用深度学习与自编码器提升信用卡欺诈检测准确率，降低误报。
Credit Card Fraud Detection: A Deep Learning Approach
- 采用深度学习结合自编码器，无监督学习正常交易模式。
- 实现高欺诈覆盖率与极低误报率，优于传统方法。
- 适合需要实时、低误报的金融风控系统部署。
信用卡是当前电子交易中广泛使用的支付方式，但其便利性也带来了新的欺诈风险，导致机构与个人损失巨大。现有欺诈检测系统（FDS）普遍面临概念漂移、类别不平衡和验证延迟等挑战。大多数基于人工智能、机器学习等技术的系统难以全面应对这些难题。本文旨在通过深度学习方法实现高欺诈覆盖范围且误报率极低的检测效果，并引入自编码器作为无监督（半监督）学习手段，挖掘正常交易的共性模式，从而提升检测系统的动态适应能力与准确性。
原文摘要 · Abstract (English)
Credit card is one of the most extensive methods of instalment for both online and offline mode of payment for electronic transactions in recent times. credit cards invention has provided significant ease in electronic transactions. However, it has also provided new fraud opportunities for criminals, which results in increased fraud rates. Substantial amount of money has been lost by many institutions and individuals due to fraudulent credit card transactions. Adapting improved and dynamic fraud recognition frameworks thus became essential for all credit card distributing banks to mitigate their losses. In fact, the problem of fraudulent credit card transactions implicates a number of relevant real-time challenges, namely: Concept drift, Class imbalance, and Verification latency. However, the vast majority of current systems are based on artificial intelligence (AI), Fuzzy logic, Machine Learning, Data mining, Genetic Algorithms, and so on, rely on assumptions that hardly address all the relevant challenges of fraud-detection system (FDS). This paper aims to understand & implement Deep Learning algorithms in order to obtain a high fraud coverage with very low false positive rate. Also, it aims to implement an auto-encoder as an unsupervised (semi-supervised) method of learning common patterns. Keywords: Credit card fraud, Fraud-detection system (FDS), Electronic transactions, Concept drift, Class imbalance, Verification latency, Machine Learning, Deep Learning
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。