Journal of Science, Innovation & Social Impact
type: channel
description: Journal of Science, Innovation & Social Impact (JSISI) is an international, refereed, double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research at the interface of scientific discovery, technological innovation, and societal impact. The journal welcomes interdisciplinary studies that advance both theoretical knowledge and practical applications, with a focus on topics such as emerging scientific breakthroughs, innovation-driven solutions, societal challenges, policy implications, and the broader social consequences of scientific and technological advancements.
language: en
publication name: Journal of Science, Innovation & Social Impact
items: Comparative Evaluation of Post-Hoc Feature Attribution Methods on Tabular Financial Data: Faithfulness, Stability, and Computational Efficiency
items: A Comparative Evaluation of Class Imbalance Handling Techniques for Credit Card Fraud Detection
items: Covariate Feature Importance and Cross-Indication Transferability Analysis for Phase III Oncology Trial Outcome Prediction
items: Benchmarking Robustness-Efficiency Trade-offs of Camera-LiDAR Fusion Strategies for 3D Object Detection Under Environmental Corruptions
items: Performance Evaluation and Optimization Strategies for Privacy-Preserving Document Classification in Distributed Learning Environments
items: A Multi-Dimensional Coverage Metric with Evolutionary Search for Safety-Critical Scenario Generation in Autonomous Driving Testing
items: Behavioral Association Analysis for Organized Fraud Ring Detection in Large-Scale User Interaction Data
items: Language-Driven Interactive Annotation for Pulmonary Nodules in Chest CT: An LLM Prompt-Translation and Multi-Round Refinement Approach
items: Privacy-Preserving Techniques in Credit Risk Assessment: A Comparative Analysis of Differential Privacy, Federated Learning, and Homomorphic Encryption
items: Performance Evaluation of Anomaly-Based Detection Approaches for Zero-Day Attack Early Warning in Cloud Infrastructure
items: Performance Benchmarking and Optimization of Deep Learning-Based Point Cloud Processing Algorithms for Industrial Quality Inspection
items: A Comparative Study of Machine Learning Methods for Automated Customer Service Dialogue Quality Assessment
items: Accuracy Evaluation of Machine Learning-Based Hospital Resource Demand Forecasting During Infectious Disease Surges: A Comparative Analysis
items: Enhancing Financial Compliance Transparency through Automated Data Governance and Intelligent Risk Reporting
items: Identifying Undisclosed Related Party Relationships and Revenue Recognition Irregularities: A Rule-Based Analytical Approach for Audit Planning
items: Comparative Evaluation of Supervised Learning Algorithms for Cancer Treatment Response Prediction Using Clinical and Biomarker Features
items: Multi-Dimensional Feature Analysis and Evaluation Methods for Anomalous Fund Flow Identification in Cross-Border Financial Transactions
items: Service Architecture and Optimization Strategies in Cloud-Based Big Data Platforms
items: Multi-Objective Optimization of Process Parameters for Dental Resin 3D Printing Using Improved NSGA-II Algorithm
items: A Comparative Study of Reinforcement Learning and Metaheuristic Algorithms for Real-Time Last-Mile Delivery Scheduling
items: FraudGuardian: Self-Supervised and Adversarial Learning for Robust Financial Fraud Detection
items: Context-Aware Classification of Verbal Operants in Children with ASD Using Deep Learning
items: Fairness-Aware Multimodal Fusion for Early Chronic Disease Risk Prediction: A Temporal Deep Learning Approach
items: StatFuse: Bridging Statistical Inference and Neural Prediction for Interpretable Forecasting
items: Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control
items: Study on Uncertainty Data Analysis for Common Natural Disaster Prediction in the U.S. Using Cloud Computing and Machine Learning
items: A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection
items: Theoretical Evolution and Frontier Trends of Corporate Strategic Management in the AI Era: A Literature Review
items: Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach
items: Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation

Comparative Evaluation of Post-Hoc Feature Attribution Methods on Tabular Financial Data: Faithfulness, Stability, and Computational Efficiency
type: item
description: The deployment of machine learning in credit scoring and fraud detection has intensified regulatory and societal demand for transparent decision-making. Post-hoc feature attribution methods such as SHAP, LIME, Integrated Gradients, and Anchors promise to explain individual predictions, yet their comparative reliability on financial tabular data remains insufficiently characterized. This study conducts a controlled empirical evaluation of four prominent attribution methods across four public financial datasets spanning credit scoring and transaction fraud detection. Three classifiers---XGBoost, Random Forest, and Multilayer Perceptron---serve as the underlying predictive functions. Explanation quality is quantified along three axes: faithfulness measured by Prediction Gap on Important features and infidelity, stability measured by max-sensitivity, and computational efficiency measured by wall-clock time per explanation. Results indicate that TreeSHAP achieves the highest faithfulness and lowest sensitivity on tree-based classifiers, while Integrated Gradients attains competitive faithfulness on neural networks. LIME exhibits the largest variance across repeated runs, raising concerns for regulatory settings that require reproducible explanations. Anchors produce the sparsest explanations at the cost of reduced faithfulness. No single method dominates all evaluation criteria simultaneously, corroborating recent theoretical predictions of an inherent trade-off among explanation desiderata. These findings provide practitioners and regulators with empirically grounded guidance for selecting attribution methods in financial applications.
subject: Articles
  topic: section
subject: explainable artificial intelligence
  topic: keywords
subject: feature attribution
  topic: keywords
subject: credit scoring
  topic: keywords
subject: faithfulness evaluation
  topic: keywords
creator: Pengyuan Xiao
creator: Xuanyi Fu
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 1
ending page: 11

