A Study on Translation Approaches to Commercial Letters of Credit under the Guidance of Skopos Theory

According to the customs,by the end of 2011,the whole value of Chinese foreign trade is two thousand nine hundred and ninety four billion dollars,by the end of 2009,5 percents of the accounts of Chinese foreign trade with a value of fifty hundred billion dollars are unpaid.Most of the financial loss are caused by wrong translation of documents.The letter of credit is the most important way of payment in the international trade,so great attention should be paid to the translation of this type of document.Until now,the study against the translation of the letter of credit is scant.This paper will apply the skopos theory put forward by Hans Vermeer and developed in Germany in the late 1970s to guide the translation practice of the letter of credit.

Defending the Soft Clause of the Letter of Credit

The soft clause of the letter of credit(L/C)is a very difficult problem in international trade practice.Starting with the causes of the soft clause of L/C,this paper analyzes the methods of recognization and preventive measures,Providing knowledge and skills reserve for international trade students which entering the workplace.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

A Letter of Credit

In international trade, one of the major things is how to protect benefits and implement obligations of seller and buyer, more concerns are than domestic businesses. As a result, solutions for facilitate and ensure obligations are undeniable necessity. By the nature, Letter of credit balance risk for both seller and buyer sides, protect the benefits and ensure obligations. Letter of credit plays a very important role in international trade.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Credit Card Fraud Detection Using Weighted Support Vector Machine

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.

Credit Card Fraud Detection Based on Machine Learning

In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its obvious advantages including discounts and earning credit card points.So credit card fraudulence has become a target of concern.In order to deal with the situation,credit card fraud detection based on machine learning is been studied recently.Yet,it is difficult to detect fraudulent transactions due to data imbalance(normal and fraudulent transactions),for which Smote algorithm is proposed in order to resolve data imbalance.The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’daily transactions.Besides,to prove the new model’s superiority in detecting credit card fraudulence,Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment.The results indicate that Light Gradient Boosting Machine model has a good performance.The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99%in real dataset and fast feedback,which proves the new model’s efficiency in detecting credit card fraudulence.

Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.

Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network

Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the ability to defraud people through online transactions.Fraudsters make assumptions about consumers’routine behaviour,and fraud develops swiftly.Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques.Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current algorithms.When compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_RNN.In this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods.

aLMGAN-信用卡欺诈检测方法

针对信用卡交易数据的不平衡重叠问题,提出一种基于生成对抗网络的端到端一类分类方法。提出一种基于PCA和T_SNE的混合数据降维方法,对清洗后的数据进行特征降维;将降维后的数据送入所提出的基于LSTM和aMLP的生成对抗网络(aLMGAN),提出一种基于闵可夫斯基距离(Minkowski distance)的损失函数(Min-loss)代替原始生成对抗网络中的交叉熵损失函数,对正常交易数据进行单类稳定训练,形成一种特殊特征模式,区分不属于该特征的异常数据。通过使用kaggle上两个真实的公共信用卡交易数据集进行实验,验证了aLMGAN算法的有效性。

基于特征提取和集成学习的个人信用评分方法

在大数据蓬勃发展的今天,信息经济已经深入社会方方面面,个人信用体系建设的重要性越发突出。而传统的信用体系存在覆盖率不足、评价特征维度高、数据孤岛等问题,为了解决以上问题,提出一种基于特征提取和Stacking集成学习的个人信用评分方法(PSL-Stacking)。方法首先利用Pearson和Spearman系数对数据进行初始化分析剔除不相关数据,利用LightGBM算法进行特征选择,减少冗余特征对模型的影响;其次选取XGboost、LightGBM、Random Forest以及Huber回归等算法,利用Stacking集成学习技术构造个人信用评分模型。最后,以某电信数据为研究对象,对该上述模型的个人信用评分能力进行验证。实验结果得出上述模型具有很好的预测能力,能够准确的对用户信用进行评分,有效降低企业遭受金融欺诈、团伙套利等问题的风险。

