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\title{Anti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services}
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\begin{document}

             \author[1]{Yan Quan  Liu}

             \affil[1]{  Beihang University; Southern Connecticut State University}

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\date{\small \em Received: 12 December 2015 Accepted: 2 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


This study aims to determine the best practices and provide a model of the technical solutions that can effectively and systematically limit fraudulent transactions of online orders in e-commerce services, using the methods of analytical mining and case studies. Based on a process of fraud prevention and detection performed in the e-business Dangdang, Inc., a leading online retailer in China, twelve identifying features of fraudulent order data were extracted and compiled into a feature matrix. Logistic regression with this matrix was then used to build a model to judge if an order was fraudulent. The model was tested using various order data with machine learning techniques to meet the requirements of being effective, correct, adaptive, and persistent. Then an online detection and prevention schema was established and the hypothesis of so-called Behavior Pattern Change Assumption (BPCA) was proven. The results show the model can detect 94% of fraudulent orders. The Anti-fraud Schema System established for Dangdang is shown to be the best model for the determination and prevention of fraudulent behaviors in the e-commerce services.

\end{abstract}


\keywords{e-commerce services, fraud behavior, determination, fraud prevention, case studies, logistic regression, machine learning.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
lectronic commerce has enjoyed rapid growth in recent years \hyperref[b0]{[1]}, as more and more people have accepted online shopping. However, along with the growing number of transactions, there are a growing number of fraud activities. The temptation of economic gain and the difficulty of internet supervision have led to a great number of online fraud activities. Hackers can steal online accounts and use these accounts in criminal activities \hyperref[b1]{[2]}. Prevention of fraud activities in order to provide a safe online shopping experience is a challenge for electronic commerce \hyperref[b2]{[3]}. EBay is the leading e-commerce company around the globe, and every day thousands of customers trade through eBay. Therefore, eBay has hired experts from the National Aeronautics and Space Administration (NASA) of the E-commerce started late in China, and few resources have been devoted to the anti-fraud field, so systematic anti-fraud solutions are especially scarce. As a leading business-to-consumer e-business in China founded in 1999, China Dangdang, Inc. offers products mainly in the categories of books, audios, digital devices, and household merchandise. Dangdang made an initial public offering (IPO) on the New York Stock Exchange in November 2010and had over 9,000,000 active customers. Because of its main business is online, Dangdang shows great interest in solving Internet-related fraud problems, especially those involving online orders directly affecting its customers, as its key strategy is to grow its e-business, Beginning with are iew of past studies relevant to the present research, this paper provides detailed process descriptions of an anti-fraud model's development (Section 3), and discussions of its implementation with a real data mining process (Section 4). The results were concluded significantly that proven the best practice of fraud determination and detection in real situation of e-commerce order transactions. 
\section[{II. Literature Review}]{II. Literature Review}\par
A review by Hogan \hyperref[b3]{[4]} summarized research on fraud behavior over the past decade. Prior to that researchers mainly focused on fraud in the areas of accounting, auditing and finance activities \hyperref[b4]{[5]}.The growth of online transactions led to a growth of fraud activities, and the lack of supervision online made it easy to commit fraud \hyperref[b2]{[3]}. The online market has some unique features that attract fraud activities, namely information asymmetry, online transactions and the uncertainty of traders' identities and commodities \hyperref[b5]{[6]}.\par
Lou \& Wang's research reveals that though many methods can be used to prevent fraud activities, e-commerce should use a systematic method to solve the problems uniformly \hyperref[b6]{[7]}. Account information is collected and the information used to summarize the patterns of fraud to adapt to different situations \hyperref[b7]{[8]}. Latch (1999) pointed out that using classification algorithms such as ID3 and C4.5 to detect fraud patterns and identify accounts with suspicious activity Detecting fraud behavior and managing fraud risk require the design and application of a frauddetection model. Eining et al. find that auditors can manage different levels of fraud risk better and make unanimous auditing decisions by using an expert system \hyperref[b9]{[10]}. Green and Choi use a neural network to detect fraud behavior and achieve satisfactory results \hyperref[b10]{[11]}.\par
Ohlson found that the identifying features of fraud activity could be used to alert sellers to fraud activities during financial transactions \hyperref[b11]{[12]}. Lenard and Alam used logistic regression in detecting fraud activities \hyperref[b12]{[13]}. This method has also been employed in several researcheslater on \hyperref[b13]{[14]} \hyperref[b14]{[15]}.\par
Maranzato \hyperref[b0]{[1]} researches how to detect fraud activities in an e-commerce system. He then uses logistic regression to detect and identify features of credit fraud, and he also points out that logistic regression depends greatly on the data quality \hyperref[b15]{[16]}. 