A Comparative Evaluation of Class Imbalance Handling Techniques for Credit Card Fraud Detection
type: item
description: Credit card fraud detection presents a challenging classification task due to extreme class imbalance, where fraudulent transactions constitute less than 1% of all observations. Selecting an appropriate imbalance handling technique is critical, yet the comparative performance of these techniques under varying imbalance severities remains insufficiently understood. This study conducts a systematic empirical evaluation of nine class imbalance handling techniques across two publicly available fraud detection datasets exhibiting different imbalance ratios (578:1 and 90:1). The techniques evaluated span data-level resampling (SMOTE, ADASYN, Borderline-SMOTE, SMOTE combined with Edited Nearest Neighbors, and random undersampling), algorithm-level cost-sensitive learning (class weighting), and ensemble-based approaches (EasyEnsemble, RUSBoost, and Balanced Random Forest). Each technique is paired with four base classifiers---Logistic Regression, Random Forest, XGBoost, and LightGBM---and assessed using five evaluation metrics: AUC-ROC, PR-AUC, F1-score, Matthews Correlation Coefficient, and recall. Results indicate that ensemble-based methods, particularly EasyEnsemble, achieve the most consistent improvements across both datasets. Hybrid resampling via SMOTE with Edited Nearest Neighbors produces comparable gains among data-level methods. A notable finding is that standard SMOTE, while improving AUC-ROC and F1-score, can reduce PR-AUC relative to the untreated baseline under severe imbalance. Cost-sensitive class weighting emerges as a computationally efficient alternative that preserves strong PR-AUC performance. These findings provide practical guidance for practitioners selecting imbalance handling strategies in fraud detection applications.
subject: Articles
  topic: section
subject: class imbalance
  topic: keywords
subject: fraud detection
  topic: keywords
subject: resampling techniques
  topic: keywords
subject: ensemble learning
  topic: keywords
creator: Zijie Chen
creator: Pengyuan Xiao
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 131
ending page: 140

Covariate Feature Importance and Cross-Indication Transferability Analysis for Phase III Oncology Trial Outcome Prediction
type: item
description: Oncology drug development suffers from the lowest clinical success rates among all therapeutic areas, with the likelihood of approval (LOA) from Phase I to regulatory approval estimated at 3.4%--5.3%. Accurate prediction of Phase III trial outcomes is essential for optimizing resource allocation and reducing late-stage attrition costs. This study presents a comparative regression analysis framework designed to identify and rank covariate features that influence Phase III oncology trial success, and to evaluate the temporal stability and cross-indication transferability of these features. Using 1,203 Phase III oncology trials registered between 2005 and 2023, sourced from ClinicalTrials.gov and the BioMedTracker database, 42 candidate covariates spanning trial design, molecular characteristics, patient baseline demographics, and historical performance metrics were extracted and analyzed. SHAP-based importance ranking revealed that prior Phase II efficacy endpoints, biomarker-driven patient selection, and sponsor therapeutic area experience constituted the three most influential predictive features. A comparison of five regression techniques demonstrated that gradient boosted regression trees achieved the highest discriminative performance (AUC = 0.823, 95% CI: 0.791--0.855) on the hold-out validation cohort. Cross-indication transferability analysis across six major cancer types showed moderate to strong covariate consistency for trial design features (Spearman ρ = 0.71--0.89) and lower consistency for molecular target features (ρ = 0.43--0.62). These findings provide quantitative evidence supporting the development of more robust technical success probability (PTS) calculation methods.
subject: Articles
  topic: section
subject: clinical trial outcome prediction
  topic: keywords
subject: covariate feature importance
  topic: keywords
subject: cross-indication transferability
  topic: keywords
subject: technical success probability
  topic: keywords
creator: Xizhu Liu
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 117
ending page: 130

Benchmarking Robustness-Efficiency Trade-offs of Camera-LiDAR Fusion Strategies for 3D Object Detection Under Environmental Corruptions
type: item
description: Multi-modal sensor fusion combining camera and LiDAR data has become the dominant paradigm for 3D object detection in autonomous driving. The selection among early fusion, late fusion, and deep fusion strategies involves complex trade-offs between detection accuracy, computational efficiency, and robustness under adverse conditions. This paper presents a systematic benchmarking study that quantitatively evaluates these trade-offs on the nuScenes dataset under diverse environmental corruptions and calibration perturbations. Six representative fusion algorithms spanning three fusion categories are evaluated across 10 corruption types at three severity levels, with simultaneous measurement of detection accuracy (mAP, NDS), computational resource consumption (latency, GPU memory), and degradation patterns under spatial misalignment and temporal desynchronization. The results reveal that deep fusion approaches achieve 2.8%--5.1% higher NDS than early fusion under clean conditions, while late fusion strategies demonstrate 12.3%--18.7% lower mean Corruption Error under LiDAR-degraded scenarios. Calibration perturbation analysis shows that soft-association mechanisms reduce mAP degradation by 41.2% compared to hard-association approaches at 0.5-meter spatial misalignment. These findings provide evidence-based guidance for engineering teams selecting sensor fusion configurations under real-world deployment constraints.
subject: Articles
  topic: section
subject: multi-modal sensor fusion
  topic: keywords
subject: robustness benchmarking
  topic: keywords
subject: 3D object detection
  topic: keywords
subject: autonomous driving perception
  topic: keywords
creator: Yuhan Li
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 104
ending page: 116

Performance Evaluation and Optimization Strategies for Privacy-Preserving Document Classification in Distributed Learning Environments
type: item
description: The proliferation of sensitive documents across healthcare, financial, and governmental sectors necessitates robust privacy-preserving classification mechanisms. This study presents a comprehensive performance evaluation of privacy-preserving document classification within distributed learning frameworks, examining federated learning and differential privacy implementations. Through systematic experimentation on benchmark datasets, we quantify accuracy-privacy trade-offs, communication overhead, and computational costs across various privacy budget configurations. Results demonstrate that adaptive privacy allocation reduces accuracy degradation by 12-18% compared to uniform distribution while maintaining equivalent privacy guarantees. Gradient compression techniques achieve 67% communication reduction with minimal convergence impact. These findings provide actionable deployment guidelines for organizations implementing privacy-preserving document processing systems.
subject: Articles
  topic: section
subject: privacy-preserving machine learning
  topic: keywords
subject: federated learning
  topic: keywords
subject: differential privacy
  topic: keywords
subject: document classification
  topic: keywords
creator: Qiaomu Zhang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 94
ending page: 103

