The Business Value of Predicting Churn: Impact on ROI CHAPTER 3 Sources of Customer Interaction Data: CRM, Logs, and Beyond CHAPTER 4 The Architecture of a Churn Prediction System CHAPTER 5 Data Acquisition and Quality Assessment CHAPTER 6 Preprocessing High-Dimensional Interaction Data CHAPTER 7 Feature Engineering: Creating Meaningful Indicators from Raw Data CHAPTER 8 Exploratory Data Analysis for Churn Patterns CHAPTER 9 Traditional Statistical Methods in Churn Modeling CHAPTER 10 Machine Learning Approaches: From Random Forests to XGBoost CHAPTER 11 Deep Learning for Temporal Interaction Sequences CHAPTER 12 Natural Language Processing for Sentiment-Based Churn Analysis CHAPTER 13 Handling Class Imbalance in Churn Datasets CHAPTER 14 Evaluating Model Performance: Beyond Accuracy CHAPTER 15 Interpreting Black-Box Models for Stakeholder Trust CHAPTER 16 Real-Time Churn Scoring and Pipeline Automation CHAPTER 17 Designing Proactive Service Interventions CHAPTER 18 Personalized Marketing and Customer Success Strategies CHAPTER 19 Ethical Considerations and Data Privacy in Churn Modeling CHAPTER 20 Case Studies and Future Trends in Predictive Analytics
Subrat Kumar Jena, Gayatri Palai, Asst. Prof. Rumana Hasinullah Shaikh
Abstract-The rapid expansion of the global gig economy has fundamentally changed the structure of personal finance management. Unlike salaried professionals who operate within predictable monthly income cycles, freelancers and independent contractors face highly volatile cashflow patterns characterized by delayed client payments, irregular project pipelines, seasonal fluctuations, and unstable liquidity reserves. Traditional Personal Financial Management (PFM) systems primarily focus on historical transaction tracking and static budgeting, making them ineffective for proactive financial survival planning in modern freelance ecosystems. This project introduces Prophet AI v1.1, an AI-driven financial intelligence platform engineered specifically to simulate, forecast, and analyze unstable freelance cashflow environments using distributed cloud infrastructure, cryptographic verification, and real-time neural intelligence. The proposed system functions as a Financial Flight Simulator that allows freelancers to model financial risk before it becomes catastrophic in real life. The platform combines machine learning-based forecasting, stochastic risk simulation, cryptographic integrity validation, asynchronous AI orchestration, and multilingual neural voice synthesis within a single integrated ecosystem. The system architecture follows a distributed deployment model consisting of a Next.js 14 frontend hosted on Vercel, a FastAPI Intelligence Gateway hosted on Render, and a Supabase PostgreSQL secure transaction vault. This decoupled architecture ensures scalability, modularity, low frontend latency, and reliable handling of long-running AI inference tasks. The financial forecasting engine utilizes a hybrid intelligence pipeline combining statistical forecasting principles and ensemble-based analytical logic. The platform generates 30-day rolling liquidity forecasts, safe spending corridors, and stress-based runway simulations that help users evaluate financial survival scenarios under varying burn conditions. Unlike conventional financial dashboards, Prophet AI introduces dynamic What-If simulation controls, allowing users to manipulate variables such as liquidity lag, expense escalation, and delayed client payments in real time. To establish institutional-grade trust and forensic-grade auditability, the system implements an Integrity Shield powered by the SHA-256 cryptographic hashing algorithm. Every transaction entered into the system generates a unique digital fingerprint using transaction attributes including amount, date, category, and user identification. This verification mechanism ensures that tampered or manipulated financial records cannot enter the intelligence pipeline, thereby maintaining a Verified Ledger architecture. The project additionally documents real-world deployment challenges involving decimal precision mismatches between JavaScript and Python environments and explains the implementation of strategic normalization bypass mechanisms for stable production deployment. The intelligence layer of Prophet AI is powered using Llama 3.3-70B via Groq infrastructure, enabling high-speed financial reasoning and structured JSON-based strategy generation. The platform utilizes a carefully engineered Ruthless Financial Strategist system prompt designed to deliver direct, survival-oriented financial recommendations rather than emotionally comforting advice. This design philosophy reflects the real-world operational needs of freelancers who require