Smart contract vulnerabilities have caused documented financial losses exceeding $6 billion across decentralized finance (DeFi) ecosystems between 2020 and 2024. Existing automated security toolsincluding Slither, Mythril, and Manticoreemploy rule-based static analysis that systematically fails to detect contextual, indirect, and semantically complex vulnerability patterns that are commonly exploited in production attacks. This paper presents CyberTrust AI, a production-deployed security analysis framework that applies large language model (LLM) inference via Anthropic's Claude Sonnet to perform contextual, semantic vulnerability analysis of Solidity smart contracts. The system identifies critical vulnerability classes including reentrancy attacks, integer overflow and underflow, unprotected self-destruct, unchecked external call return values, tx.origin authentication abuse, timestamp manipulation, and access control logic flawsgenerating severity-classified structured findings, natural language attack vector explanations, automated Solidity remediation code, and cryptographically verifiable onchain NFT trust scores. The complete system has been deployed at cybersheild-sooty.vercel.app and implements seven production capabilities: multi-mode audit analysis (security audit, threat simulation, gas optimization), realtime streaming analysis output, batch multi-file contract auditing, side-by-side contract diff comparison, conversational AI chat assistance for vulnerability Q&A, and a public trust score leaderboard. We conducted preliminary evaluation on 35 annotated Solidity contracts covering five canonical vulnerability types, demonstrating that LLM-based contextual analysis successfully detects all vulnerability instances while conventional static analysis tools miss approximately 13% of contextual casesparticularly indirect reentrancy patterns and access control logic errors requiring cross-function semantic reasoning. A full quantitative comparative evaluation is in progress.
Dr. Pankaj Malik, Mohit Kapoor, Akshat Gupta, Aman Singhai · 5 authors
The rapid expansion of decentralized finance (DeFi) platforms has been accompanied by a surge in rug pull scams, where malicious actors exploit liquidity pools and abandon projects, causing substantial investor losses. Existing detection approaches are largely centralized and platform-specific, limiting their effectiveness due to privacy constraints, fragmented data sources, and the dynamic behavior of blockchain ecosystems. This paper proposes a novel Federated Time-Series Learning (FTSL) framework for cross-platform rug pull detection that enables collaborative model training without sharing raw transaction data. The proposed system integrates federated learning with advanced time-series modeling to capture temporal patterns in token price volatility, liquidity changes, transaction frequency, and smart contract activities. A hybrid deep learning architecture combining Long Short-Term Memory (LSTM) networks with an attention mechanism is employed to effectively learn sequential dependencies and identify early indicators of fraudulent behavior. The federated setup ensures privacy preservation while enabling knowledge sharing across multiple decentralized platforms. Experimental results on multi-chain DeFi datasets demonstrate that the proposed FTSL model achieves 96.3% detection accuracy, outperforming traditional centralized models (91.2%) and single-platform approaches (88.7%). The model also improves precision (95.1%), recall (94.6%), and F1-score (94.8%), indicating robust and balanced performance. Furthermore, the system is capable of detecting rug pull events 6–12 hours earlier than baseline methods, providing critical early warning signals. Communication overhead is reduced by approximately 28% through optimized federated aggregation, while maintaining scalability across heterogeneous platforms. These findings highlight that Federated Time-Series Learning offers a scalable, privacy-preserving, and highly effective solution for real-time rug pull detection, contributing to enhanced security, transparency, and trust in decentralized financial ecosystems.
Decentralized finance (DeFi) vaults are smart-contract-based asset management systems that pool deposits, execute programmable strategies, and mint tokenized shares representing claims on underlying assets and strategy performance. As vault designs have evolved from early yield aggregators to modular, actively managed systems, a new control layer, curation, has emerged to select strategies, configure risk parameters, and coordinate operational execution, introducing principal-agent dynamics and new failure modes. This paper systematizes DeFi vault architectures and curator-mediated control planes through (i) a unified system model and formal definitions for share accounting, roles, and operational dependencies, and (ii) three complementary taxonomies covering vault exposures and objectives, curator governance and accountability mechanisms, and strategy execution patterns together with their failure modes. We further map a representative set of production protocols to the proposed dimensions. The frameworks in this work aim to support rigorous analysis and safer design of blockchain-based financial applications.
