Credit card fraud detection is a critical challenge in the financial sector,
demanding sophisticated approaches to accurately identify fraudulent
transactions. This research proposes an innovative methodology combining Neural
Networks (NN) and Synthet ic Minority Over-sampling Technique (SMOTE) to
enhance the detection performance. The study addresses the inherent imbalance
in credit card transaction data, focusing on technical advancements for robust
and precise fraud detection. Results demonstrat e that the integration of NN
and SMOTE exhibits superior precision, recall, and F1-score compared to
traditional models, highlighting its potential as an advanced solution for
handling imbalanced datasets in credit card fraud detection scenarios. This
rese arch contributes to the ongoing efforts to develop effective and efficient
mechanisms for safeguarding financial transactions from fraudulent activities.

信用卡欺诈检测是金融领域的一个重要挑战，需要使用先进的方法准确识别欺诈交易。本研究提出了一种创新的方法，将神经网络（NN）和合成少数类过采样技术（SMOTE）结合起来，以提高识别性能。该研究解决了信用卡交易数据中存在的不平衡问题，专注于技术的进步，用于强大而准确的欺诈检测。结果表明，相比传统模型，NN 和 SMOTE 的整合在精确度、召回率和 F1 得分方面表现出优越性，凸显其作为处理不平衡数据集的信用卡欺诈检测场景中的先进解决方案的潜力。本研究为开发有效和高效的机制来保护金融交易免受欺诈活动的努力做出了贡献。

提升信用卡欺诈检测的神经网络与 SMOTE 集成方法

Enhancing Credit Card Fraud Detection A Neural Network and SMOTE  Integrated Approach

In an increasingly digitalized commerce landscape, the proliferation of
credit card fraud and the evolution of sophisticated fraudulent techniques have
led to substantial financial losses. Automating credit card fraud detection is
a viable way to accelerate detection, reducing response times and minimizing
potential financial losses. However, addressing this challenge is complicated
by the highly imbalanced nature of the datasets, where genuine transactions
vastly outnumber fraudulent ones. Furthermore, the high number of dimensions
within the feature set gives rise to the ``curse of dimensionality". In this
paper, we investigate subspace learning-based approaches centered on One-Class
Classification (OCC) algorithms, which excel in handling imbalanced data
distributions and possess the capability to anticipate and counter the
transactions carried out by yet-to-be-invented fraud techniques. The study
highlights the potential of subspace learning-based OCC algorithms by
investigating the limitations of current fraud detection strategies and the
specific challenges of credit card fraud detection. These algorithms integrate
subspace learning into the data description; hence, the models transform the
data into a lower-dimensional subspace optimized for OCC. Through rigorous
experimentation and analysis, the study validated that the proposed approach
helps tackle the curse of dimensionality and the imbalanced nature of credit
card data for automatic fraud detection to mitigate financial losses caused by
fraudulent activities.

利用基于子空间学习的 OCC 算法来处理信用卡欺诈检测的挑战，解决数据高度不平衡和维度灾难问题，减少欺诈交易带来的财务损失。

基于子空间学习的一类分类的信用卡欺诈检测

Credit Card Fraud Detection with Subspace Learning-based One-Class  Classification

Credit card fraud detection is a very challenging problem because of the
specific nature of transaction data and the labeling process. The transaction
data is peculiar because they are obtained in a streaming fashion, they are
strongly imbalanced and prone to non-stationarity. The labeling is the outcome
of an active learning process, as every day human investigators contact only a
small number of cardholders (associated to the riskiest transactions) and
obtain the class (fraud or genuine) of the related transactions. An adequate
selection of the set of cardholders is therefore crucial for an efficient fraud
detection process. In this paper, we present a number of active learning
strategies and we investigate their fraud detection accuracies. We compare
different criteria (supervised, semi-supervised and unsupervised) to query
unlabeled transactions. Finally, we highlight the existence of an
exploitation/exploration trade-off for active learning in the context of fraud
detection, which has so far been overlooked in the literature.

本文研究了信用卡欺诈检测的问题，提出了一些主动学习的策略，并比较了不同的查询标准，强调了在欺诈检测中主动学习存在探索 / 开发的权衡。