金融取引の詐欺検知におけるデータ増強のための,境界意識の二重差別者生成的対抗ネットワーク
Honghao Zhu1, Zhanchao Wang2, Yu Xie2
1School of Computer and Information Engineering, Bengbu University, Bengbu, China.
PloS one
|February 20, 2026
まとめ
金融取引詐欺検知 (FTFD) は,不均衡なデータによる課題に直面しています. 新しい Boundary-Aware Dual-discriminator Generative Adversarial Network (BADGAN) は,意思決定の境界付近で現実的な合成詐欺データを生成することにより,詐欺の検出を改善します.
科学分野:
- コンピュータサイエンス コンピュータサイエンス
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
背景:
- デジタル決済の成長は,金融取引詐欺検出 (FTFD) の複雑性を高めています.
- 極端な階級の不均衡で,詐欺事件は少ないが,FTFDモデルの正確性を妨げている.
- 既存のデータ増強方法は,異常な詐欺パターンと隠蔽戦略と闘っています.
研究 の 目的:
- 金融取引詐欺検知 (FTFD) のクラス不均衡の問題に対処するために.
- FTFDモデルの不正パターンを正確に学習し,検出する能力を向上させる.
- よりよいモデルトレーニングのための合成詐欺データ生成の質を向上させる.
主な方法:
- 境界意識の二重差別性生成的敵対ネットワーク (BADGAN) を提案した.
- 遠隔対抗学習を使用して境界サンプル分類器と二重制約メカニズムを統合しました.
- ジェネレータは,実際の詐欺分布に準拠した合成サンプルを生産し,意思決定の境界から距離を保つことができました.
主要な成果:
- BADGANは,分類の境界に近い高品質の合成詐欺サンプルを効果的に生成します.
- 境界意識のアプローチは,サンプル品質を最適化し,下流分類器のパフォーマンスを改善します.
- 現実世界と公共のデータセットでの実験は,同等方法に対するBADGANの優越性を確認しました.
結論:
- BADGANは,FTFDにおける階級の不均衡をうまく緩和しています.
- 提案された方法は,詐欺検出モデルの正確性と堅実性を高めます.
- BADGANは,金融取引詐欺検出システムを改善するための有望なソリューションを提供します.
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