论文标题
深层神经网络和K-Neart最邻居的信用卡欺诈检测的结合
A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection
论文作者
论文摘要
在信用卡上发现欺诈交易成为金融机构，组织和公司的主要问题之一。由于全球金融系统与非现金交易高度连接，在线运营欺诈制造商发明了更有效的访问客户财务状况的方法。信用卡欺诈检测的主要问题是，欺诈交易的数量明显低于真正的欺诈交易。该论文的目的是实施新技术，其中包含新的数据集中采样不足算法，K-Nearest邻居算法（KNN）和深神经网络（KNN）。绩效评估表明，DNN模型具有精确的高精度（98.12％），这表明了呈现方法检测欺诈交易的良好能力。
Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies. As the global financial system is highly connected to non-cash transactions and online operations fraud makers invent more effective ways to access customers' finances. The main problem in credit card fraud detection is that the number of fraud transactions is significantly lower than genuine ones. The aim of the paper is to implement new techniques, which contains of under-sampling algorithms, K-nearest Neighbor Algorithm (KNN) and Deep Neural Network (KNN) on new obtained dataset. The performance evaluation showed that DNN model gives precise high accuracy (98.12%), which shows the good ability of presented method to detect fraudulent transactions.
