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I need help with Literature review on a reserch paper. Requirement: "Write a 7-8 page literature review using the acmart latex class (https://www.sigplan.org/Resources/Author/) (use the acmsmall subformat). A literature review is a summary and analysis of a paper or research topic that gives a broad view, situating the paper/topic in its context. You should cite and discuss at least 8 other papers (in addition to your topic paper) you read that are related to your paper, at least three of which came before your topic paper and are cited by it, and at least three of which came after your topic paper and cite it. Expand on your explanation and analysis from your paper summary within this lit review, and motivate the problem your chosen paper addresses. I have attached the paper used.

Real-Time Credit-Card Fraud Detection using

Artificial Neural Network Tuned by Simulated

Annealing Algorithm

Azeem Ush Shan Khan, Nadeem Akhtar and Mohammad Naved Qureshi

Aligarh Muslim University, Department of Computer Engineering, Aligarh, India

Email: azeem5257@gmail.com

Aligarh Muslim University, Department of Computer Engineering, Aligarh, India

Email: {nadeemalakhtar, navedmohd786}@gmail.com

Abstract— Now-a-days, Internet has become an important part of human’s life, a person

can shop, invest, and perform all the banking task online. Almost, all the organizations have

their own website, where customer can perform all the task like shopping, they only have to

provide their credit card details. Online banking and e-commerce organizations have been

experiencing the increase in credit card transaction and other modes of on-line transaction.

Due to this credit card fraud becomes a very popular issue for credit card industry, it causes

many financial losses for customer and also for the organization. Many techniques like

Decision Tree, Neural Networks, Genetic Algorithm based on modern techniques like

Artificial Intelligence, Machine Learning, and Fuzzy Logic have been already developed for

credit card fraud detection. In this paper, an evolutionary Simulated Annealing algorithm is

used to train the Neural Networks for Credit Card fraud detection in real-time scenario.

This paper shows how this technique can be used for credit card fraud detection and

present all the detailed experimental results found when using this technique on real world

financial data (data are taken from UCI repository) to show the effectiveness of this

technique. The algorithm used in this paper are likely beneficial for the organizations and

for individual users in terms of cost and time efficiency. Still there are many cases which are

misclassified i.e. A genuine customer is classified as fraud customer or vise-versa.

Index Terms— Credit Card fraud detection, Simulated Annealing, Machine Learning,

Training, Classification, Artificial Neural Network (ANN), Activation Function.

I. INTRODUCTION

Credit card fraud is a kind of theft or unauthorized activity to make payment using credit card in an

electronic payment system as a fake source of fund. The purpose of credit card fraud is to obtain money or

make payment without owner permission. It involves illegal use of card or card information without the

owner permission though it is a criminal deception and banned by laws. Because of the advancement in

technology and software’s, users can hide their identity and locations while committing any transaction over

the web, which increases the fraud over the web. There are many methods of credit card fraud depicted in

“figure 1”, all are mentioned in details “see [1]”, are as follows:

1. First fraud is that user using its own credit card knowing that they have no money in his credit card and

DOI: 02.ITC.2014.5.65

© Association of Computer Electronics and Electrical Engineers, 2014

Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC

114

Fig 1: Types of Credit Card Fraud

bank has to pay by sending bill to the address.

2. Second fraud is done when submitting the application form to the bank for issuing the credit card with

the fake information.

3. Third fraud is done online when purchasing any item by submitting the card information of any credit

card without letting known to the owner.

4. Fourth fraud is the stealing of any credit card and using it by showing as an owner of this card until that

card become blocked by bank.

Credit card fraud affect the organization by financial losses and individual user also affected if the

information of credit card get steal. So it is important to find a solution which classifies a transaction into

fraud or non-fraud. Many techniques have been developed for credit card fraud detection like Artificial

Intelligence & Machine Learning and also based on locations [2]. In this paper, we focus on Machine

Learning technique, basically it provides a system which is supposed to classify a current transaction into

fraud or non-fraud.