accurate liquidity warnings and actionable strategic insights during financial instability. The generated intelligence is converted into multilingual audio briefings using the edge-tts neural voice synthesis engine, supporting both English and Hindi voice outputs. To avoid cloud timeout failures and synchronous processing bottlenecks, the platform implements an asynchronous polling architecture using UUID-based job orchestration. The frontend submits a /briefing request and continuously polls a /briefing-status/{job_id} endpoint until the AI-generated strategy and MP3 briefing become available. This architecture enables the system to safely execute computationally expensive large language model inference and neural voice generation workflows even on limited-resource cloud infrastructure. The completed system demonstrates the practical integration of distributed AI infrastructure, cryptographic verification, asynchronous backend engineering, financial forecasting, and multimodal intelligence synthesis within a real-world production environment. Prophet AI v1.1 represents a transition from passive financial recordkeeping to proactive survival-oriented financial intelligence. The project establishes a scalable blueprint for next-generation AI-powered fintech systems capable of delivering real-time strategic decision support for the rapidly growing global freelance economy.Keywords-Freelance finance; cashflow forecasting; stochastic simulation
Smart contract vulnerabilities in Decentralized Finance (DeFi) protocols resulted in over 1.49 billion USD in confirmed losses in 2024 alone, across 192 incidents [1]. As LLM-based vulnerability detection emerges as a promising approach to address these threats, the quality of evaluation datasets has become a critical bottleneck. Existing datasets suffer from three fundamental problems: they are built on outdated Solidity versions (e.g., v0.4) that no longer reflect modern DeFi contracts [5][6][7]; they rely on automated or LLM-generated annotations that introduce hallucination-driven label noise [9][10]; and they apply coarse single-layer labeling that fails to capture the semantic complexity of real-world business logic vulnerabilities [6][7][11][12]. We present Bastet, an expert-labeled DeFi smart contract vulnerability dataset that addresses all three problems through real-world audit findings (2021-2024), human expert annotation with discussion-based consensus, and a two-layer taxonomy of 46 Tags and 77 Subtags. Bastet comprises 4,402 findings collected from 394 Code4rena competitive audit reports spanning April 2021 to November 2024, of which 849 findings are fully annotated by white-hat security researchers from the DeFiHackLabs community. All annotations are produced through a two-annotator consensus workflow, ensuring label accuracy grounded in real-world vulnerability root causes.
Imran Hasan, Abdullah All Ahhad, Md Zamilur Rahman, Bikash Chandra Singh
Smart contracts enable decentralized applications across domains such as finance, logistics, and healthcare, but their immutable nature and complex execution logic make them highly susceptible to vulnerabilities, including reentrancy, integer overflows, and access control flaws. These weaknesses can lead to severe financial and operational losses. Traditional static or rule-based detection tools lack scalability and adaptability, while existing deep learning models often struggle with limited data, poor generalization, and the absence of actionable mitigation guidance. This paper proposes a hybrid multi-task learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for smart contract vulnerability detection, coupled with a transformer-based Large Language Model (LLM) for root cause analysis and dynamic mitigation generation. The framework extracts spatial opcode features using CNNs and captures temporal execution patterns via LSTMs, supported by preprocessing steps that include opcode extraction, positional encoding, static and dynamic analysis features, and data augmentation. A feature fusion module consolidates spatial and temporal information, while SHAP and LIME provide interpretability by identifying features driving model predictions. The mitigation layer employs an encoder–decoder transformer to map detected vulnerabilities to their underlying causes and generate context-aware remediation strategies. Experimental results show strong performance, achieving 93% accuracy, 90% precision, and an AUC-ROC of up to 90% across multiple vulnerability categories. Beyond accurate detection, the framework delivers explainable root cause insights and tailored mitigations, offering a scalable and adaptive solution for enhancing smart contract security in modern blockchain ecosystems.
