[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2051":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":24,"paper":25,"created_at":45},2051,"A Decision Support System (DSS) for Fraud Detection Using Genetic Support Vector Machine (GSVM)","https:\u002F\u002Fdoi.org\u002F10.35912\u002Fijfam.v8.n2.p289-302.2026","Purpose: This study developed a Decision Support System (DSS) for fraud prediction using a Genetic Support Vector Machine (GSVM), a hybrid model that employs a Genetic Algorithm (GA) to optimize Support Vector Machine (SVM) hyperparameters (C and γ) on financial ratios extracted from MachameRatios®. Research Methodology: A quantitative ex post facto design using data from 75 purposively sampled quoted manufacturing firms across six sectors over an 11-year panel (2011–2021) was assessed. Class imbalances were handled via Synthetic Minority Oversampling Technique (SMOTE), data were labelled via a performance-based threshold, and the hybrid framework was benchmarked against Decision Tree, Bayesian Networks, and Naïve Bayes classifiers. Results: The GSVM with an Radial Basis Function (RBF) kernel achieved an optimal 10-fold cross-validation accuracy of 87.91%, 89.4% sensitivity, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.92, significantly outperforming the standard baseline models. Conclusions: The study concludes that the wrapper-based GSVM provides a highly accurate and parsimonious nonparametric pipeline for financial screening, establishing a mathematically robust foundation for automating corporate surveillance in regional markets. Limitations: This study relied solely on the financial ratio architectures of the sampled manufacturing firms. Contributions: The system addresses data imbalance via SMOTE and demonstrates that GA-optimized feature selection outperforms other methods.","目的：本研究开发了一套用于欺诈预测的决策支持系统（DSS），采用遗传支持向量机（GSVM）——一种混合模型，利用遗传算法（GA）对从MachameRatios®中提取的财务比率上的支持向量机（SVM）超参数（C和γ）进行优化。研究方法：采用定量事后回溯设计，评估了来自六个行业75家目的性抽样的上市制造企业在11年面板期（2011—2021年）内的数据。通过合成少数类过采样技术（SMOTE）处理类别不平衡，通过基于绩效的阈值对数据进行标注，并将该混合框架与决策树、贝叶斯网络和朴素贝叶斯分类器进行了基准比较。结果：采用径向基函数（RBF）核的GSVM实现了87.91%的最优10折交叉验证准确率、89.4%的灵敏度和0.92的受试者工作特征曲线下面积（AUC-ROC），显著优于标准基线模型。结论：研究认为，基于包装法的GSVM为财务筛查提供了一条高精度且简约的非参数流程，为区域市场中企业监控的自动化奠定了数学上稳健的基础。局限性：本研究仅依赖于所抽样制造企业的财务比率架构。贡献：该系统通过SMOTE解决了数据不平衡问题，并证明经GA优化的特征选择优于其他方法。",null,"International Journal of Financial Accounting and Management","2026-09-07T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦制造业财务舞弊检测的GSVM模型，与三农、农业信息化、数字乡村等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"智慧农业","农业人工智能","金融风控","10.35912\u002Fijfam.v8.n2.p289-302.2026",{"doi":24,"openalex_id":26,"authors":27,"venue":10,"cited_by_count":15,"oa_url":36,"card":37,"direction":43,"ingested_from":44},"W7211957388",[28,31,34],{"name":29,"orcid":30},"Chinedu Francis Egbunike","https:\u002F\u002Forcid.org\u002F0000-0002-4159-8630",{"name":32,"orcid":33},"Chidiebele Innocent Onyali","https:\u002F\u002Forcid.org\u002F0000-0001-7832-0619",{"name":35,"orcid":9},"Kenebechukwu Okafor","https:\u002F\u002Fgoodwoodpub.com\u002Findex.php\u002Fijfam\u002Farticle\u002Fdownload\u002F4050\u002F1979",{"tldr":38,"method":39,"finding":40,"direction":41,"opportunity":42},"用遗传算法优化SVM构建财务欺诈检测决策支持系统，在制造业面板数据上验证。","遗传算法优化SVM超参数，SMOTE处理不平衡，75家制造业11年财务比率数据。","GSVM的RBF核10折交叉验证准确率87.91%，AUC 0.92，显著优于基线模型。","农业人工智能与决策模型","可将GSVM框架迁移至农业企业信贷欺诈或农产品供应链金融风险识别，填补农业金融风控空白。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:17.798689Z"]