In this paper, we are taking credit card fraud detection problem as a classification problem. Many

classification algorithm have been developed [3], but the most popular one is Decision Tree. This algorithm

is already been proposed for credit card fraud detection problem (“see [4]”). Basically there are two

technique for credit card fraud detection:

1. Supervised

2. Un-supervised

These are the machine learning techniques, in which the first one uses training data, to build the model,

which have all the attributes including class label i.e. it already contains the attribute which tells whether this

previous transaction is fraud or not. And in the second technique, training data does not contain the class

label i.e. this technique is class less. More study on these can be found in [5]. This paper propose a credit

card fraud detection technique using Neural Networks and Simulated Annealing algorithm to adjust the

weight of the neural network, Neural Network is a supervised machine learning technique. Many research

papers has already been proposed for fraud detection using Neural Networks and many researchers have been

uses Genetic Algorithm to adjust the weight of Neural Network in different fields [6]. This study of using

Simulated Annealing to train Neural Network is one of the first to use for credit card fraud detection on real

data set provided by UCI repository [7]. Basically, annealing is a process of heating and then cooling a solid

to change the hardness of the solid and simulated annealing is to emulate this process.

The main aim of this process is to build a training model on the basis of previous transactions, called training

data, for fraud detection. Once a learning of training model is complete, the model is capable of classifying

the unseen online transaction as fraudulent or non-fraudulent in real time [8] & [9].

II. ARTIFICIAL NEURAL NETWORK

Artificial neural network works in the same way as a human brain does, human brain consist of number of

neurons connected with each other, in the same way ANN consists of artificial neurons, called nodes in

network, connected with each other. The idea of Artificial Neural Network was presented in late 1943 by

Walter Pitts and Warren S.McCulloch as a data processing unit for classification or prediction problems

[10]. For the first time, Dorronsoro “et al.” in 1997 developed a system to detect credit card fraud by using

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Neural Network. Now-a-days, ANN have been successfully applied in business failure prediction, stock price

prediction, credit fraud detection and many more area.

ANN comes in many forms like Recurrent NN, Associative NN, etc. In this paper, we will discuss FeedForward Neural Network which will trained by Simulated Annealing method.

“Figure 2” depict a simple multi-layer feed forward neural network. It consist of an Input layer, an output

layer and an Hidden Layer, hidden layer depends on the problem we are going to solve, it can be no or more

than one hidden layer. The number of neurons in input layer corresponds to the number of input attributes in

the training dataset which we will see later in this paper and the number of neurons in output layer is depend

on the type of problem you are going to solve, in credit card fraud detection case we have two output one is

fraud and the other one is non-fraud i.e., 0 and 1 respectively.

In the Feed-forward neural network, as we can see in “fig. 2”, there is no feedback loop. Each of the neurons

in each layer is connected with each other without making any loop and the link between these neurons has

weights, represented by Wij. The connection between each neuron do not perform any calculation but is used

to store the weights. These weights are initialized with some random values and changes at every iteration in

training process. The simple neuron in each layer are often called perceptron is the simplest neuron network.

Fig 2: Simple Feed-Forward Neural Network

A feed-forward perceptron works by sending the input to the neurons and send to the output neuron after

processing. This is a simple neuron, i.e. perceptron, “figure 3” which has three input and two output.

Fig 3: Simple Feed-Forward Perceptron

All the perceptron in the Neural Network have two functions i.e. Input and Activation Function. As the name

suggest, the input function collects all the input and perform summation function on the input and then

transfer the result to the activation function. An activation function perform some operation on the result after

summation and then transfer to the next level. Let’s take an example, in the above figure we have three

inputs, let’s say, I0, I1, I2 and two output Z1, Z2 and their corresponding weights. Now, input function will

perform the summation on the inputs multiplied by their corresponding weights. Let’s say the output of input

function is S.

The result of this summation function is then pass to activation function. Activation function scale the value

of S in proper range. Common activation function are sigmoid activation function which works on threshold,

if the value of S exceeded the threshold value then the node pass output.

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There are two activation function which is commonly used in Neural Networks, Sigmoid and Hyperbolic

Tangent Activation Function. It depends on the training dataset on which we are going to train the network

that which activation function is good.

The “figure 4” shows sigmoid activation function graph, which refers to one of the case of logistic function.

It works for real input values and it only returns positive value (“refer [11]”). The formula of sigmoid

function is:

The Hyperbolic Tangent activation function (TANH) is the next version of sigmoid function because it

produces both negative as well as positive values as shown in “figure 5”.