\section[{III. Research design}]{III. Research design}\par
This work combines analytical mining and case study using the real data of Dangdang's sale transactions to identify the common patterns of detecting and preventing fraud activities from occurring within online orders. 
\section[{a) Collecting and Processing Data}]{a) Collecting and Processing Data}\par
Real customer order records collected from Dangdan's transaction logs, including initially identified fraudulent orders by the company's customer services officers, were processed in the following five phases:\par
Phase one: collect order data from Dangdang (01/01/2014-07/09/2014) and statistically analyze it to identify the features that can distinguish fraudulent orders from legitimate ones. Purposely, we used this method to find key features of fraud activities to and build a feature matrix system from machine learning.\par
Phase two: use the order data collected from the same period of time (01/01/2014-07/09/2014) to develop and train a logistic regression model to predict orders that are unusual.\par
Phase three: test the order data collected from Dangdang (07/01/2014-08/31/2014) with this logistic regression model to reveal how well the model works on the condition that the fraud ratio of orders is high.\par
Phase four: test the order data collected from Dangdang (10/07/2014-10/28/2014) with this logistic regression model again to reveal how well the model works on the condition that the fraud ratio of orders is low.\par
Phase five: employ this model in a non line environment with real customer records to assess the usefulness of the model. 
\section[{b) Making Behavior Pattern Change Assumption}]{b) Making Behavior Pattern Change Assumption}\par
Unusual orders are associated with customer behaviors differing from normal ones. As empirical evidence in daily sales has accumulated, the researchers are convinced with such a set of hypothetical rules that may direct the fraud discovering process, as we so call Behavior Pattern Change Assumption (BPCA).\par
Rule 1, for most of the e-commerce user accounts, customer behaviors are consistent with shipping address, receiver name, receiver phone, payment habits, and so on, remaining steady. This is called 'steady behavior.' Sudden changes of some or all of these attributes may indicate fraudulent behavior.\par
Rule 2, when an order is confirmed as a fraudulent order, all the orders whose receiver address, receiver IP and so on are same as this order are considered suspicious. This is because one hacker may steal multiple accounts and make multiple orders; however, the IP and address may stay the same. It's like one fraudulent order infects the IP or address. This is known as the 'suspicion infection'. Rule 3, hackers won't add their own money to an account but will just deplete the balance in the account or do other things that won't benefit the account but will deplete all possible resources from the account. They want to maximize their profit. This is called ''maximum rob". 
\section[{c) Underling Research Procedure}]{c) Underling Research Procedure}\par
An outline of the research procedure for this study consists of defining the case, analyzing the data, until extracting, evaluating and implementing the outcomes shown in Figure  {\ref 3}. This work focuses on how to detect and prevent fraud activities in online transactions, especially before fraudulent orders are completed in the environment of ecommerce.\par
Analyzing order data: The data from orders that have been marked as fraudulent by customer service are analyzed. Feature extraction: Custom service and technology experts brainstorm to extract some features that may distinguish fraudulent orders from legitimate ones and statistically test them. Model construction: Use the statistical analysis and apply an algorithm to the data and establish the norm of the algorithm. Use order data to create a logistic regression model. Test and optimize the model: Use test order data to test and assess the performance of the model, then continue optimizing the model. Model application: Use the fraudulent order detection model to judge online transaction orders then assess the performance and economy value of the model. 
