A support vector machine-based model for detecting top management fraud

Detecting fraudulent financial statements (FFS) is critical in order to protect the global financial market. In recent years, FFS have begun to appear and continue to grow rapidly, which has shocked the confidence of investors and threatened the economics of entire countries. While auditors are the last line of defense to detect FFS, many auditors lack the experience and expertise to deal with the related risks. This study introduces a support vector machine-based fraud warning (SVMFW) model to reduce these risks. The model integrates sequential forward selection (SFS), support vector machine (SVM), and a classification and regression tree (CART). SFS is employed to overcome information overload problems, and the SVM technique is then used to assess the likelihood of FFS. To select the parameters of SVM models, particle swarm optimization (PSO) is applied. Finally, CART is employed to enable auditors to increase substantive testing during their audit procedures by adopting reliable, easy-to-grasp decision rules. The experiment results show that the SVMFW model can reduce unnecessary information, satisfactorily detect FFS, and provide directions for properly allocating audit resources in limited audits. The model is a promising alternative for detecting FFS caused by top management, and it can assist in both taxation and the banking system.     

Data Mining techniques for the detection of fraudulent financial statements

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances.     

基于RST修正核单类SVM的程序行为控制系统

程序行为控制系统对程序行为进行建模、检测和响应。单类支持向量机(SVM)在有限样本的情况下用于异常检测,具有较好的分类精度和泛化能力。针对以前利用单类支持向量机进行异常检测的研究中没有考虑属性权重的问题,该文提出利用粗糙集理论(RST),引入反映属性重要性程度的权重值。给出通过找出决策系统中所有约简的集合确定属性权重的方法,并利用属性权重修正单类SVM的核函数。实验表明基于RST修正核的单类SVM具有更好的检测能力。     

Support vector machine with adaptive parameters in financial time series forecasting

A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.     

Dynamic analysis of a novel time‐lag four‐dimensional fractional‐order financial system

In this paper, a novel four‐dimensional fractional‐order financial system (FFS) with time delay is presented. Unlike traditional bifurcation analysis of financial systems, the selection rules of two bifurcation points within the system are discussed. In addition, the motion state of the system in the vicinity of two bifurcation points are analyzed separately, such that the dynamic analysis of this novel nonlinear fourth‐dimensional FFS is more comprehensive. The detailed dynamical behaviors of this financial system, such as oscillation, stability, and bifurcation points, are deduced via rigorous mathematical analysis. Finally, some simulations are performed to verify the dynamic characteristics of the FFS around the two bifurcation points which satisfy the selection conditions of the bifurcation point.     

Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM

The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, ‘rough set theory’ is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy.     

基于边缘检测的生产线手机膜缺陷识别方法

目前国内手机保护膜的产量和销量巨大,但手机膜生产线上的缺陷检验仍采用目检法,检测效率与准确率较低.针对生产线上手机膜缺陷检测的4个关键问题(正常与缺陷类别不平衡、高信噪比去噪、边缘特征提取以及缺陷样本检测效率)进行研究.采用RST和图像剪切方法实现缺陷样本扩充,解决缺陷样本少,缺陷位置和形式不足问题;提出自适应小波阈值及新的阈值函数,实现传统小波阈值去噪方法的改进,获得优异的去噪效果;在图像边缘检测技术中,引入改进小波阈值去噪方法及Otsu阈值设置方法,提高传统Canny算子的边缘检测性能,实现图像特征有效提取;利用具有旋转、平移及尺度不变性的Zernike矩对边缘检测结果进行特征表达,提高模式识别的效率及准确率.采用支持向量机(SVM)对正常手机膜和缺陷手机膜的边缘Zernike矩特征进行识别,实验结果表明所提方法准确率高、检测速度快,满足生产线上手机膜的缺陷检测要求.     

