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English(EN) Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem

AI框架增强数字银行欺诈检测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

本研究介绍了一种检测数字银行欺诈的新型框架，特别解决了数据不平衡和操作限制等挑战。该框架结合了时间序列评估、防止数据泄露的特征工程、概率校准以及用于可解释性的SHAP。在PaySim基准数据集上的实验表明，在测试的模型中，XGBoost表现最佳，PR-AUC达到0.5658。研究还强调了在实施自适应策略时，降低误报率与维持召回率之间的权衡。 AI

影响 为改进金融服务中的欺诈检测提供了一个方法论框架，有望带来更安全的数字银行业务。

排序理由 学术论文，详细介绍了新的机器学习框架和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI框架增强数字银行欺诈检测能力

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学术论文，详细介绍了新的机器学习框架和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem

    The rapid expansion of digital banking and mobile financial services creates growing challenges for fraud detection, particularly under severe class imbalance and operational constraints. This study presents an explainable and adaptive machine-learning framework integrating chron…

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