\section[{IV. Construction of a Fraudulent Orders' Detection Model}]{IV. Construction of a Fraudulent Orders' Detection Model}\par
In an attempt to create effective technical solutions that could systematically limit fraudulent transactions of Internet orders, a fraudulent order detection model based on the Behavior Pattern Change Assumption (BPCA) was developed, consisting of the following process. 
\section[{a) Determination of Fraudulent orders}]{a) Determination of Fraudulent orders}\par
Fraudulent orders occur when a hacker steals a customer's account and uses the balance in the account to purchase goods for him/her self. Fraudulent orders are confirmed when customers call customer service to complain. Customer service staff will also call the customer to check if the customer or hacker places an unusual order, which usually is the primary method to determine whether the order is a "regular" or "fraud" order. 
\section[{b) Process of Analysis}]{b) Process of Analysis}\par
The core idea of fraudulent order detection is to compare normal orders with fraudulent orders to find identifying features that distinguish them. These features can then be used to judge if an order is fraud or not with BPCA.\par
There are three steps to extract the features of fraudulent orders:\par
Step 1: Customer service staffs locate fraudulent orders because of customer complaints.\par
Step 2: Statistical analysis of commonly used information such as the IP address of the order, receiver name, receiver address and receiver phone number.\par
Step 3: Compare normal orders with fraudulent orders to identify features that distinguish fraudulent orders from normal ones. 
\section[{c) Analysis of Source Data}]{c) Analysis of Source Data}\par
Labelled order data are provided by Dangdang customer service, and analyzing these data can verify BPCA at some level. When a hacker places a fraudulent order, the receiver name, receiver number and receiver address are different from the normally used information. Because hackers don't want to use real addresses, they may use some generic rough ones ending with 'county', 'block', 'corner' or 'street'. Six features that can distinguish fraudulent orders from normal ones therefore were identified.\par
We analyzed the six features using real data from Dangdang to determine their effectiveness in identifying fraudulent orders. The source data were from Dangdang's order data between January 1 to July 9 of 2014, in a total of 2075 fraudulent orders and 1513 stolen accounts. Taking the feature rough\textunderscore  addr as example, frequency in the table means that 71.1\% of all orders that have rough\textunderscore  addr are fraudulent orders. According to the results of these statistical tests, a basic idea of fraudulent orders emerges and this result will help in building a machine-learning model. These results also reveal some interesting facts. First is that rough\textunderscore  addr is a very identifiable feature from which to detect name, usually \textunderscore email all show some potential to detect fraudulent orders. Payment\textunderscore  ratio verifies that hackers just want to maximize their profit, which is the rule 3 of BPCA. These simple statistical results are useful at some level, to solve the fraudulent order detection problem and to further verify that BPCA, the machine learning algorithm is needed. fraudulent order. Usually\textunderscore  city, usually\textunderscore  tel, usually\textunderscore  d) Fraudulent order Detection Model using Logistic Regression Firstly, Choosing an appropriate algorithm. Logistic regression is a suitable algorithm for fraudulent order detection because it is not hard to apply, and the company's customer service staff can easily interpret the result.\par
Assuming some identification features as previously described. The identifying features and order data are input into the regression, and then the algorithm builds a model where each feature has a coefficient showing how much this feature can affect the result. The features with low coefficients (i.e. that don't significantly affect the results) are removed and the model is run again.\par
Formula 4-1 shows the result of the model. If the model returns a result of 1, then the order is fraud, less than that indicating otherwise.\par
Using identification features as x = (x\textunderscore 1,x\textunderscore 2,?,x\textunderscore p), logistic regression can be represented as Formula 4-2.Pf = P(Y=1|x) = ?(x) = 1/(1+e\textasciicircum (-g(x)) ) (4-2) Where g(x) = ?\textunderscore 0+?\textunderscore 1 x\textunderscore 1+?\textunderscore 2 x\textunderscore 2+?+?\textunderscore p x\textunderscore p\textbf{(1?p?n)}\par
Using maximum likelihood estimation, the coefficients ?0??1??p can be obtained. According to Formula 4-2, an interested party can calculate the possibility of one order being a fraudulent order. When the order is a fraudulent order, otherwise it may be innocent. The value of the threshold can be anywhere between 0 and 1.However, if the threshold is too low, the model would be unstable and if the threshold is too high, the recall rate would not be ideal. This paper chooses 0.75 as the threshold.\par