A Multi-Dimensional Coverage Metric with Evolutionary Search for Safety-Critical Scenario Generation in Autonomous Driving Testing
type: item
description: Ensuring autonomous driving safety requires rigorous testing across diverse and safety-critical scenarios. Manual scenario design is labor-intensive and insufficient in capturing edge cases, while random generation produces redundant test cases. This paper proposes a coverage-guided evolutionary search algorithm (CGES) for automated generation of safety-critical test scenarios with quantitative coverage assessment. A parameterized scenario representation is established based on six functional dimensions, and three complementary coverage metrics---scenario parameter space coverage (SPSC), behavioral diversity coverage (BDC), and risk-weighted fault mode coverage (RFMC)---are defined to quantify test adequacy. An adaptive evolutionary search strategy that incorporates risk-prioritized fitness evaluation and diversity-aware selection is designed to efficiently explore high-risk regions of the scenario space. Experiments on CARLA using five operational design domains demonstrate that CGES achieves 17.3% higher composite coverage and discovers 28.6% more unique safety violations than the baselines, while reducing redundant test cases by 41.2%. The proposed metrics provide a quantitative foundation for evaluating the completeness of autonomous driving test suites, contributing to standardized safety validation aligned with NHTSA regulatory requirements.
subject: Articles
  topic: section
subject: autonomous driving testing
  topic: keywords
subject: scenario generation
  topic: keywords
subject: coverage metric
  topic: keywords
subject: evolutionary search
  topic: keywords
creator: Yi Guo
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 82
ending page: 93

Behavioral Association Analysis for Organized Fraud Ring Detection in Large-Scale User Interaction Data
type: item
description: The proliferation of organized fraud rings poses significant threats to online platforms and financial systems worldwide. This paper presents a comprehensive behavioral association analysis framework for detecting coordinated fraudulent activities in large-scale user interaction data. We construct heterogeneous user interaction graphs from multi-source behavioral data and extract coordinated behavioral fingerprints across accounts through multi-dimensional association signals including fund flows, device sharing patterns, and temporal synchronization features. Community detection algorithms are employed to identify tightly-connected fraud groups within massive interaction networks. Experimental evaluation on real-world datasets demonstrates that our association-based approach achieves superior detection performance compared to traditional methods, with precision rates reaching 94.7% and recall rates of 89.3% on organized fraud ring identification tasks.
subject: Articles
  topic: section
subject: fraud detection
  topic: keywords
subject: behavioral association
  topic: keywords
subject: graph analysis
  topic: keywords
subject: community detection
  topic: keywords
creator: Minghua Deng
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 67
ending page: 81

Language-Driven Interactive Annotation for Pulmonary Nodules in Chest CT: An LLM Prompt-Translation and Multi-Round Refinement Approach
type: item
description: High-quality pixel-level annotation remains a principal bottleneck for medical artificial intelligence, particularly for pulmonary nodule analysis on chest computed tomography, where expert labeling is costly and heterogeneous across institutions. This paper investigates a narrow but practical question: how short free-text descriptions produced by clinicians can be mediated into the spatial prompts expected by foundation segmentation models such as the Segment Anything Model via structured slot extraction, and how a lightweight multi-round refinement loop can stabilize the resulting masks under realistic annotation budgets. We emphasize that the role of the large language model in this study is restricted to structured slot extraction from short English phrases and to classifying each correction utterance into one of four canonical categories; the language model does not predict pixel coordinates, and the spatial initialization itself is driven by a coarse lobe-level anatomical prior, a size heuristic, and a vessel-suppressed point-sampling rule, rather than by free-form visual reasoning. This study is therefore best described as language-mediated structured prompting rather than free-form reasoning segmentation. We do not propose a new backbone or a full clinical system; rather, we study a prompt-translation strategy coupled with bounded interactive correction, evaluated on three public datasets: LIDC-IDRI, LUNA16, and Medical Segmentation Decathlon Task06 Lung. We report Dice, intersection over union, ninety-fifth percentile Hausdorff distance, and per-case annotation time, together with paired Wilcoxon signed-rank tests and bootstrap confidence intervals, so that the magnitude and reliability of any improvement can be evaluated directly. Results suggest a modest improvement in Dice and a reduction in measured per-case annotation time compared with purely geometric prompting; the annotation-time comparison should be read as an engineering-level approximation rathe…
subject: Articles
  topic: section
subject: pulmonary nodule annotation
  topic: keywords
subject: language-mediated segmentation
  topic: keywords
subject: prompt translation
  topic: keywords
subject: interactive refinement
  topic: keywords
creator: Yali Zhang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 55
ending page: 66

Privacy-Preserving Techniques in Credit Risk Assessment: A Comparative Analysis of Differential Privacy, Federated Learning, and Homomorphic Encryption
type: item
description: The proliferation of machine learning in financial services has intensified concerns regarding consumer data privacy during credit risk assessment. This paper presents a comparative study of three privacy-preserving paradigms relevant to credit risk assessment: differential privacy, federated learning, and homomorphic encryption. Through empirical evaluation on a large-scale, representative credit dataset across multiple privacy-preserving configurations, we examine the privacy-utility trade-offs inherent in each approach. Our experimental framework assesses prediction accuracy, computational overhead, and privacy guarantees across multiple configurations. Results demonstrate that differential privacy with epsilon=8.65 achieves a balanced privacy-utility tradeoff with competitive discriminative performance, while homomorphic encryption preserves near-baseline predictive performance during encrypted inference. Federated learning enables collaborative model training across institutions without sharing raw data. The analysis reveals that privacy parameter selection critically impacts model utility, with differential privacy offering quantifiable privacy budgets and homomorphic encryption providing cryptographic security guarantees. These findings provide actionable guidance for financial institutions navigating regulatory compliance requirements while maintaining effective risk management capabilities.
subject: Articles
  topic: section
subject: Privacy-preserving computation
  topic: keywords
subject: Credit risk assessment
  topic: keywords
subject: Differential privacy
  topic: keywords
subject: Federated learning
  topic: keywords
subject: Homomorphic encryption
  topic: keywords
creator: Zhi Luo
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-05-06
publication date: 2026-05-06
starting page: 37
ending page: 54