Lending protocols in decentralized finance have traditionally relied on over-collateralization mechanisms, where investor protection is primarily ensured through the automatic liquidation of collateral. While effective from an operational perspective, this approach limits the economic role of credit when compared with under-collateralized structures. In such settings, the prudential management of credit risk becomes a central element for protocol sustainability. This paper proposes a prudential framework for decentralized lending protocols by introducing an additional protection layer based on the distinction between tokenized loss-absorbing capital, an operational buffer, and a prudential reserve. The model defines three classes of subordinated instruments-First Loss Token, Contingent Capital Token, and Subordinated Backstop Token-arranged according to a progressive loss waterfall. The model is first applied to public data from Goldfinch and then extended to a TrueFi dataset, with the aim of assessing the ability of the policy to reduce losses borne by senior liquidity providers. The model shows a net reduction in losses. The sensitivity analysis confirms that the mechanism maintains a positive net benefit across variations in instrument costs, risk weights, and loss severity. The results suggest that an explicit prudential layer may contribute to strengthening the resilience of DAO-based credit protocols by making the prudential cost of risk-taking more transparent and by distinguishing between available liquidity, loss-absorbing capital, and protective reserves.
This paper introduces AURORA, a predictive institutional risk intelligence architecture designed to detect market manipulation and systemic fragility within decentralized finance ecosystems. The framework integrates multi-chain blockchain monitoring, behavioral graph analytics, liquidity stress modeling, and causal verification to identify engineered market abuse and cross-protocol contagion risks. AURORA further translates technical detection outputs into structured compliance classifications aligned with emerging regulatory regimes including the UK Market Abuse Regime for Cryptoassets (MARC) and the EU Markets in Crypto-Assets Regulation (MiCA). The study contributes to financial regulation, market microstructure analysis, and systemic risk modeling by proposing a unified institutional surveillance architecture for decentralized financial markets.
Effective risk management has grown more and more crucial in the complex world of international trade finance, bolstered by security, trust, and openness. By creating an integrated system that blends Hyperledger Fabric blockchain technology, Supply Chain Finance (SCF) protocols, and Generative Adversarial Networks (GANs), this study seeks to improve the intelligence and dependability of financial risk assessment. Four interrelated steps make up the suggested approach: (1) preprocessing and encoding SCF datasets; (2) creating synthetic risk data with GANs to mimic uncommon or dishonest trade behaviors; (3) using Hyperledger Fabric to execute smart contracts and log transactions decentralized; and (4) using real-time SCF compliance modeling for dynamic risk assessment. While blockchain guarantees the transparency, immutability, and auditability of financial records, GAN integration improves the prediction model by adding value to the training corpus. Comparative studies show that the suggested system considerably lowers the likelihood of data tampering and improves risk prediction accuracy by 12% when compared to traditional machine learning models. The results demonstrate that integrating generative modeling with blockchain technology can significantly improve financial risk management, transparency, and adaptability in global trade settings.
Decentralized finance liquidity providers (LPs) who use their Uniswap v3 positions as collateral on lending platforms such as Aave often face liquidations because these platforms rely on fixed Loan-to-Value (LTV) rules. These rules do not account for Impermanent Loss which can increase rapidly when asset prices move outside an LP's chosen price range. To address this, we simulated Uniswap v3 LP positions using historical ETH/USD price data and compared the standard fixed-LTV lending model with a hybrid risk-management framework that incorporates stress testing and dynamic exposure reduction. Under the conventional 65% LTV model, liquidations were frequent, with 39,649 liquidation events observed over roughly a decade of daily price data (2015-2025). The proposed framework reduced liquidations by 97.66% while maintaining healthier collateral positions, achieving this through adaptive reductions in effective leverage rather than full liquidation. These findings suggest that incorporating impermanent loss-aware risk controls into DeFi lending protocols could significantly reduce liquidations while keeping leveraged positions safer through periods of volatility. By combining the accessibility of DeFi with the risk management techniques used in traditional finance, lending platforms can become more stable and efficient, benefiting liquidity providers.