基于支持向量机的高速公路事件检测

提出用支持向量机分类方法研究高速公路事件检测问题。阐述了支持向量机分类算法,根据交通事件对交通流参数的影响规律选择了支持向量机的输入量,用高速公路管理处提供的样本数据进行了仿真研究。仿真实验表明,支持向量机事件检测算法具有检测准确率高、训练时间短、泛化能力好等优点,它为事件检测提供了一种切实可行的新思路。     

Predicting business failure using support vector machines with straightforward wrapper: A re-sampling study

Business failure prediction (BFP) is an effective tool to help financial institutions and relevant people to make the right decision in investments, especially in the current competitive environment. This topic belongs to a classification-type task, one of whose aims is to generate more accurate hit ratios. Support vector machine (SVM) is a statistical learning technique, whose advantage is its high generalization performance. The objective of this context is threefold. Firstly, SVM is used to predict business failure by utilizing a straightforward wrapper approach to help the model produce more accurate prediction. The wrapper approach is fulfilled by employing a forward feature selection method, composed of feature ranking and feature selection. Meanwhile, this work attempts to investigate the feasibility of using linear SVMs to select features for all SVMs in the wrapper since non-linear SVMs yield to over-fit the data. Finally, a robust re-sampling approach is used to evaluate model performances for the task of BFP in China. In the empirical research, performances of linear SVM, polynomial SVM, Gaussian SVM, and sigmoid SVM with the best filter of stepwise MDA, and wrappers respectively using linear SVM and non-linear SVMs as evaluating functions are to be compared. The results indicate that the non-linear SVM with radial basis function kernel and features selected by linear SVM compare significantly superiorly to all the other SVMs. Meanwhile, all SVMs with features selected by linear SVM produce at least as good performances as SVMs with other optimal features.     

基于SVM的报税欺诈检测

税收申报欺诈检测是税务机关税收征管和稽查中面临的一个重要问题。该文提出了一种基于SVM的税收中报欺诈检测方法。首先用采样来的企业经营和财务数据训练好．个SVM识别系统，然后用这个SVM识别系统判断一个企业的报税额是否真实。实验结果说明该打法是一种有效的疗法，在31个测试样本中，检测精度达87．10％，比基于See5．0的方法高3．23％，而训练时间只需1.708。     

AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies

Due to the important role of financial distress prediction (FDP) for enterprises, it is crucial to improve the accuracy of FDP model. In recent years, classifier ensemble has shown promising advantage over single classifier, but the study on classifier ensemble methods for FDP is still not comprehensive enough and leaves to be further explored. This paper constructs AdaBoost ensemble respectively with single attribute test (SAT) and decision tree (DT) for FDP, and empirically compares them with single DT and support vector machine (SVM). After designing the framework of AdaBoost ensemble method for FDP, the article describes AdaBoost algorithm as well as SAT and DT algorithm in detail, which is followed by the combination mechanism of multiple classifiers. On the initial sample of 692 Chinese listed companies and 41 financial ratios, 30 times of holdout experiments are carried out for FDP respectively one year, two years, and three years in advance. In terms of experimental results, AdaBoost ensemble with SAT outperforms AdaBoost ensemble with DT, single DT classifier and single SVM classifier. As a conclusion, the choice of weak learner is crucial to the performance of AdaBoost ensemble, and AdaBoost ensemble with SAT is more suitable for FDP of Chinese listed companies.     

基于PSO-SVM的电机故障检测

传统智能故障检测模型中算法初始参数复杂,选取难度较大,缺乏自学习、自组织能力、泛化能力弱,极易陷入局部极小值、算法单一等缺点.组合应用智能检测算法可整合不同算法优势,避免单一算法缺点,为此,文中提出支持向量机算法与改进粒子群算法相结合的电机故障检测模型:以电机故障特征频率特征数据为基础,首先使用改进全局求解性能的粒子群算法求解影响支持向量机分类检测性能的最佳参数,然后把最佳参数应用于的擅长模式识别的支持向量机算法,进行样本数据的训练,构建故障检测模型;最后,使用故障检测模型对电机的状态进行预测.实验结果表明,采用该方法进行故障检测的准确率,比传统的神经网络方法提高17%,比纯支持向量机算法提高3.33%.     

Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches

In this paper, we compare some traditional statistical methods for predicting financial distress to some more “unconventional” methods, such as decision tree classification, neural networks, and evolutionary computation techniques, using data collected from 200 Taiwan Stock Exchange Corporation (TSEC) listed companies. Empirical experiments were conducted using a total of 42 ratios including 33 financial, 8 non-financial and 1 combined macroeconomic index, using principle component analysis (PCA) to extract suitable variables.This paper makes four critical contributions: (1) with nearly 80% fewer financial ratios by the PCA method, the prediction performance is still able to provide highly-accurate forecasts of financial bankruptcy; (2) we show that traditional statistical methods are better able to handle large datasets without sacrificing prediction performance, while intelligent techniques achieve better performance with smaller datasets and would be adversely affected by huge datasets; (3) empirical results show that C5.0 and CART provide the best prediction performance for imminent bankruptcies; and (4) Support Vector Machines (SVMs) with evolutionary computation provide a good balance of high-accuracy short- and long-term performance predictions for healthy and distressed firms. Therefore, the experimental results show that the Particle Swarm Optimization (PSO) integrated with SVM (PSO-SVM) approach could be considered for predicting potential financial distress.     