Performance Evaluation of Anomaly-Based Detection Approaches for Zero-Day Attack Early Warning in Cloud Infrastructure
type: item
description: The escalating sophistication of zero-day attacks poses unprecedented challenges to cloud infrastructure security, necessitating advanced detection mechanisms beyond traditional signature-based approaches. This paper presents a comprehensive performance evaluation of anomaly-based detection approaches specifically designed for early warning of zero-day attacks in cloud environments. We systematically analyze multiple detection strategies leveraging multi-source telemetry data, including network traffic patterns, system call sequences, and resource usage metrics. Through extensive experimentation on a realistic cloud infrastructure testbed using synthesized attack scenarios, we compare statistical-, machine learning-, and ensemble-based detection approaches across critical performance dimensions, including detection accuracy, false positive rates, and detection timeliness. Our evaluation reveals significant trade-offs among approaches, with ensemble methods achieving a recall (TPR) of 94.7% while maintaining a false positive rate of 0.20%. The findings provide actionable insights for cloud service providers seeking to optimize their zero-day threat detection capabilities.
subject: Articles
  topic: section
subject: zero-day attacks
  topic: keywords
subject: Cloud infrastructure security
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: Performance evaluation
  topic: keywords
creator: Xiaoyi Long
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-31
publication date: 2026-03-31
starting page: 352
ending page: 363

Performance Benchmarking and Optimization of Deep Learning-Based Point Cloud Processing Algorithms for Industrial Quality Inspection
type: item
description: The deployment of deep learning-based point cloud processing algorithms in industrial quality inspection systems requires comprehensive performance evaluation frameworks to guide algorithm selection and optimization. This study establishes a systematic benchmarking methodology that evaluates state-of-the-art point cloud neural networks across multiple dimensions, including recognition accuracy, computational efficiency, and robustness under industrial conditions. Through controlled experiments on synthetic and real-world manufacturing datasets, we quantify the performance tradeoffs among competing approaches and identify optimal configuration strategies for different deployment scenarios. Our benchmark framework incorporates standardized metrics for accuracy assessment, efficiency profiling, and robustness evaluation. The empirical findings provide data-driven guidelines for manufacturing enterprises to select appropriate point cloud processing solutions.
subject: Articles
  topic: section
subject: point cloud processing
  topic: keywords
subject: deep learning
  topic: keywords
subject: performance benchmarking
  topic: keywords
subject: Industrial quality inspection
  topic: keywords
creator: Yuhan Li
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-31
publication date: 2026-03-31
starting page: 339
ending page: 351

A Comparative Study of Machine Learning Methods for Automated Customer Service Dialogue Quality Assessment
type: item
description: The rapid expansion of digital customer service channels has created an urgent need for automated quality assessment methods capable of evaluating dialogue interactions at scale. This paper presents a comprehensive comparative study of machine learning approaches for automated assessment of customer service dialogue quality, examining traditional machine learning algorithms, deep learning architectures, and transformer-based models. A multi-dimensional quality assessment framework is proposed, incorporating three primary evaluation categories: information accuracy, communication appropriateness, and process compliance. An experimental evaluation on a customer service dialogue dataset demonstrates that BERT-based models achieve superior overall classification accuracy (94.2%), while traditional methods offer advantages in computational efficiency and interpretability. The analysis reveals significant performance differences across service defect categories, with transformer models excelling at detecting subtle compliance violations and attitude-related issues. These findings provide practical guidance for enterprises seeking to implement standardized quality-monitoring systems that align with consumer protection regulations.
subject: Articles
  topic: section
subject: Customer Service Quality Assessment
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Text Classification
  topic: keywords
subject: Machine Learning Comparison
  topic: keywords
creator: Yajing Zhang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-31
publication date: 2026-03-31
starting page: 328
ending page: 338

Accuracy Evaluation of Machine Learning-Based Hospital Resource Demand Forecasting During Infectious Disease Surges: A Comparative Analysis
type: item
description: The COVID-19 pandemic exposed critical vulnerabilities in hospital resource allocation during infectious disease surges, necessitating accurate demand forecasting capabilities. This study evaluates machine learning-based prediction algorithms for hospital resource demand (e.g., ICU occupancy) through comparative analysis. We assess time series methods, ensemble learning techniques, and deep learning architectures using historical utilization data from multiple healthcare facilities. Performance metrics, including MAE, RMSE, and MAPE, were computed for short-term and medium-term prediction horizons. Results demonstrate that ensemble approaches achieve higher accuracy than traditional methods. Across 7-21-day horizons, the ensemble model (XGBoost + Random Forest + LSTM) achieved the lowest prediction errors, with a 7-day MAPE of 7.64% and sustained advantages over ARIMA/SARIMA baselines. These findings provide evidence-based guidelines for healthcare coordinators aligned with AHRQ emergency preparedness priorities.
subject: Articles
  topic: section
subject: hospital resource forecasting
  topic: keywords
subject: machine learning accuracy evaluation
  topic: keywords
subject: infectious disease surge capacity
  topic: keywords
subject: epidemic prediction algorithms
  topic: keywords
creator: Yijie Wang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-31
publication date: 2026-03-31
starting page: 314
ending page: 327

