尼日利亚金融业的信用卡欺诈检测：基于TensorFlow的无监督异常检测技术、自编码器与PCA算法的比较
Credit Card Fraud Detection in the Nigerian Financial Sector: A Comparison of Unsupervised TensorFlow-Based Anomaly Detection Techniques, Autoencoders and PCA Algorithm
AI总结：
针对尼日利亚金融业信用卡欺诈问题，对比基于TensorFlow的无监督自编码器与PCA算法的欺诈检测效果，验证自编码器降维性能更优，更适配复杂交易数据且错标率更低。
摘要：
信用卡欺诈是尼日利亚金融业引发全国关注的重大问题，每秒影响数百笔交易，并对国际电商造成负面影响。尽管网络营销快速普及，但由于针对信用卡欺诈的禁令和政策，数百万尼日利亚人无法使用本地信用卡在多个国家进行交易。目前已有大量技术可用于检测欺诈交易，其中仅有少数被尼日利亚金融机构采用以主动管控这一局面。欺诈检测可让机构将违规者限制在网络之外，且借助集中式银行身份管理系统（如尼日利亚央行使用的银行验证号），可能盗用其他身份的违规者可被追溯，其银行账户也会被冻结。本文旨在比较两种预计可完全独立于人工干预运行、有望预测和检测信用卡欺诈交易的欺诈检测技术的有效性。自编码器作为一种基于TensorFlow的无监督异常检测技术，在降维方面通常表现出比主成分分析（Principal Component Analysis，PCA）更优的性能，本研究在尼日利亚信用卡交易数据上对这一理论进行了验证。结果表明，与PCA算法相比，自编码器更适合分析复杂且规模庞大的数据集，能提供更可靠的结果，且错标率极低。
英文摘要：
Credit card fraud is a major cause of national concern in the Nigerian financial sector, affecting hundreds of transactions per second and impacting international ecommerce negatively. Despite the rapid spread and adoption of online marketing, millions of Nigerians are prevented from transacting in several countries with local credit cards due to bans and policies directed at restricting credit card fraud. Presently, a myriad of technologies exist to detect fraudulent transactions, a few of which are adopted by Nigerian financial institutions to proactively manage the situation. Fraud detection allows institutions to restrict offenders from networks and with a centralized banking identity management system, such as the Bank Verification Number used by the Central Bank of Nigeria, offenders who may have stolen other identities can be backtraced and their bank accounts frozen. This paper aims to compare the effectiveness of two fraud detection technologies that are projected to work fully independent of human intervention to possibly predict and detect fraudulent credit card transactions. Autoencoders as an unsupervised tensorflow based anomaly detection technique generally offers greater performance in dimensionality reduction than the Principal Component Analysis, and this theory was tested out on Nigerian credit card transaction data. Results demonstrate that autoencoders are better suited to analyzing complex and extensive datasets and offer more reliable results with minimal mislabeling than the PCA algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