Fig 4: Sigmoid Activation Function Graph Fig 5: Hyperbolic Tangent Activation Function Graph

The equation of hyperbolic tangent function is given by:

In this project, we have use both of the above activation function and the result of fraud detection is better

with TANH.

III. SIMULATED ANNEALING

Basically Annealing is a thermodynamics process, it is a heat treatment process upon any metal to change the

structure of the metal. It involves heating of any metal slightly above its critical temperature and then cooling

it down slowly. It makes the metal harder or stronger and makes the structure of the metal homogenous. The

emulation of the process of annealing is called Simulated Annealing.

This method was developed by adapting some changes in Metropolis-Hastings algorithm, also known as

Monte Carlo method, invented by M.N. Rosenbluth and published in a paper in 1953 [12]. This method was

developed by Scott Kirkpatrick, C. Daniel Gelatt and Mario P. Vecchi in 1983 [13], and later on by Vlado

Cerny in 1985 [14]. Corana “et al.” (1987) and Goffe (1994) had proposed some changes which was suitable

to train discrete-valued weights. In this study, the implementation of simulated annealing is based on these

algorithms, which is adjusted to find the best configuration of weights in artificial neural network. The basic

procedure are as follows:

1. Heat the system at high temperature T and generate a random solution.

2. As the algorithm progress, T decreases at each iteration and each iteration forms a nearby model.

3. Then cool the system slowly until the minimum value of T is reached and generate a model at each

iteration, which takes the system towards global minima.

In each iteration, a solution is generated which is compared with current solution, by using acceptance

function, if it is better than current solution than it get replaced by this solution. The terminology and

definitions used in Simulated Annealing is defined in [15] and [16]. These definitions are used in this paper

to train the neural network for fraud detection. The main definitions which is needed for this algorithm are:

(1) a method is to generate initial solution, by generating worst solution at the beginning helps to avoid

converging to local minimum, (2) a Perturbation Function to find a next solution with whom the current

solution is compared, (3) an Objective Function is to be defined to evaluate and rate the current solution on

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the basis of performance, (4) an Acceptance Function, which is used to check whether the current solution is

good or not in comparison with the current one, a very basic one is exp((currentSol-nextSol)/currentTemp),

(5) and the last one is stopping criteria, there are many stopping criteria’s, in this paper we have used an

threshold value of objective function as an stopping criteria.

IV. TRAINING OF ANN

ANN is made up of connection between neurons in each layer and links connecting these neurons has some

weights on it, so the adjustment of weights to learn the relationship between the input and the given output

i.e. label, is called learning or training of neural network. The most popular training algorithm is

BackPropogation which was given by Salchenberger “et al.” in 1992. The main problem of this algorithm is

that it gets stuck in local minima and the error still remains the same. An evolutionary algorithm, Simulated

Annealing and Genetic Algorithm, was given to solve this problem of local minima, among these algorithm

simulated annealing is preferred because it takes less time in comparison with genetic algorithm.

As we know, to perform training of ANN we should have millions of data but to train neural network for

credit card fraud detection we don’t have much amount of data of previous transaction to perform training

upon. In this paper, we have used Simulated Annealing for training and it gives very good result in

comparison with genetic and backpropogation, which we will see later.

Fig 6: ANN training model

“Figure 6” depicts a basic model for the training process of ANN. In this paper we have used supervised

learning [17], so our data consist of both input and desired output. A random weight is generated for each

connection and output is calculated based on current weight & input. Obviously, in the initial states the

desired output is different from current output which can be calculate by using any error function like Mean

Squared Error (MSE) or Sum Squared Error (SSE). Now, according to the training algorithm the weights are

adjusted and repeat these steps again until some threshold value for error function will reached.

In 1988, Jonathan Engel publish a paper [18] in which he had explained the training process for feed-forward

NN using simulated annealing, we have used this paper to implement the simulated annealing algorithm for

training purpose, while a brief description of algorithm is given below in this paper(for pseudo code refer

[19]). There are the series of different steps which simulated annealing algorithm has to follow at each cycle.