Transaction monitoring and efficient audit management have become increasingly importantin modern financial systems. Traditional centralized databases and auditing methods often face challengesrelated to security vulnerabilities, fraudulent activities, and data manipulation. A blockchain-based financialtransaction monitoring system integrates smart contracts, decentralized ledgers, and audit trails to automatefinancial operations, enhance transparency, and reduce fraud risks. The proposed architecture is implementedon Ethereum and Hyperledger Fabric platforms, enabling automated transaction validation and executionthrough smart contracts. All transactions are stored in an immutable decentralized ledger, while audit trailsare generated and maintained automatically. Simulation results demonstrate a 40–60% reduction in fraudulentactivities and up to a 70% decrease in audit processing time compared with conventional approaches. Theapplication of cryptographic algorithms and Zero-Knowledge Proofs further strengthens data security andprivacy protection. The proposed solution contributes to the improvement of financial control and auditingsystems within the framework of the digital economy.
Abstract - Traditional machine learning-based fraud detection frameworks treat transaction registries as static, isolated, non-relational entities. While effective for simple localized pattern recognition, these methods are structurally blind to multi-hop relational dependencies, automated asset splitting, or continuous temporal dynamics characteristic of modern financial fraud within decentralized finance (DeFi) networks. This paper presents a complete structural paradigm utilizing Temporal Graph Neural Networks (TGNNs) to identify non-linear anomaly patterns directly in transaction graphs. By projecting raw financial data streams as dynamic, continuous-time directed graphs, our model learns evolving node and edge representations without relying on synthetic tabular oversampling mechanisms. Empirical simulation methodologies demonstrate that shifting the analytical paradigm from local, isolated classification to global temporal network topology minimizes false positives by 34.2% while significantly improving minority-class recall. Key Words: Credit Card Fraud, Graph Neural Networks, Temporal Embeddings, Class Imbalance, Deep Learning, Decentralized Finance (DeFi)
Pedro Henrique F. S. Oliveira, Heder S. Bernardino, Saulo Moraes Villela, Edelberto Franco Silva · 6 authors
O Ethereum é uma plataforma de criptomoedas que permite a execução de contratos inteligentes, programas autônomos que operam em uma rede descentralizada. As vulnerabilidades nesses contratos representam grandes riscos financeiros e de segurança nos ecossistemas blockchain, motivando a automatização do processo de detectá-las. Este trabalho estuda a detecção de vulnerabilidades em contratos inteligentes Ethereum usando embeddings derivados de bytecode. Embeddings são representações vetoriais geradas por modelos de linguagem, que capturam as características estruturais de texto. Essas representações foram usadas como entrada para os algoritmos de regressão logística, árvore de decisão e floresta aleatória, com o fim de detectar quais contratos possuem vulnerabilidades. Os resultados mostram que os embeddings contêm informações úteis para distinguir contratos vulneráveis de não vulneráveis. O estudo também constata que a alteração da distribuição original dos dados durante o treinamento afeta significativamente o desempenho, destacando a sensibilidade das abordagens baseadas em embeddings às estratégias de amostragem.
This paper examines the use of Event-Driven Architecture (EDA) patterns to improve the optimization of financial risk evaluation in a distributed cloud-based system of finance.Today's financial system is characterized by a number of difficulties in processing high-speed data feeds in a timely manner, ensuring sub-millisecond latency and high availability.This paper proposes a decoupled system utilizing distributed event brokers and stream processors to identify market anomalies and credit risks in a timely fashion.This research utilizes a risk data set of 404 unique risk scenarios, including high-frequency trading (HFT) simulation data and credit transaction data, to measure system efficiency.The system environment utilizes Apache Kafka for event streaming, Kubernetes for cloud orchestration, and Prometheus for monitoring.The results show that event-driven architecture can improve system efficiency by eliminating traditional requestresponse processing bottlenecks.Furthermore, by utilizing distributed ledgers and serverless architecture, financial organizations can improve their risk profile granularity.The results show that by utilizing reactive programming, financial organizations can improve their risk management approach by shifting their traditional reactive approach to a proactive approach.