Enhancing Financial Compliance Transparency through Automated Data Governance and Intelligent Risk Reporting
type: item
description: Financial institutions face mounting pressure to maintain regulatory compliance while managing escalating costs and data complexity. This paper presents a comprehensive framework integrating automated data governance mechanisms with intelligent risk reporting capabilities to enhance compliance transparency. The proposed approach addresses three critical dimensions: real-time data quality monitoring through contract-based validation, anomaly detection using machine learning techniques, and automated audit trail generation for regulatory oversight. Experimental validation demonstrates a 43.2% reduction in compliance processing time and 38.7% improvement in data quality metrics compared to traditional manual approaches. The framework provides particular value for small and medium-sized financial institutions by reducing human resource requirements while maintaining rigorous regulatory standards. Implementation results confirm the framework's effectiveness in detecting compliance violations with 94.3% precision and generating comprehensive audit documentation satisfying regulatory transparency requirements.
subject: Articles
  topic: section
subject: financial compliance
  topic: keywords
subject: data governance
  topic: keywords
subject: automated risk reporting
  topic: keywords
subject: regulatory transparency
  topic: keywords
creator: Yifei Li
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-31
publication date: 2026-03-31
starting page: 299
ending page: 313

Identifying Undisclosed Related Party Relationships and Revenue Recognition Irregularities: A Rule-Based Analytical Approach for Audit Planning
type: item
description: Related party transactions and revenue recognition manipulations remain persistent sources of financial statement fraud, posing significant challenges to audit procedures and investor protection. This research develops a rule-based analytical framework designed to identify potential irregularities in corporate financial disclosures during the audit planning phase. The proposed approach integrates network analysis techniques for detecting undisclosed related-party relationships by cross-referencing entity information and time-series pattern-detection methods for identifying suspicious revenue recognition behaviors, including period-end concentration and cash flow divergence. A composite risk scoring mechanism combines multiple indicators to prioritize audit attention. Empirical analysis using SEC EDGAR filings from 847 publicly traded companies demonstrates the framework's effectiveness, achieving a precision rate of 78.3% in flagging high-risk company filings
subject: Articles
  topic: section
subject: related party transaction
  topic: keywords
subject: revenue recognition anomaly
  topic: keywords
subject: audit planning
  topic: keywords
subject: risk scoring framework
  topic: keywords
creator: Dun Liang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-04-01
publication date: 2026-04-01
starting page: 26
ending page: 36

Comparative Evaluation of Supervised Learning Algorithms for Cancer Treatment Response Prediction Using Clinical and Biomarker Features
type: item
description: Accurate prediction of cancer treatment response remains essential for personalized oncology. This study conducts comprehensive evaluation of six supervised learning algorithms---Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, XGBoost, and LightGBM---for predicting chemotherapy and targeted therapy responses. We developed a feature engineering pipeline integrating neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and treatment history from 847 cancer patients across five tumor types. XGBoost achieved superior performance with AUC-ROC of 0.873, accuracy of 81.4%, and F1-score of 0.796, significantly outperforming baseline Logistic Regression (AUC-ROC 0.742, p < 0.001). SHAP analysis identified NLR (mean |SHAP| = 0.187), tumor stage, and prior treatment lines as the most predictive features. Kaplan-Meier survival analysis stratified by predicted risk demonstrated strong clinical validity, with high-risk patients exhibiting median progression-free survival of 4.7 months versus 14.3 months for low-risk patients (log-rank p < 0.001, hazard ratio 3.24). Gradient boosting algorithms provide optimal balance between predictive accuracy, computational efficiency, and clinical interpretability for treatment response prediction.
subject: Articles
  topic: section
subject: Cancer treatment response
  topic: keywords
subject: supervised learning
  topic: keywords
subject: biomarker features
  topic: keywords
subject: algorithm comparison
  topic: keywords
subject: survival analysis
  topic: keywords
creator: Chuhan Zhang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-04-01
publication date: 2026-04-01
starting page: 14
ending page: 25

Multi-Dimensional Feature Analysis and Evaluation Methods for Anomalous Fund Flow Identification in Cross-Border Financial Transactions
type: item
description: Cross-border financial transactions are inherently complicated by the multi-currency nature of the transaction, the different regulatory systems, and the various methods used to launder dirty money; anomaly detection is not easy. The paper presents a comprehensive, multi-sided, and characteristic-based analysis framework for anomaly detection in cross-border fund transactions. Using this framework by taking characteristics of transactions, network topology features, and temporal behavior patterns into account, in order to boost detection. A systematic evaluation was conducted on 2.8M transactional data; a combination of graph-structure-based features and time-series behavioral indicators outperformed a single-dimensional approach. After experimentation, this strategy increased the baseline's accuracy by 18.7 percent and recall by 23.4 percent.
subject: Articles
  topic: section
subject: Cross-border transactions
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: Feature engineering
  topic: keywords
subject: Anti-money laundering
  topic: keywords
creator: Minju Zhong
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 13

Service Architecture and Optimization Strategies in Cloud-Based Big Data Platforms
type: item
description: Cloud-based big data platforms offer unprecedented opportunities for data-driven insights and services. However, the inherent complexities of distributed systems and the diverse needs of applications present significant challenges in service architecture design and optimization. This research investigates service architecture paradigms and optimization strategies for cloud-based big data platforms, focusing on enhancing performance, scalability, reliability, and cost-efficiency. We analyze existing service architectures, identify key performance bottlenecks, and propose novel optimization techniques encompassing resource allocation, service placement, request routing, and data management. The proposed strategies leverage machine learning and adaptive control mechanisms to dynamically adjust system parameters in response to workload variations and resource availability. We evaluate the effectiveness of the proposed techniques through extensive simulations and real-world experiments on a production-scale cloud platform. Our results demonstrate significant improvements in key performance indicators, including response time, throughput, resource utilization, and energy consumption. Furthermore, we provide practical guidelines for designing and deploying optimized service architectures in cloud-based big data environments, enabling organizations to harness the full potential of their data assets. This research contributes to the advancement of efficient and scalable big data services in the cloud computing era.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Big Data
  topic: keywords
subject: Service Architecture
  topic: keywords
subject: Optimization
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Performance Evaluation
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Peiyilin Shen
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-26
publication date: 2026-03-26
starting page: 288
ending page: 298