Second, conducting characteristic statistics and extraction. The most critical process to build the fraud detection model is to select identification features. The statistical information in Table  {\ref 4}.1 shows fraudulent orders always having features such as different receiver name, receiver address, receiver city, and receiver telephone number, which can ascertain fraudulent orders made by hackers going directly to their own addresses.\par
Features 1-14 in Table  {\ref 4}.2 are deduced by the concept "steady behavior" of BPCA. Receivers related information changes mean that this order might be a fraudulent order. Features 15-19 deduced by the concept "suspicion infection" of BPCA mean that if some receiver's phone numbers or receiver addresses have been complained about before, new orders that have the same receiver address and receiver phone number have the possibility of being fraudulent orders. Feature 20 is based on the statistical results shown in Table  {\ref 4}.1, if the receiver address is rough, there is a high possibility that the order is fraudulent order. Features 21-23 are based on "maximum rob" of BPCA, meaning that the hackers want to make the most profit possible out of the stolen account. These identification features are used to compose an identification features matrix, and then training data is used to train the matrix. Some of the identification features may not be effective in detecting fraudulent orders, and some ineffective features are eliminated during the training process.\par
The implementation of the logistic regression discussed above helps develop a fraudulent order detection model and test its effectiveness using the real order data of Dangdang as the experimental subject. 
\section[{a) Preparation and Preprocessing of Data}]{a) Preparation and Preprocessing of Data}\par
Preprocessing of data is a key problem in machine learning, because in most cases data is incomplete, noisy and incompatible. The result of a machine-learning algorithm depends greatly on the quality of data. Data preprocessing includes: data Because of the volume of data, a sample of the total order data has been used with a ratio of fraudulent orders versus normal orders of from 1:5 to 1:9.\par
Continuous numbers were assigned to discrete sections. For example, the total money was divided into sections [0,10), \hyperref[b9]{[10,} {\ref 50)}, [50,100), and >100. Discretization can be used when the focus is only on relative value instead of absolute value. The discretization formula used in this paper is ln (x+1)/ln2. Discretization is useful to describe nonlinear relationships and solve the hidden flaws in data \hyperref[b10]{[11]}. 
\section[{b) Process and Application of Model}]{b) Process and Application of Model}\par
Based on Formulae 4 -1 and 4 -2, R programming language was used to create a logistic regression model and then train the model to obtain coefficients.g(x) = ?\textunderscore 0+?\textunderscore 1 x\textunderscore 1+?\textunderscore 2 x\textunderscore 2+?+?\textunderscore p x\textunderscore p(1?p?23)\par
(5-1) After training, the coefficients, namely ?0??1???p can be figured out as shown in Table \hyperref[tab_5]{5}.1. cleaning, data integration, data conversion and data reduction \hyperref[b16]{[17]}.\par
*permid is used to identify customers. Whether or not the customer is logged in, Dangdang will save a permid on the device being used to browse Dangdang.  
\section[{V. Application and Results}]{V. Application and Results} 
\section[{payment\textunderscore ratio}]{payment\textunderscore ratio} 
\section[{c) Optimization and Second Training of Model}]{c) Optimization and Second Training of Model}\par
After first training, the coefficients of the first model were obtained. Then, based on the analysis of these results, 13 features were deleted and 1 new feature added. Features were deleted based on three rules: Rule 1: If one feature's coefficient is 2 magnitudes lower than the biggest coefficient, it should be deleted.\par
Rule 2: If one feature's coefficient is not logical, it should be deleted. Rule 3: If one feature is considered not logical after discussion with experts, it should be deleted. Features that have the pattern "*** \textunderscore cust\textunderscore dubious\textunderscore count" have low coefficients and after discussion it was determined that these features are not very logical, so they were deleted (see Table \hyperref[tab_5]{5}.2). A new feature, phone\textunderscore address was added. Phone\textunderscore address is a complex feature that can be calculated by this rule: if neither the receiver mobile number nor the address of one order have ever been used in this account, then phone\textunderscore address is the number of total history orders of this account, unless phone\textunderscore address is 0. This feature was added based on the reasoning that the more orders a user has bought, the lower the likelihood of them changing receiver address and mobile number at the same time. The final logistic model is shown in Table \hyperref[tab_5]{5}.3.  