Blockchain technology has revolutionized numerous industries by providing decentralized, transparent, and immutable ledgers. However, its adoption is hindered by persistent security challenges, including arbitrage attacks, liquidity exploits, and noncompliance with antimoney laundering (AML) regulations. This paper proposes an enhanced framework to address these issues, combining dynamic pricing mechanisms, AI‐based anomaly detection, and regulatory compliance checks within a multilayered architecture. The framework is composed of five interconnected layers: the input layer for data collection and validation, the data warehouse layer for structured data classification, the processing layer for anomaly detection and pricing adjustments, and the decision layer for transaction validation, execution, and reporting. The integration of these layers ensures robust security and compliance mechanisms, reducing system vulnerabilities while optimizing efficiency. To validate the proposed framework, we conducted simulations using real‐world blockchain scenarios, including decentralized finance (DeFi) platforms and cryptocurrency exchanges. Results demonstrate significant reductions in arbitrage opportunities and liquidity risks, with improved accuracy in anomaly detection and compliance adherence. For instance, the dynamic pricing mechanism mitigated 87% of arbitrage attack attempts, while the AI‐based anomaly detection achieved an 89% accuracy rate in identifying high‐risk transactions. This study provides actionable insights and a scalable solution for enhancing blockchain security and trust. Future work will focus on integrating cross‐chain interoperability, real‐time threat intelligence, and privacy‐preserving techniques to further expand the framework’s applicability. By addressing critical vulnerabilities, this research contributes to the development of secure, transparent, and compliant blockchain ecosystems, paving the way for wider adoption across industries. Unlike previous blockchain security models, our framework introduces a real‐time, AI‐enhanced risk assessment mechanism that dynamically updates transaction risk scores, mitigating financial threats in decentralized environments. This holistic approach provides a scalable, explainable, and adaptive security system that not only protects decentralized financial infrastructures but also aligns with emerging regulatory requirements, ensuring long‐term applicability.
Sai Srikanth Madugula, jose Luis de la Rosa Esteva, Daya Shankar
This paper presents an integrated framework for decentralized invoice-backed loan underwriting combining interpretable machine learning, dynamic pricing algorithms, and on-chain trust infrastructure. We develop and validate SHAP-explainable ML models for real-time default probability assessment, design a Reverse Kelly AMM smart contract for optimal risk-adjusted loan pricing, integrate ERC-725 identity and on-chain reputation scoring with an automated insurance reserve, and deploy the system on Ethereum testnet with end-to-end functional and security testing. Stress testing across simulated default and fraud scenarios demonstrates the model achieves AUC-ROC of 0.89 on validation data, maintains LP yields of 12–18% under normal conditions while containing non-performing loan ratios below 3% under adverse scenarios, and sustains reserve solvency across 95th percentile stress events. The framework addresses critical gaps in DeFi lending by bridging regulatory interpretability requirements with decentralized credit assessment, demonstrating both technical feasibility and economic viability for permissionless SME financing at scale.
Ye Tian, Liangliang Song, Yuanyuan Ma, Yanbin Wang · 7 authors
Detecting fraudulent accounts on Ethereum is critical for securing decentralized finance (DeFi) ecosystems. Although temporal transaction dynamics offer richer behavioral signals than static graphs, existing methods struggle to jointly model the continuous, directional, and community-level nature of fraudulent fund flows. Specifically, they inadequately capture two key cues: directional-temporal transaction patterns (integrating directionality and temporal dynamics) and higher-level behavioral communities formed by accounts with similar transactional and temporal traits.To address these, we propose TimeTrace, an unsupervised graph representation learning framework for fraud detection in Ethereum transaction networks. TimeTrace models temporal behaviors via a Directed Temporal Aggregation mechanism that explicitly distinguishes incoming and outgoing flows while adaptively emphasizing recent interactions to capture pattern evolution. Building on these directional representations, TimeTrace further incorporates a differentiable clustering module to identify latent behavioral communities and encode cluster-level relational semantics. To enhance representation coherence and structural consistency, we introduce Cohesive Embedding Regularization (CER)—a graph refinement objective combining a global Laplacian term for structural consistency, an intra-cluster Laplacian term for cluster compactness, and a fidelity term to retain initial clustering information. Additionally, we construct a new phishing account benchmark with up-to-date Ethereum transaction records for realistic evaluation. Extensive experiments on three datasets demonstrate that TimeTrace outperforms state-of-the-art methods in both accuracy and efficiency, achieving F1-score improvements of 3.62\%-10.83\% and inference speeds at least as fast as the quickest baseline.