Multi-Objective Optimization of Process Parameters for Dental Resin 3D Printing Using Improved NSGA-II Algorithm
type: item
description: This study presents an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing process parameters in dental resin 3D printing. The optimization framework addresses three conflicting objectives: dimensional accuracy, surface roughness, and printing time. Five critical process parameters, including layer thickness, exposure duration, print angle, infill density, and lift speed, are investigated using a design of experiments approach based on Response Surface Methodology. The improved NSGA-II incorporates an adaptive mutation operator and an elite-preservation strategy to enhance convergence and solution diversity. Experimental validation using nine dental model resin types demonstrates that the proposed algorithm achieves a 25.3% improvement in dimensional accuracy compared to default parameter settings. The Pareto-optimal solutions provide dental laboratories with flexible parameter configurations that balance quality requirements and production efficiency. Statistical analysis confirms that layer thickness and exposure duration are the most influential parameters affecting print quality.
subject: Articles
  topic: section
subject: multi-objective optimization
  topic: keywords
subject: NSGA-II
  topic: keywords
subject: dental resin
  topic: keywords
subject: 3D printing parameters
  topic: keywords
creator: Pei-Ting Chung
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-18
publication date: 2026-03-18
starting page: 276
ending page: 287

A Comparative Study of Reinforcement Learning and Metaheuristic Algorithms for Real-Time Last-Mile Delivery Scheduling
type: item
description: The exponential growth of e-commerce has intensified the need for efficient real-time delivery-scheduling algorithms that can handle dynamic urban logistics environments. This study presents a comprehensive comparative analysis of reinforcement learning and metaheuristic algorithms for last-mile delivery optimization under time-sensitive constraints. We implement and evaluate Adaptive Large Neighborhood Search (ALNS) and Deep Q-Network (DQN) approaches on benchmark instances representing realistic urban delivery scenarios with 50-200 customer nodes. The experimental results demonstrate that ALNS achieves superior solution quality with an average optimality gap of 3.2% under an approximately 5-second (≤6 s) operational time budget, while DQN exhibits better runtime scalability for networks exceeding 150 customers (in the stress-test setting). The sensitivity analysis reveals critical trade-offs between computational efficiency and solution robustness under varying traffic conditions. This research provides empirical guidelines for logistics practitioners to select appropriate algorithms based on operational constraints, thereby contributing to sustainable urban transportation systems.
subject: Articles
  topic: section
subject: last-mile delivery scheduling
  topic: keywords
subject: Reinforcement learning
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subject: Metaheuristic algorithms
  topic: keywords
subject: Dynamic vehicle routing
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subject: Real-time optimization
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creator: Jialu Wang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-18
publication date: 2026-03-18
starting page: 264
ending page: 275

FraudGuardian: Self-Supervised and Adversarial Learning for Robust Financial Fraud Detection
type: item
description: Financial fraud detection is challenged by severe class imbalance, evolving adversarial tactics, and the demand for explainable decisions. To address these issues, we propose FraudGuardian, a novel deep learning framework for robust and interpretable fraud detection. FraudGuardian integrates two synergistic learning mechanisms: Self-supervised Consistency Learning (SCL) captures intrinsic normal patterns at the local event level to improve sensitivity to subtle anomalies, while Adversarial Feature Mining (AFM) actively synthesizes challenging samples to learn a more generalized decision boundary. These components are dynamically balanced through an adaptive multi-task optimization scheme, effectively mitigating data imbalance. Extensive experiments on real-world financial transaction datasets show that FraudGuardian significantly outperforms state-of-the-art methods, achieving 97.9% AUC, 90.6% PR-AUC, and 86.2% F1-Score on a challenging credit card fraud dataset, representing a 3.1% PR-AUC improvement over the best baseline. Ablation studies validate the contribution of each component. Moreover, FraudGuardian demonstrates strong generalization in cross-dataset and cross-attack-type evaluations, with a 9.1% F1Score improvement over baselines when detecting novel fraud strategies. The framework also provides interpretability by highlighting suspicious local patterns, offering a powerful and generalizable solution for enhancing secure transaction systems.
subject: Articles
  topic: section
subject: financial fraud detection
  topic: keywords
subject: self-supervised consistency learning
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subject: class imbalance
  topic: keywords
subject: model generalization
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creator: Yijing Wei
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-03-18
publication date: 2026-03-18
starting page: 244
ending page: 263

Context-Aware Classification of Verbal Operants in Children with ASD Using Deep Learning
type: item
description: Verbal operant assessment plays a critical role in autism spectrum disorder intervention planning, yet current manual evaluation methods suffer from subjectivity and time constraints. This study presents a context-aware deep learning framework for automatic classification of verbal operants (mand, tact, echoic, and intraverbal) in therapeutic speech recordings of children with ASD. The proposed multi-task learning architecture integrates contextual features including antecedent stimuli, functional consequences, and prosodic patterns through attention mechanisms. Experiments on 1,847 annotated speech samples from 52 children demonstrate classification accuracy of 83.7% for operant type identification and 89.2% for spontaneous versus prompted language discrimination. The framework successfully identifies atypical language patterns including delayed echolalia and scripted language with 81.4% precision. Results indicate that contextual feature integration improves classification performance by 12.3% compared to text-only baselines, providing objective support for language assessment and intervention planning in clinical practice.
subject: Articles
  topic: section
subject: verbal operants
  topic: keywords
subject: autism spectrum disorder
  topic: keywords
subject: natural language processing
  topic: keywords
subject: context-aware classification
  topic: keywords
creator: Yaqing Bai
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 232
ending page: 243