A cycle is completed when it follows all the steps shown in the “fig. 7” and randomized the weights at each

cycle. The ‘n’ number of cycle is fixed by the programmer, at each iteration it will perform n cycles and after

one iteration is completed, the current temperature gets lowered and checked against the minimum allowed

lowest temperature, if it is not less than the threshold value then again a cycle of randomization is repeated.

The method used in this paper for temperature reduction is based on start and stop temperatures. Its equation

is given by:

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NewTemp= Ratio * currentTemp

The ratio causes the new temperature lies in between start and stop temperature, ratio is calculated at each

cycle and it decreases the temperature logarithmically. Its equation is given by:

The values of start and stop temperature is decided by hit and trial method, you have to check the result by

putting different values and compare to find the best one. The high temperature will cause more

randomization in weights.

Fig 7: Simulated Annealing method for training ANN

The main part in the training process of an ANN is the randomization of weights, simulated annealing uses

previous input values and current temperature to randomize the weights, it depends on the type of problem

we are going to solve with trained neural network. In this paper, we have used TANH as an activation

function so we have to normalize the input, output and their corresponding weights in between (-1,1) and a

weight matrix is created which acts as a linear array of double data type. The randomization of weights is not

a complex task, in this paper we have generated a random number and multiply it with current temperature.

Q = currentTemp * Random (N)

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Then this number is multiplied by each value in the weight matrix Wij*Q and updates all the values. This

task is performed in each ‘n’ cycles and the updated matrix is compared with the previous one, if it is better

than previous then the weight matrix gets updated.

V. RESULTS

In this section, we present the result which we get by using this algorithm on real world dataset. The dataset

is taken from UCI repository consist of 1000 instance. This dataset contains useful information about

transaction. This dataset contains 20 attributes and values corresponding to each rows is converted into

symbolic or numerical form because of some privacy agreement. But the types of attributes is mentioned,

shown in the table I.

TABLE I: ATTRIBUTES USED FOR TRAINING AND EVALUATING THE NEURAL NETWORK

In this paper, we have divided the dataset into two parts: 75% data is taken from dataset for the training

purpose and 25% data is taken for the evaluation of trained neural network. So, the evaluation dataset is new

for the trained neural network, by performing the evaluation task we can classify the unseen data as a fraud or

non-fraud. In this paper we present the result by showing the percentage of correct & incorrect classified data

by comparing the predicted label with the existing label.

The configuration of Neural Network and the list of parameter used in Simulated Annealing to train neural

network and their respective values is shown in table 2 & 3 respectively.

TABLE II. PARAMETERS OF AN ARTIFICIAL NEURAL NETWORK

TABLE III. PARAMETERS OF SIMULATED ANNEALING ALGORITHM TO TRAIN NEURAL NETWORK

After running this program it takes almost two days and stop training after reaching 1% error. After the

process of training is, it goes for the evaluation process. This project is based on real-time i.e. at the time

when performing any transaction at any online portal, so the time taken to classify any unseen data should be

S. No Attribute Name

1 Status of existing checking Account

2 Duration in month

3 History of credit taken

4 Purpose of transaction

5 Credit amount

6 Saving account/bonds

7 Present employment since

8 Instalment rate in %

9 Personal status & sex

10 Guarantors

11 Present address since

12 Property

13 Age

14 Other instalment plans

15 Housing

16 Existing credits at this bank

17 Job

18 Number of people liable to provide maintenance for

19 Telephone

20 Foreign worker(Yes or No)

Input layer Neurons Hidden layer Neurons Output layer neurons Activation Function

20 50 2 Hyperbolic Tangent Activation

Function

Start Temperature Stop temperature Number of Cycles/Iteration

100 3 100

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less. We have implemented this algorithm in java and perform an evaluation on the 25% of the dataset. It

classifies all the data i.e. 250 instances, within 5-10 seconds, which is good in comparison with different

configuration of neural network.

TABLE IV. RESULT OF TRAINED NEURAL NETWORK WHEN EVALUATION DATASET IS APPLIED

Table IV display the result of the trained neural network using simulated annealing. As we can see, in the

first row the total case is only 250 instances from which 224 data is correctly classified i.e. based on the predefined label, the label predicted by the trained network is correct.