\section[{C}]{C}\par
used in this account many times, the order is less\par
The coefficients of city\textunderscore frequency\textunderscore count and addr\textunderscore frequency\textunderscore count are negative, which means if the suspicious.\par
The coefficients of addr\textunderscore dubious\textunderscore count, email\textunderscore  dubious\textunderscore count,tel\textunderscore mobile\textunderscore dubious\textunderscore count,permid\textunderscore dubio receiver city and receiver address of an order have been us\textunderscore count and orderip\textunderscore dubious\textunderscore count are all positive. This result confirms the hypothesis of "suspicion infection": the new orders that have the same receiver address, mobile number and IP as previous fraudulent orders are considered suspicious.\par
The coefficient of rough\textunderscore address is positive, which means that an order with a rough address is suspicious because those committing fraud do not want to supply their address.\par
The coefficient of par\textunderscore rate is negative, which means that if the customer should pay extra money over the value of their account balance, the order is less suspicious.\par
The coefficient of whole\textunderscore price is positive, which means that fraudulent orders tend to be greater in total price. However, the absolute value of the coefficient is one of the lowest ones, so this tendency is not very important.\par
The coefficient of phone\textunderscore address is positive, which means that if one account has made many orders in history and now uses both a new receiver mobile number and a new address to make a new order, then the new order is suspicious. The results shown in table 5.3 verify the BPCA. 
\section[{d) Test and Performance of Model}]{d) Test and Performance of Model}\par
Using the values (x1, x2, x4?.) of the features of one order as input, the model calculated a possibility (Pf as shown in Formula 5-2) of this order being a Pf = P(Y=1|x) = ?(x) = 1/(1+e\textasciicircum (-g(x)) )\par
(5-2)g(x) = ?\textunderscore 0+ ?\textunderscore 1 x\textunderscore 1+?\textunderscore 2 x\textunderscore 2+?+?\textunderscore p x\textunderscore p (1?p?11)\par
The values of ?0??1?? ?p are shown in Table \hyperref[tab_5]{5}.3. X1, x2?xp are provided by Dangdang's data system.\par
Four categories were defined: FF (judged as fraud, in fact is fraud), FC (judged as fraud, in fact is clear), CF (judged as clear, in fact is fraud), and CC (judged as clear, in fact is clear). Human check number is the number of orders that should be checked by customer service officers, recall rate is the rate of fraudulent orders that can be detected by the system, the calculation of these two values are shown below. 
\section[{Human check number=FF+FC}]{Human check number=FF+FC}\par
(5-3) Recall rate=FF/(FF+CF)\par
(5-4) Two experiments were designed to test the effects of the fraud detection model in different conditions of time and situation.\par
Experiment 1 used Dangdang order data from 07/01/2014 to 08/31/2014 as the test data set. It detected 395 of a total of 417 fraudulent orders. The result is shown in Table \hyperref[tab_5]{5}.4 where it can be seen that the model works well in a situation where the rate of fraudulent orders is high. The fraudulent order rate in Experiment 2 is 0.000189, which was less than the rate 0.001201 of Experiment 1. It can be seen that the recall rate and human check number are close to that in Experiment 1. 