Fast Filtered Sampling

Traffic sampled from the network backbone using uniform packet sampling is commonly utilized to detect heavy hitters, estimate flow level statistics, as well as identify anomalies like DDoS attacks and worm scans. Previous work has shown however that this technique introduces flow bias and truncation which yields inaccurate flow statistics and “drowns out” information from small flows, leading to large false positives in anomaly detection.In this paper, we present a new sampling design: Fast Filtered Sampling (FFS), which is comprised of an independent low-complexity filter, concatenated with any sampling scheme at choice. FFS ensures the integrity of small flows for anomaly detection, while still providing acceptable identification of heavy hitters. This is achieved through a filter design which suppresses packets from flows as a function of their size, “boosting” small flows relative to medium and large flows. FFS design requires only one update operation per packet, has two simple control parameters and can work in conjunction with existing sampling mechanisms without any additional changes. Therefore, it accomplishes a lightweight online implementation of the “flow-size dependent” sampling method. Through extensive evaluation on traffic traces, we show the efficacy of FFS for applications such as portscan detection and traffic estimation.     

Forecasting financial condition of Chinese listed companies based on support vector machine

Due to the radical changing and specialty of Chinese capital market, it is challenging to develop a powerful financial distress prediction model. In this paper, we first analyzed the feasibility of Chinese special-treated companies as distressed sample by using statistical methods. Then we developed a prediction model based on support vector machines (SVM) for an unmatched sample of Chinese high-tech manufacture companies. The grid-search technique using 10-fold cross-validation is used to find out the best parameter value of kernel function of SVM. The experiment results show that the proposed SVM model outperforms conventional statistical methods and back-propagation neural network. In general, SVM provides a robust model with high prediction accuracy for forecasting financial distress of Chinese listed companies. It is also suggested that Chinese special-treated event adopted as cut-off line has some effect on the prediction accuracy of the models.     

The use of hybrid manifold learning and support vector machines in the prediction of business failure

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. This paper proposes a hybrid manifold learning approach model which combines both isometric feature mapping (ISOMAP) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the ISOMAP algorithm to perform dimension reduction, is then utilized as a preprocessor to improve business failure prediction capability by SVM. To create a benchmark, we further compare principal component analysis (PCA) and SVM with our proposed hybrid approach. Analytic results demonstrate that our hybrid approach not only has the best classification rate, but also produces the lowest incidence of Type II errors, and is capable of achieving an improved predictive accuracy and of providing guidance for decision makers to detect and prevent potential financial crises in the early stages.     

Construct support vector machine ensemble to detect traffic incident

This study presents the applicability of support vector machine (SVM) ensemble for traffic incident detection. The SVM has been proposed to solve the problem of traffic incident detection, because it is adapted to produce a nonlinear classifier with maximum generality, and it has exhibited good performance as neural networks. However, the classification result of the practically implemented SVM depends on the choosing of kernel function and parameters. To avoid the burden of choosing kernel functions and tuning the parameters, furthermore, to improve the limited classification performance of the real SVM, and enhance the detection performance, we propose to use the SVM ensembles to detect incident. In addition, we also propose a new aggregation method to combine SVM classifiers based on certainty. Moreover, we proposed a reasonable hybrid performance index (PI) to evaluate the performance of SVM ensemble for detecting incident by combining the common criteria, detection rate (DR), false alarm rate (FAR), mean time to detection (MTTD), and classification rate (CR). Several SVM ensembles have been developed based on bagging, boosting and cross-validation committees with different combining approaches, and the SVM ensemble has been tested on one real data collected at the I-880 Freeway in California. The experimental results show that the SVM ensembles outperform a single SVM based AID in terms of DR, FAR, MTTD, CR and PI. We used one non-parametric test, the Wilcoxon signed ranks test, to make a comparison among six combining schemes. Our proposed combining method performs as well as majority vote and weighted vote. Finally, we also investigated the influence of the size of ensemble on detection performance.     