DharmaCoin is an artificial intelligence-based cyber fraud detection system that is supposed to establish a safe and moral online financial system. The suggested model enables combining blockchain security, fraud detection with the use of artificial intelligence, biometric multi-factor authentication, and secure document verification to enhance the safeguarding of digital financial crimes. It has a framework built on the values of Indian philosophies based on logic, transparency, and ethical governance and in the name of responsible and trust-worthy digital finance. DharmaCoin manages to identify transaction fraud with a 97% F1-score and forged/altering documents with 99% accuracy in using state-of-the-art technologies SatyaAI, a real-time deepfake and identity-checking tool, and RishiGuard, an anomaly financial activity detecting tool, in 200 milliseconds. The system uses a Proof-of-Stake blockchain registry to ensure unalterable records of transactions, and to increase the level of transparency within financial deals. Performance checks show that the blockchain infrastructure has the capability of handling a rate of transactions of over 1000 transactions in less than five minutes as well as be able to verify transactions in less than five seconds hence scaling to high financial volume environments. A Pilot project involving a banking partner found that a reduction of up to 40 in time to process KYC manually led to a faster approach to operations and better fraud detection. In general, DharmaCoin offers a safe, transparent, sustainable digital finance system, which is a moral and tech-centered solution to the prevention of cyber fraud and promotes the trustful financial ecosystem in accordance with the global sustainability development objectives.
The rapid growth of Ethereum blockchain transactions has led to increased vulnerability to fraudulent activities. Traditional rule-based detection systems fail to capture complex and evolving fraud patterns. This paper presents an unsupervised deep learning approach using an Autoencoder to model normal transaction behavior and detect anomalies based on reconstruction error. The model is trained on normal transactions and evaluated on a dataset of 9,841 Ethereum accounts with 45 features. The system achieves a recall of 78.7%, precision of 69%, F1-score of 0.74, and ROC-AUC of 0.70. The proposed approach demonstrates the effectiveness of unsupervised learning for fraud detection without requiring labeled datasets.
With the rapid development of financial technology and the digital economy, fraud detection in financial transactions faces increasing challenges due to complex transaction networks, temporal dependencies, and nonlinear interactions.This study proposes an RL-LGNN framework that integrates long short-term memory (LSTM) networks, graph neural networks (GNN), and reinforcement learning (RL) for fraud detection in the financial transaction settlement process.LSTM is used to encode historical transactions as temporal sequences and extract time-dependent behavioural features.GNN then models inter-node transaction relationships and captures structural information from the transaction graph.On this basis, RL is introduced to dynamically optimise the detection strategy, thereby improving model adaptability and robustness.Experimental results on both public and real-world datasets show that the proposed framework outperforms conventional methods and achieves fraud detection accuracy above 90%.The proposed framework provides an effective solution for fraud detection in financial transaction settlement.
The book Solutions and Technologies for Modern Business stands as a relevant contribution to understanding the technological and strategic transformations impacting the contemporary business environment.With a broad approach, the work offers reflections on innovative solutions, technological tools, and management practices aimed at strengthening and adapting organizations in the face of constant market changes.By emphasizing the integration of theory and practice, the book contributes to the development of critical analyses regarding the use of technology in organizational processes, highlighting its importance for competitiveness, innovation, and decision-making.The work brings together diverse perspectives that enrich academic debate and encourage the development of more efficient and sustainable strategies in the business context.This book is recommended for professors, students, and professionals in the fields of administration, management, technology, and business, as well as for anyone interested in expanding their knowledge of solutions and technologies applied to the corporate environment.It is a work that fosters learning, reflection, and the improvement of organizational practices in the digital era.
This paper develops computational methods for optimizing revenue recognition in machine learning platforms operating on cloud computing infrastructure. We analyze how Artificial Intelligence as a Service (AIaaS) platforms leverage distributed computing architectures, containerization technologies (Docker, Kubernetes), and microservices patterns to deliver AI capabilities, creating complex revenue recognition challenges under IFRS 15. Our research employs algorithmic analysis to examine five critical technical challenges: (1) computational resource allocation tracking across multi-tenant cloud environments, (2) real-time transaction price determination using usage metering APIs and consumption-based billing algorithms, (3) automated revenue allocation across platform components using distributed ledger technologies, (4) temporal revenue recognition optimization through event-driven architectures and streaming data processing, and (5) network effect quantification using graph algorithms and data analytics.