\section[{e) Performance of the Model}]{e) Performance of the Model}\par
The changes in numbers of fraudulent orders and amount of money stolen were analyzed to show the usefulness of the fraudulent order detection system. The anti-fraud system using this fraud detection model started to run on 06/24/2014. 2, it appears that after the system was implemented in 06/2014, the fraudulent order problem was controlled. Such a fraud detection system was not in place in Dangdang before, as shown in figure  {\ref 5}.1. Prior to April 2014 there were not many fraudulent orders found, because Dangdang had no way to control the situation. April 2014 could be a bench mark in China. That year a number of accounts were terribly leaked in China. Dangdang, as one of the biggest ecommerce entities, were attacked by hackers who used the leaked accounts  94\% of all fraudulent orders can be detected by this system. After the anti-fraud detection system was implemented, fewer complaints were received. Combining this system with human rechecking, both the number of fraudulent orders and the amount of money stolen were reduced. 
\section[{VI. Conclusions}]{VI. Conclusions}\par
We found that trading fraud in online ecommerce can be detected and prevented. Order fraud is a key problem of trading online and is very harmful to companies. By focusing on the determination of these online order frauds, the real situation and data of Dangdang was used with these two steps to follow. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue IV Version I  
\section[{( )}]{( )}\par
First, statistical analysis was carried out to determine the basic differences between fraudulent orders and normal ones, and then a logistic regression model was created. Finally, different experiments were designed to test the validity and effectiveness of the Fraudulent orders' Detection Model using twelve identifying features, which is the best model approved by the company in identification and prevention of fraud behaviors in ecommerce services. a) Pattern of Detecting and Preventing Online Seller and Customer Fraudulent orders First, statistical analysis of order data was carried out to provide a basic idea of the characteristics of fraudulent orders. Then, the features that can help in distinguishing fraudulent orders from normal ones were extracted and used to format a feature matrix. The feature matrix and logistic regression algorithm were used to build a fraudulent order detection model and carry out optimizations. Finally, the model's effectiveness was tested, and the model was used to detect real time orders and keep track of the performance of the model.\par
There would be no way to determine fraudulent orders without the implementation of this new developed model, which has been allowing enabled the company's customer service staff to successfully catch and free zesuspicious accounts. About 94\% of fraudulent orders were detected in Dangdang in the past year with the model. It helped the company reduce fraudulent orders and therefore could be instructive to and implemented by similar electronic commerce entities. 
\section[{b) Economical Significance of Detecting and Preventing Fraudulent order}]{b) Economical Significance of Detecting and Preventing Fraudulent order}\par
Reducing the number of fraudulent orders can benefit both customers and companies. First, fewer fraudulent orders means fewer customers losing money and more customers enjoying their shopping experience. Second, fewer fraudulent orders means that companies will receive fewer complaints and customer satisfaction will be higher. The public image of companies is improved with fewer fraudulent orders. 
\section[{VII. Future Work}]{VII. Future Work}\par
This report describes innovative research on the role of features of online orders and accounts in monitoring online transaction activities. This work resulted in a model that can detect patterns of fraud activity in online transactions, and judges the likelihood of each transaction being fraudulent. When the likelihood of a transaction being a fraud is high, human checks are still required to make a final judgment. Therefore, human resources are needed in identifying fraud. There are a variety of fraud activities, so different detection processes are required. Therefore, the process and model of this work can in future be adjusted to be used in more situations.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{51}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 5 . 1 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.29234592445328034\textwidth}P{0.48667992047713715\textwidth}P{0.0709741550695825\textwidth}}