Fairness-Aware Multimodal Fusion for Early Chronic Disease Risk Prediction: A Temporal Deep Learning Approach
type: item
description: Chronic diseases constitute a significant public health challenge, with early detection enabling effective preventive interventions. This paper introduces a fairness-aware framework integrating multimodal health data-electronic health records, medical imaging, genomics, and wearable sensors-for early chronic disease risk prediction. The approach addresses three critical challenges: cross-modal feature harmonization across heterogeneous data types, algorithmic bias mitigation through fairness-constrained learning, and temporal pattern extraction for disease progression modeling. Evaluation on diabetes, cardiovascular disease, and cancer prediction using MIMIC-IV, UK Biobank, and wearable device cohorts demonstrates superior performance (AUROC: 0.892-0.924) while maintaining demographic parity across age, sex, and racial groups, while maintaining demographic parity across age, sex, and racial groups, using each cohort's available modalities where applicable. Fairness metrics improve by 76.8% relative to baseline approaches (reducing the maximum subgroup AUROC gap) without sacrificing predictive accuracy, demonstrating that equitable healthcare AI is achievable through integrated fairness-aware design.
subject: Articles
  topic: section
subject: multimodal health data fusion
  topic: keywords
subject: algorithmic fairness
  topic: keywords
subject: temporal deep learning
  topic: keywords
subject: chronic disease prediction
  topic: keywords
creator: Xiaotong Shi
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 217
ending page: 231

StatFuse: Bridging Statistical Inference and Neural Prediction for Interpretable Forecasting
type: item
description: The integration of traditional statistical methods with modern deep learning architectures offers opportunities to develop prediction frameworks that balance accuracy and interpretability. This paper introduces StatFuse, a hybrid approach synthesizing statistical decomposition with neural prediction while maintaining rigorous uncertainty quantification. By combining time-series analysis principles with neural architectures, the framework achieves strong and competitive performance across benchmark datasets. The methodology incorporates conformal prediction intervals for distribution-free coverage guarantees and employs statistical diagnostics and perturbation-based attribution for feature importance. Experimental validation on economic forecasting and public health monitoring demonstrates that StatFuse improves performance on two of four benchmarks and remains close to strong baselines on the others, while offering enhanced interpretability.
subject: Articles
  topic: section
subject: statistical-neural fusion
  topic: keywords
subject: interpretable forecasting
  topic: keywords
subject: uncertainty quantification
  topic: keywords
subject: conformal prediction
  topic: keywords
creator: Ye Lei
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 205
ending page: 216

Anomaly Detection and Cross-Center Consistency Assessment for Multi-Site Clinical Trial Quality Control
type: item
description: Multi-site clinical trials generate heterogeneous data requiring robust quality control mechanisms to ensure data integrity and regulatory compliance. This paper presents a comprehensive framework for automated anomaly detection and cross-center consistency assessment in distributed clinical trials. We propose a multi-layered detection approach combining rule-based thresholds, quantile drift analysis, and graph-structured consistency verification to identify protocol violations and data irregularities across trial sites. The methodology integrates dynamic threshold calibration with historical distributions, hierarchical relationship mapping between centers, investigators, and subjects, and ensemble aggregation techniques to construct audit-friendly evidence matrices. Experimental validation on multi-center trial datasets demonstrates superior detection accuracy, with sensitivity exceeding 87.3%, and early-warning capabilities that identify anomalies after analyzing only 23.5% of accumulated data, compared to 61.2% required by conventional approaches. The framework achieves 42.6% higher sensitivity compared to traditional monitoring approaches while maintaining computational efficiency suitable for real-time deployment. Implementation considerations address regulatory alignment with FDA and EMA guidelines, supporting repeatability, traceability, and auditability principles essential for clinical trial quality assurance.
subject: Articles
  topic: section
subject: clinical trial quality control
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: cross-center consistency
  topic: keywords
subject: evidence matrix
  topic: keywords
creator: Yisi Liu
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 190
ending page: 204

Study on Uncertainty Data Analysis for Common Natural Disaster Prediction in the U.S. Using Cloud Computing and Machine Learning
type: item
description: Accurate natural disaster prediction requires the integration of heterogeneous environmental datasets and the ability to model uncertainty arising from sensor noise, incomplete observations, and inconsistent spatiotemporal coverage. This study proposes a cloud-based, uncertainty-aware machine learning framework designed to support large-scale prediction of hurricanes, floods, and wildfires in the United States. The framework incorporates distributed data ingestion, cloud-native preprocessing, multi-source feature engineering, and probabilistic learning to address the complexity and variability inherent in environmental data. Through a unified cloud workflow, heterogeneous datasets from NOAA, USGS, and NASA FIRMS are harmonized using temporal alignment, spatial normalization, and uncertainty-reduction strategies such as imputation, smoothing filters, and cross-source calibration. Model evaluation focuses on performance trends rather than fixed numerical benchmarks, examining how different learning algorithms respond to the characteristics of each disaster type. Results indicate that deep models and uncertainty-aware approaches are particularly effective for highly dynamic hazards such as hurricanes and wildfires, while tree-based models perform well for structured hydrological predictions. A real-world wildfire event is used to illustrate the practical applicability of the framework. Overall, the proposed approach enhances robustness, interpretability, and operational relevance, demonstrating the value of combining cloud computing with uncertainty-aware machine learning for multi-hazard disaster prediction.
subject: Articles
  topic: section
subject: cloud computing
  topic: keywords
subject: multi-source environmental data
  topic: keywords
subject: natural disaster prediction
  topic: keywords
subject: data integration
  topic: keywords
subject: probabilistic modeling
  topic: keywords
subject: feature engineering
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creator: Guoli Ying
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-27
publication date: 2026-02-27
starting page: 178
ending page: 189