The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements

In this paper a brute force logistic regression (LR) modeling approach is proposed and used to develop predictive credit scoring model for corporate entities. The modeling is based on 5 years of data from end-of-year financial statements of Serbian corporate entities, as well as, default event data. To the best of our knowledge, so far no relevant research about predictive power of financial ratios derived from Serbian financial statements has been published. This is also the first paper that generated 350 financial ratios to represent independent variables for 7590 corporate entities default predictions’. Many of derived financial ratios are new and were not discussed in literature before. Weight of evidence (WOE) method has been applied to transform and prepare financial ratios for brute force LR fitting simulations. Clustering method has been utilized to reduce long list of variables and to remove highly correlated financial ratios from partitioned training and validation datasets. The clustering results have revealed that number of variables can be reduced to short list of 24 financial ratios which are then analyzed in terms of default event predictive power. In this paper we propose the most predictive financial ratios from financial statements of Serbian corporate entities. The obtained short list of financial ratios has been used as a main input for brute force LR model simulations. According to literature, common practice to select variables in final model is to run stepwise, forward or backward LR. However, this research has been conducted in a way that the brute force LR simulations have to obtain all possible combinations of models that comprise of 5–14 independent variables from the short list of 24 financial ratios. The total number of simulated resulting LR models is around 14 million. Each model has been fitted through extensive and time consuming brute force LR simulations using SAS® code written by the authors. The total number of 342,016 simulated models (“well-founded” models) has satisfied the established credit scoring model validity conditions. The well-founded models have been ranked according to GINI performance on validation dataset. After all well-founded models have been ranked, the model with highest predictive power and consisting of 8 financial ratios has been selected and analyzed in terms of receiver-operating characteristic curve (ROC), GINI, AIC, SC, LR fitting statistics and correlation coefficients. The financial ratio constituents of that model have been discussed and benchmarked with several models from relevant literature.     

基于模拟退火支持向量机的入侵检测系统

为了提高入侵检测系统在小样本集条件下的检测效率,将支持向量机用于网络入侵检测.支持向量机的参数决定了检测效率,然而难以选择合适的参数值,因此提出利用模拟退火算法来优化这些参数,并设计出基于参数优化的支持向量机用于入侵检测.通过对样本数据集中的样本进行实验性检测,并与原始支持向量机入侵检测系统进行比较,结果表明模拟退火支持向量机入侵检测系统检测率高、误报率低,并且缩短了训练时间和检测时间.     

面向半球尺度数据的云检测方法

目的 云覆盖着地球上空大部分区域,在地球水循环、地气系统能量平衡和辐射传输过程中有着重要的作用,同时云也是天气气候中最重要、最活跃的因子之一;此外,云覆盖地表信息,导致影像配准、融合等处理过程的很多问题,所以云检测十分重要。方法 基于2015年发射的深空气候观测台（DSCOVR）卫星搭载的地球彩色成像相机（EPIC）数据,针对EPIC数据波段范围较广和影像数据是半球尺度的特点,以云指数法作为基础,提出一种新的面向半球尺度数据的云检测方法。首先,分析EPIC数据各个波段的波段特征,尤其是紫光波段,然后根据云在不同波段的反射特性,以指数的形式完成波段组合进行云检测,再与SVM（support vector machine）云检测法和可见光云检测法进行比较,最后利用EPIC L2产品对所获得的云分布图和统计云量值进行结果验证,以正确率、漏检率、误检率和Kappa系数作为参考标准完成精度评定。结果 实际EPIC夏季（2017年7月）和冬季（2017年1月）数据的实验结果表明,本文方法的正确率均高于91%,Kappa系数大于0.9;其他方法的正确率均低于89%,且Kappa系数在0.8左右,均小于0.9。所以本文能够有效地检测到薄云（即使在冬季）,且云量和云的分布都最为接近实际。结论 在EPIC影像的云检测过程中,本文方法从云分布图和云量结果两个方面都优于可见光云检测法和SVM云检测法,经EPIC L2产品验证,本文方法有效、可靠,且能够快速获得半球范围内云的分布情况,有助于对全球云的动态研究和自然天气预测。