item ID\tabcellsep Definition\tabcellsep Frequency\\
rough\textunderscore addr\tabcellsep Is the address rough?\tabcellsep 71.1\%\\
usually\textunderscore city\tabcellsep Is the receiver city usually used?\tabcellsep 42.5\%\\
usually\textunderscore tel\tabcellsep Is the receiver phone usually used?\tabcellsep 35.5\%\\
usually\textunderscore name\tabcellsep Is the receiver name usually used?\tabcellsep 34\%\\
usually\textunderscore email\tabcellsep Is the receiver email usually used?\tabcellsep 32.6\%\\
payment\textunderscore ratio,0.05\tabcellsep Pay for the order using extra money\tabcellsep 7.3\%\\
\tabcellsep instead of the money stored in the\tabcellsep \\
\tabcellsep account, the ratio of extra money is\tabcellsep \\
\tabcellsep more than 0.05\tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{51} \par 
\begin{longtable}{P{0.022180801491146318\textwidth}P{0.7493942218080148\textwidth}P{0.07842497670083877\textwidth}}
\tabcellsep : Coefficients of first training\tabcellsep \\
Index\tabcellsep X (Feature name)\tabcellsep ? (coefficient)\\
1\tabcellsep email\textunderscore dubious\textunderscore count\tabcellsep 6.990\\
2\tabcellsep name\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.619\\
3\tabcellsep rough\textunderscore address\tabcellsep 0.532\\
4\tabcellsep orderip\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.168\\
5\tabcellsep permid\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.166\\
6\tabcellsep addr\textunderscore dubious\textunderscore count\tabcellsep 0.075\\
7\tabcellsep tel\textunderscore mobile\textunderscore dubious\textunderscore count\tabcellsep 0.062\\
8\tabcellsep tel\textunderscore home\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.049\\
9\tabcellsep addr\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.003\\
10\tabcellsep whole\textunderscore price\tabcellsep 0.000\\
11\tabcellsep name\textunderscore frequency\textunderscore count\tabcellsep 0.000\\
12\tabcellsep email\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.000\\
13\tabcellsep Payment\tabcellsep -0.000\\
14\tabcellsep addr\textunderscore frequency\textunderscore count\tabcellsep -0.004\\
15\tabcellsep tel\textunderscore mobile\textunderscore frequency\textunderscore count\tabcellsep -0.006\\
16\tabcellsep tel\textunderscore home\textunderscore dubious\textunderscore count\tabcellsep -0.023\end{longtable} \par
 
\caption{\label{tab_3}Table 5 . 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.7473267326732673\textwidth}P{0.0648019801980198\textwidth}P{0.03787128712871287\textwidth}}
\multicolumn{2}{l}{2 : Features that were deleted and why}\tabcellsep \\
X (Feature name)\tabcellsep ?(coefficient)\tabcellsep Reason for deletion\\
Payment\tabcellsep -0.000\tabcellsep r1\\
addr\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.003\tabcellsep r3\\
email\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0\tabcellsep r3\\
name\textunderscore dubious\textunderscore count\tabcellsep -0.462\tabcellsep r2\\
name\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.619\tabcellsep r3\\
orderip\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.168\tabcellsep r3\\
permid\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.166\tabcellsep r3\\
tel\textunderscore home\textunderscore dubious\textunderscore count\tabcellsep -0.023\tabcellsep r1\\
tel\textunderscore home\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep 0.049\tabcellsep r3\\
tel\textunderscore mobile\textunderscore cust\textunderscore dubious\textunderscore count\tabcellsep -0.031\tabcellsep r3\\
name\textunderscore frequency\textunderscore count\tabcellsep 0\tabcellsep r1\\
tel\textunderscore home\textunderscore frequency\textunderscore count\tabcellsep -0.464\tabcellsep r3\\
tel\textunderscore mobile\textunderscore frequency\textunderscore count\tabcellsep -0.006\tabcellsep r1\end{longtable} \par
 
\caption{\label{tab_5}Table 5 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{53} \par 
\begin{longtable}{P{0.09642416769420468\textwidth}P{0.12681874229346485\textwidth}P{0.53557336621455\textwidth}P{0.09118372379778052\textwidth}}