A Comparative Evaluation of Deep Learning and Ensemble Algorithms for Online Payment Fraud Detection
type: item
description: The exponential growth of digital payment platforms has introduced unprecedented security challenges in detecting fraudulent transactions. This study presents a comprehensive comparative evaluation of deep learning architectures and ensemble learning algorithms for online payment fraud detection. We systematically assess Long Short-Term Memory networks, Recurrent Neural Networks, logistic regression, and gradient boosting methods across detection accuracy, precision-recall trade-offs, and computational efficiency. Through rigorous experimentation on real-world transaction datasets, we evaluate two feature engineering strategies: user behavior-based features from RFM analysis and transaction amount patterns. Our analysis reveals that ensemble methods achieve superior F1-scores of 0.876, while LSTM architectures demonstrate enhanced capability in capturing temporal dependencies. The study establishes quantitative guidelines for algorithm selection based on dataset characteristics and operational constraints.
subject: Articles
  topic: section
subject: fraud detection
  topic: keywords
subject: deep learning
  topic: keywords
subject: ensemble learning
  topic: keywords
subject: feature engineering
  topic: keywords
creator: Jin Zhang
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-26
publication date: 2026-02-26
starting page: 164
ending page: 177

Theoretical Evolution and Frontier Trends of Corporate Strategic Management in the AI Era: A Literature Review
type: item
description: This literature review explores the theoretical evolution and emerging trends in corporate strategic management within the context of the artificial intelligence (AI) era. The rapid advancements in AI technologies are fundamentally reshaping the business landscape, necessitating a re-evaluation of traditional strategic management frameworks. This review traces the historical development of strategic management theories, highlighting key milestones and paradigm shifts. It then delves into two core themes: the integration of AI into strategic decision-making processes and the impact of AI on competitive advantage and dynamic capabilities. Furthermore, the review compares and contrasts existing strategic management approaches in light of AI’s transformative potential, identifying key challenges and limitations. Finally, it proposes future research directions, focusing on the development of new theoretical models and empirical investigations that can better capture the complexities of corporate strategic management in the AI era. This review contributes to a deeper understanding of how organizations can effectively leverage AI to achieve sustainable competitive advantage and navigate the challenges of a rapidly evolving technological environment. It synthesizes existing knowledge, identifies research gaps, and offers insights for both academics and practitioners seeking to understand the strategic implications of AI.
subject: Articles
  topic: section
subject: Strategic Management
  topic: keywords
subject: Artificial Intelligence
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subject: Literature Review
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subject: Competitive Advantage
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subject: Dynamic Capabilities
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subject: Strategic Decision-Making
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subject: AI Era
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creator: Fiona C. McGregor
creator: Alistair J. Vance
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-25
publication date: 2026-02-25
starting page: 155
ending page: 163

Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach
type: item
description: This review paper explores the intersection of valuation logic and venture capital (VC) investment strategies within the domain of Artificial Intelligence (AI) startups, employing a knowledge graph-based approach. Traditional valuation techniques often fall short in capturing the unique characteristics and inherent uncertainties associated with AI ventures, including rapidly evolving technologies, data dependencies, and talent scarcity. A knowledge graph provides a structured framework for integrating diverse data sources, representing complex relationships between entities (e.g., technology, teams, market trends), and facilitating nuanced valuation assessments. The paper synthesizes existing literature on AI startup valuation, VC decision-making, and knowledge graph applications, identifying key valuation drivers and investment criteria. We examine how knowledge graphs can enhance due diligence processes, support dynamic risk assessment, and improve the accuracy of financial projections. Specifically, we investigate applications to forecasting AI’s impact on different sectors. We explore the strategic implications for VC firms seeking to capitalize on the growth potential of AI, discussing how they can leverage knowledge graphs to identify promising startups, optimize investment portfolios, and generate superior returns. The review addresses the challenges and limitations of adopting a knowledge graph-based approach, including data quality issues, computational complexity, and the need for specialized expertise. Finally, it highlights future research directions, such as the development of automated valuation models and the integration of explainable AI (XAI) techniques to enhance transparency and trust in VC investment decisions. Ultimately, this review argues that a knowledge graph-based approach offers a powerful tool for navigating the complexities of AI startup valuation and informing more effective VC investment strategies, creating win-win outcomes for invest…
subject: Articles
  topic: section
subject: AI Startups
  topic: keywords
subject: Venture Capital
  topic: keywords
subject: Valuation
  topic: keywords
subject: Knowledge Graph
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subject: Investment Strategy
  topic: keywords
subject: Due Diligence
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subject: Artificial Intelligence
  topic: keywords
creator: Julian S. Thorne
creator: Hao-Ran Chen
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-22
publication date: 2026-02-22
starting page: 146
ending page: 154

Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation
type: item
description: This study proposes a dynamic stress-testing framework for consumer-finance portfolios by generating forward-looking macroeconomic scenarios using a vector autoregression model combined with Monte Carlo simulation. The scenarios are fed into a panel logistic-regression engine trained on 4.2 million loan accounts from 2015-2024. Using 1,000 simulated macro paths, the model quantifies stressed default probabilities up to 12 months ahead. Results show that under the 5% most adverse scenarios, 90-day delinquency rates rise by 28.9-46.2%, depending on product type. The model achieves an ROC-AUC of 0.82 in out-of-sample stress periods and captures 72.5% of actual default surges during the 2020 downturn. This approach offers a systematic tool for scenario-driven risk assessment in consumer lending.
subject: Articles
  topic: section
subject: Stress testing
  topic: keywords
subject: consumer credit
  topic: keywords
subject: Macroeconomic simulation
  topic: keywords
subject: scenario analysis
  topic: keywords
subject: default forecasting
  topic: keywords
creator: Louis Bernard
creator: Manon Giroux
creator: Nicolas Faure
creator: Sophie Lambert
rights: Copyright (c) 2026 Journal of Science, Innovation & Social Impact
date: 2026-02-14
publication date: 2026-02-14
starting page: 139
ending page: 145