证监会警示函处罚与评级机构应对

本文分析了中国证监会对评级机构的警示函处罚带来的影响和评级机构的应对。研究发现,受处罚的评级机构会显著上调债券发行人的主体信用评级,并获得更高的评级市场份额。在采用熵平衡匹配等方法进行稳健性检验后,结果仍然存在。进一步分析发现,上调发行人评级的动作在发行人与评级机构之间存在利益关联,或者发行人自身违约风险较低时更为明显。本文系统地分析了警示函处罚对评级机构行为的影响,为我国评级机构监管的发展提供了实证依据。

CTGANBoost:基于CTGAN与Boosting的信贷欺诈检测研究

在金融行业中,信贷欺诈检测是一项重要的工作,能够为银行和消金机构减少大量的经济损失。然而,信贷数据中存在类别不平衡和正负样本特征重叠的问题,导致少数类识别灵敏度低且不同类别数据区分度低。针对这些问题,提出一种面向信贷欺诈检测的CTGANBoost方法。首先,在AdaBoost(Adaptive Boosting)方法的每一轮Boosting迭代中,引入基于类别标签信息约束的CTGAN(Conditional Tabular Generative Adversarial Network)方法学习特征分布,进行少数类数据增强工作;其次,基于CTGAN合成的增强数据集,设计了权重归一化方法,确保在样本加权过程中保持原始数据集的分布特征和相对权重。在3个开源数据集上的实验结果表明,CTGANBoost方法的表现均优于其他主流的信贷欺诈检测方法,AUC值提升了0.5%~2.0%,F1值提升了0.6%~1.8%,验证了CTGANBoost方法的有效性和泛化能力。

基于注意力机制优化的WGAN-BiLSTM信用卡欺诈检测方法

信用卡欺诈是银行操作风险的主要来源之一,对信用卡诈骗交易进行准确的检测对于减少银行经济损失具有重要意义。针对信用卡欺诈检测中存在的数据类别不平衡和数据漂移的问题,提出一种基于注意力机制优化的WGAN-BiLSTM信用卡欺诈检测方法。首先引入Wasserstein距离改进生成对抗网络(GAN),将信用卡数据输入至WGAN(Wasserstein GAN)中,在生成器和判别器相互博弈训练下,得到符合目标分布的欺诈样本;然后,构建结合注意力机制的双向长短期记忆(BiLSTM)网络,在正反两个方向上提取信用卡数据的长时依赖关系;最后,通过Softmax层输出分类结果。在欧洲持卡人数据集上的实验结果表明,所提方法能有效提升信用卡欺诈检测效果。

基于XGBoost与LR融合模型的信用卡欺诈检测

随着银行卡业务的不断发展,各种各样的信用卡欺诈方式已经给金融机构带来严重的威胁,使得信用卡欺诈检测成为一个十分紧迫的任务。为解决此问题,提出一种XGBoost与LR融合模型。该模型首先运用XGBoost算法自动进行特征组合和离散化,然后将新构造的特征向量运用在逻辑回归LR模型上,通过XGBoost与LR融合模型进行分类预测。实验结果表明,与经典传统算法相比,提出的XGBoost与LR融合模型具有更好的欺诈检测性能,提高了信用卡欺诈检测的准确率。

基于改进SMOTE算法和深度学习集成框架的信用卡欺诈检测

当前机器学习(ML)算法已经被广泛用于信用卡欺诈检测。然而持卡人线上购物的动态性,以及正常和欺诈交易数据严重不平衡问题,影响了分类器的检测精度。为此,提出了基于深度学习集成框架的信用卡欺诈检测方法。首先,通过改进的合成少数类过采样(SMOTE)算法,解决信用卡数据集中欺诈交易和正常交易数量严重不平衡问题。其次,构建堆栈式深度学习集成框架,使用双向长短时记忆网络(Bi-LSTM)和门控循环单元(GRU)作为基础分类器,并通过多层感知机(MLP)作为元分类器,结合集成学习和深度学习的优点提高信用卡欺诈检测率。在公开数据集上的实验结果表明,所提深度学习集成方法与改进SMOTE算法相结合,分别实现了99.57%和99.82%的灵敏度和特异性结果,优于其他先进的信用卡欺诈检测算法。