Year 2016\tabcellsep \tabcellsep \tabcellsep \\
12\tabcellsep \tabcellsep \tabcellsep \\
Volume XVI Issue IV Version I\tabcellsep \tabcellsep \tabcellsep \\
( )\tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Computer Science and Technology\tabcellsep Sequence 1 2 3 4 5 6 7 8 9\tabcellsep X (Feature name) city\textunderscore frequency\textunderscore count addr\textunderscore dubious\textunderscore count email\textunderscore dubious\textunderscore count orderip\textunderscore dubious\textunderscore count phone\textunderscore address tel\textunderscore mobile\textunderscore dubious\textunderscore count whole\textunderscore price addr\textunderscore frequency\textunderscore count payment\textunderscore ratio\tabcellsep ?(coefficient) -0.405 0.305 2.680 0.561 0.887 0.993 0.338 -1.050 -0.200\\
\tabcellsep 10\tabcellsep Intercept\tabcellsep -1.395\\
\tabcellsep 11\tabcellsep permid\textunderscore dubious\textunderscore count\tabcellsep 0.605\\
\tabcellsep 12\tabcellsep rough\textunderscore address\tabcellsep 0.406\\
\tabcellsep \multicolumn{2}{l}{By analyzing the final model, BPCA is testified}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{and the following conclusions are reached.}\tabcellsep \end{longtable} \par
  {\small\itshape [Note: © 2016 Global Journals Inc. (US) 1]} 
\caption{\label{tab_6}Table 5 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{54} \par 
\begin{longtable}{P{0.6283742331288343\textwidth}P{0.07039877300613497\textwidth}P{0.08865030674846625\textwidth}P{0.06257668711656442\textwidth}}
Index\tabcellsep Value\tabcellsep Index\tabcellsep Value\\
Fraudulent order\tabcellsep 417\tabcellsep CF\tabcellsep 22\\
number\tabcellsep \tabcellsep \tabcellsep \\
Normal order\tabcellsep 346725\tabcellsep FC\tabcellsep 896\\
number\tabcellsep \tabcellsep \tabcellsep \\
Total order number\tabcellsep 347142\tabcellsep CC\tabcellsep 345829\\
Threshold\tabcellsep 0.75\tabcellsep Recall\tabcellsep 0.95\\
FF\tabcellsep 395\tabcellsep Human check\tabcellsep 1291\\
\tabcellsep \tabcellsep number\tabcellsep \\
\multicolumn{2}{l}{Experiment 2 used Dangdang order data from}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{10/07/2014 to 10/28/2014 as the test data set. The}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{model detected 48 of a total of 51 fraudulent orders. The}\tabcellsep \tabcellsep \\
result is shown in\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 5 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_8}Table 5 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{55} \par 
\begin{longtable}{P{0.34\textwidth}P{0.05483870967741935\textwidth}P{0.13709677419354838\textwidth}P{0.18645161290322582\textwidth}P{0.13161290322580646\textwidth}}
Index\tabcellsep \tabcellsep Value\tabcellsep Index\tabcellsep Value\\
Fraudulent\tabcellsep order\tabcellsep 51\tabcellsep CF\tabcellsep 3\\
number\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Normal\tabcellsep order\tabcellsep 270414\tabcellsep FC\tabcellsep 1443\\
number\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Total order number}\tabcellsep 270465\tabcellsep CC\tabcellsep 268971\\
Threshold\tabcellsep \tabcellsep 0.75\tabcellsep Recall\tabcellsep 0.94\\
FF\tabcellsep \tabcellsep 48\tabcellsep Human check\tabcellsep 1491\\
\tabcellsep \tabcellsep \tabcellsep number\tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 5 . 5 :}\end{figure}
 			\footnote{Anti-Fraud Schema System for Identification and Prevention of Fraud Behaviors in E-Commerce Services} 			\footnote{© 2016 Global Journals Inc. (US)} 		 		\backmatter   			 
\subsection[{Acknowledgements}]{Acknowledgements}\par
This research was supported by Dangdang (http://www.dangdang.com). Thanks to Fu Qiang, VP of Technology, Ju Qi, Technical director, and other staff of Dangdang for their support and help. Thanks to the technical help of Michael Wagner of Yale University Social Sciences Information Center. Thanks to Hu Saiquan of Yale University Economics and Management School, and Sam of Yale Computer Science School for discussion of research methods. Thanks to Ms. Arlene and BiBielefield, as well as Mr. Keven and Quan Wang for corrections to the paper. 			  			 
\subsection[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
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