Central bank digital currencies (CBDCs) integrated with decentralized finance (DeFi) represent a transformative development in digital financial systems. However, there is a lack of systematic frameworks for prioritizing the determinants of effectiveness and sustainability in DeFi-integrated CBDC platform investments. This study develops an integrated multicriteria decision-making framework to identify critical evaluation criteria and rank alternative platform architectures under uncertainty. The proposed model combines objective expert weighting, interaction-sensitive criteria evaluation, and fuzzy-based alternative ranking within a unified analytical structure. The results indicate that technological infrastructure (0.168) and liquidity (0.167) are the most influential criteria, while hybrid and privacy-focused platforms emerge as the most suitable investment alternatives. These findings highlight the importance of balancing technological robustness, liquidity depth, and privacy considerations in CBDC design. The study contributes by offering a structured and uncertainty-sensitive decision framework to support strategic platform selection and policy formulation in evolving digital currency ecosystems.
Blockchain-based financial systems process billions in transactions but remain vulnerable to sophisticated fraud schemes. Current detection approaches analyze completed transactions, preventing neither fund loss nor protocol exploitation. We address this through an oracle-mediated prevention system integrating machine learning inference with smart contract execution. Training ensemble models on 12,847 Ethereum transactions with engineered features capturing gas anomalies and temporal patterns, we achieve 94.2\% fraud classification accuracy. Testnet deployment demonstrates 1.09-second response latency with 6.8\% computational overhead, contrasting favorably against prior on-chain implementations requiring 34\% overhead. Our working prototype validates practical viability for production environments where security requirements justify marginal transaction costs.
To address the challenges of topological obscurity and extreme label sparsity in large-scale Ethereum transaction networks, a novel self-supervised phishing detection framework named Eth-GBAV is proposed, integrating graph attention, broad learning, and adversarial variational inference. The framework initiates with a biased random walk strategy guided by transaction intensity and temporal dynamics to capture the initial behavioral semantics of nodes. To distill discriminative features from noisy backgrounds, a “Generative-Attention” encoding architecture is constructed, where a graph attention network aggregates weighted structural neighborhoods and a Variational Autoencoder (VAE) characterizes the underlying probability distribution of legitimate transaction patterns. By maximizing the evidence lower bound, anomalous accounts are effectively isolated through reconstruction residuals. Furthermore, the broad learning system is introduced as an efficient analytical decision layer. By mapping VAE-derived latent embeddings and reconstruction errors into an expanded high-dimensional feature space, the framework captures intricate behavioral correlations via mapping and enhancement neurons. Extensive experimental verification on two large-scale datasets demonstrates the superior performance of Eth-GBAV. On the XBlock dataset, it achieves a leading F1-score of 0.9847 and a recall of 0.9839, outperforming the most competitive state-of-the-art model by significant margins. On the Kaggle dataset, the framework maintains high robustness with an accuracy of 0.9592 and an F1-score of 0.9069.
Rapid urbanization and the exponential growth of vehicles have led to severe traffic congestion, increased travel time, fuel consumption, and environmental pollution in metropolitan cities.Traditional traffic control systems, which rely on fixed-time signals and manual monitoring, are inadequate to handle dynamic and unpredictable traffic conditions.This project proposes a Smart Traffic Management System designed to optimize traffic flow and reduce congestion using advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and real-time data analytics.The system integrates smart sensors, cameras, and GPS-enabled devices to continuously monitor traffic density, vehicle movement, and road conditions.Data collected from these sources is processed using machine learning algorithms to predict traffic patterns and dynamically adjust traffic signal timings.Additionally, the system provides real-time route guidance to drivers through mobile applications and digital signboards, helping to distribute traffic evenly across the road network.Emergency vehicle prioritization and incident detection mechanisms are also incorporated to enhance response efficiency and safety.