Ethereum has become a significant trading platform for financial activities such as Dapps, ICOs, and DeFi. However, it has also become a hub for criminal activities such as fraud, money laundering, and illicit fundraising. The construction of fraud detection models employing machine learning techniques is currently a mainstream research direction. Nevertheless, existing studies face significant challenges, including class imbalance in data samples and a lack of model interpretability. In this content, this work proposes a novel explainable model for Ethereum illicit account detection, ETHIAD (Ethereum Illicit Account Detection). Firstly, we pre-process the dataset by ADASYN oversampling and Lasso feature selection, etc., to more efficiently achieve feature modeling of transaction structures. Then, the ETHIAD model is trained using the XGboost algorithm, with an accuracy, precision, recall, F1 score, and AUC value of 99.70%, 99.51%, 99.02%, 99.26%, and 99.45%, respectively, the model outperforms the existing SOTA model by 0.05%-1.1%. Finally, we introduce SHAP framework to analyze the key influencing factors of illicit accounts from multiple perspectives, and the conclusions strongly enhance the explainability of the model.
Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.
Vladyslav Prosolov, Oleksandr Kushnerov, Vladyslav Sokol, Ruslan Trofymenko
Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.
Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.
Jennifer Bala, Sikiru O. SUBAIRU, Noel M. DOGONYARO, Joseph A. OJENIYI · 5 authors
Blockchain technology, particularly Ethereum, has revolutionized decentralized finance by enabling transparent, secure, and programmable smart contracts. However, these same features have created avenues for financial crimes such as Ponzi schemes, where fraudulent actors exploit pseudonymity and the absence of centralized oversight to deceive investors. This study develops an optimized hybrid detection model that combines eXtreme Gradient Boosting (XGBoost) and Gated Recurrent Units (GRU) to identify Ponzi schemes in Ethereum transaction networks. The model integrates XGBoost’s capability for structured feature learning with GRU’s temporal sequence modeling to capture both static and dynamic behavioral patterns of smart contracts. Using a dataset of 3,866 labeled Ethereum contracts obtained from Kaggle, the research employed advanced preprocessing, temporal sequence enrichment, and class balancing through SMOTE-TS to mitigate data imbalance. Bidirectional optimization, incorporating attention-enhanced GRUs and Bayesian hyperparameter tuning for XGBoost, further improved learning performance and generalization. The model was evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, achieving higher detection accuracy of 99% (F1-score = 0.945, ROC-AUC = 0.983) than standalone XGBoost or GRU models. Results demonstrate the hybrid model’s superior ability to detect temporal and statistical anomalies, reducing false negatives and improving early detection of fraudulent contracts. The approach contributes a scalable and interpretable framework for real-time Ponzi detection in blockchain ecosystems. This research not only enhances the reliability of Ethereum’s financial ecosystem but also offers regulators and developers a novel tool for proactive fraud prevention. Future work could extend this framework to multi-chain detection systems and real-time forensic monitoring.
This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.
Sr IT Developer, First Horizon Bank, Memphis, TN, USA, Rushikesh Anantrao Deshpande
The article examines methods for optimizing PL/SQL queries in distributed banking databases, emphasizing the transition from static rule-based mechanisms to adaptive, learning-driven architectures. The study’s relevance is defined by the increasing complexity of financial data environments that require real-time consistency, fault tolerance, and intelligent workload distribution. The research synthesizes results from seven recent works published between 2021 and 2025, covering neural cost modeling, heuristic algorithms, hybrid plan enumeration, and visualization-based diagnostics. Special attention is devoted to learned cost models and metaheuristic strategies that enhance selectivity estimation, reduce latency, and stabilize throughput in distributed ledger systems. The methodological framework integrates comparative analysis, systematization, and critical evaluation of hybrid, heuristic, and learning-based optimizers. The findings reveal a multi-layered optimization model that combines probabilistic inference, robust plan selection, and heuristic refinement. The conclusions underscore the practical applicability of adaptive PL/SQL optimization for high-volume banking infrastructures and data-intensive financial analytics.