Predictive analysis has become an essential component in modern financial research and practice, driven by the rapid advancement of data analytics, machine learning, and artificial intelligence. This study aims to systematically map the intellectual structure, research trends, and key contributions in the field of predictive analysis in finance through a bibliometric approach. Data were collected from the Scopus database covering publications from 2000 to 2026 and analyzed using VOSviewer to examine co-authorship networks, citation patterns, and keyword co-occurrence. The results reveal a significant growth in research output, particularly in recent years, reflecting the increasing importance of data-driven decision-making in finance. Co-authorship analysis indicates the presence of collaborative research clusters, although the field remains partially fragmented. Citation analysis highlights that the most influential studies are those integrating advanced computational methods with practical financial applications, such as credit scoring, bankruptcy prediction, and stock market forecasting. Furthermore, keyword analysis demonstrates a clear shift from traditional statistical techniques toward machine learning, artificial intelligence, and emerging technologies such as blockchain and decentralized finance. This study contributes by providing a comprehensive overview of the evolution and current state of predictive analysis in finance, identifying key research themes and gaps. The findings suggest that future research should focus on enhancing model interpretability, integrating sustainability considerations, and expanding applications in real-time financial decision-making. Overall, this study serves as a valuable reference for researchers and practitioners seeking to understand the trajectory and future direction of predictive analytics in the financial domain.
This study rigorously formulates the complex fund-scheduling problem as a Markov decision process (MDP). It constructs a state space that integrates real-time and forecast information, an atomic action space that conforms to business logic, and a reward function that balances long-term returns against immediate risk. To address the curse of dimensionality and the credit-assignment problem in coordinated scheduling among multiple fund units, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is adopted. Under a centralized-training and decentralized-execution framework, the algorithm reconciles global optimization with decentralized decision-making. In addition, a difference-reward mechanism and Kalman filtering are used to accurately measure each agent&rsquo;s individual contribution and reduce the impact of environmental noise on reward signals. The results show that, compared with a static rule engine and a conventional linear programming method, the proposed deep reinforcement learning strategy reduces average daily funding costs by 50.4%, lowers the payment failure rate to 0.002%, and maintains a high liquidity buffer adequacy ratio. The strategy also demonstrates clear advantages in decision timeliness, collaborative handling of complex instructions, and self-adaptation potential, thereby providing an innovative pathway for finance-company fund scheduling to progress from intelligentization to automation.
Financial fraud across cross-border payment systems and blockchain-based transaction networks has grown in scale, sophistication, and velocity, driven by increased digitization, regulatory fragmentation, and the pseudonymous nature of decentralized infrastructures.This study presents a comprehensive examination of AI-driven anomaly detection techniques designed to address these evolving threats.From a broad perspective, the paper reviews the global financial ecosystem, highlighting vulnerabilities in traditional correspondent banking frameworks and emerging decentralized finance (DeFi) architectures.It then narrows to advanced machine learning and deep learning approaches, including supervised, unsupervised, and hybrid models such as autoencoders, graph neural networks, and reinforcement learning systems for real-time fraud detection.Particular emphasis is placed on transaction pattern analysis, behavioral profiling, and network topology modeling to uncover hidden relationships and detect anomalous activities across distributed ledgers and cross-border payment rails.The study further evaluates challenges such as data sparsity, class imbalance, adversarial manipulation, privacy constraints, and regulatory compliance, including AML and KYC requirements.By integrating AI with blockchain analytics and financial monitoring systems, the paper demonstrates how adaptive, scalable, and explainable detection frameworks can significantly enhance fraud prevention capabilities.The findings provide strategic insights for financial institutions, regulators, and fintech developers aiming to strengthen global financial security.
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions. Existing risk management tools utilize static health-factor thresholds, which are reactive and fail to distinguish between administrative "dust" cleanup and genuine insolvency. In this work, we propose an autonomous agent that leverages time-to-event (survival) analysis and moves beyond prediction to execution. Unlike passive risk signals, this agent perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations. We introduce a return period metric derived from a numerically stable XGBoost Cox proportional hazards model to normalize risk across transaction types, coupled with a volatility-adjusted trend score to filter transient market noise. To select optimal interventions, we implement a counterfactual optimization loop that simulates potential user actions to find the minimum capital required to mitigate risk. We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles. The results demonstrate the agent's ability to prevent liquidations in imminent-risk scenarios where static rules fail, effectively "saving the unsavable" while maintaining a zero worsening rate, providing a critical safety guarantee often missing in autonomous financial agents. Furthermore, the system successfully differentiates between actionable financial risks and negligible dust events, optimizing capital efficiency where static rules fail.