VI. CONCLUSION

In this paper we showed that better result is achieved with ANN when trained with simulated annealing

algorithm. As the result shows that the training time is high but the fraud detection in real time is

considerably low and the probability of predicting the fraud case correctly in online transaction is high,

which is a main measure to evaluate any ANN. In the table 3 we can see that 65% of total fraud case is

correctly classified which is a very high percentage in comparison with genetic, resilient backpropogation

and any other training algorithm.

The main problem in credit card fraud detection is the availability of real world data for the experiment. This

approach can also be used in other applications which require classification task [20] e.g. software failure

prediction, etc.

A. Future Work in this project

There will be a lot of work to be done for fraud detection because the activity of user is different in each

transaction which causes the training of any ANN to be difficult. In this project the main task is to find the

best configuration for neural network, we can use Genetic Algorithm for this task, it would find a better

configuration by applying different combinations. So if we combine Simulated Annealing and Genetic

Algorithm to create a best model, it will gives better result than any other.

REFERENCES

[1] Linda Delamaire (UK), Hussein Abdou (UK), John Pointon (UK),”Credit card fraud and detection techniques: a

review”, Banks and Bank Systems, Volume 4, Issue 2, 2009.

[2] Nadeem Akhtar, Farid ul Haq, “ Real Time Online Banking Fraud Detection Using Loaction

Information”,International Conference on Computational Intelligence and Information Technology – CIIT

2011, Pune, India.

[3] K. Cios, W. Pedrycs, and R. Swiniarski, Data Mining Methods for Knowledge Discovery. Boston: Kluwer

Academic Publishers, 1998.

[4] Y. Sahin and E. Duman,”Detecting Credit Card Fraud by Decision Trees and Support Vector Machines”,

International conference of Engineers & computer Scientists 2011 Vol I, March 16 2011, Hong Kong.

[5] Bolton, R. J. and Hand, D. J.,”Statistical fraud Detection: A review”. Statistical Science 28(3):235-255, 2002.

[6] Karl BlomStorm,” Benchmarking an artificial neural network tuned by a genetic algorithm”, VT 2012.

[7] UCI Machine Learning Repository,”http://archive.ics.uci.edu/ml/datasets.html”, last accessed at 22/11/2013.

[8] Wai-cgiu Wong, Ada Wai-chee Fu,”Incremental Document Clustering for Web Page Classification”, Department

of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Springer Japan 2002.

[9] W. Wong and A. Fu, “Incremental Document Clustering for Web Page Classification,” Proc. 2000 Int’l Conf.

Information Soc. in the 21st Century: Emerging Technologies and New Challenges (IS2000), 2000.

[10] F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain”,

Psychological review, 65(6):386, 1958.

[11] Han, Jun; Morag, Claudio,” The influence of the sigmoid function parameters on the speed of Backpropagation

learning", In Mira, José, Sandoval, Francisco, From Natural to Artificial Neural Computation. pp. 195–201, 1995.

Observed Total Correct Percentage

Correct

Total Case 250 224 89.6%

Fraud Case 173 159 92%

Non-Fraud Case 77 65 85%

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[12] Metropolis, Nicholas, Rosenbluth, Arianna W., Rosenbluth, Marshall N., Teller, Augusta H., Teller, Edward,

"Equation of State Calculations by Fast Computing Machines", The Journal of Chemical Physics 21 (6): 1087,

1953.

[13] Kirkpatrick, S., Gelatt Jr, C. D., Vecchi, M. P., "Optimization by Simulated Annealing". Science 220 (4598): 671–

680, 1983.

[14] Cerny, V., "Thermo dynamical approach to the traveling salesman problem: An efficient simulation algorithm",

Journal of Optimization Theory and Applications 45: 41–51, 1985.

[15] P J van Laarhoven and E H Aarts,”Simulated Annealing: Theory and Applications”, Kluwer Academic Publishers,

1987.

[16] R H Otten and L P Ginneken,”The Annealing Algoritm”, Kluwer Academic Publishers, 1989.

[17] Y. Yang, J. Carbonell, R. Brown, T. Pierce, B. Archibald, and X. Liu, “Learning Approaches for Detecting and

Tracking News Events,” IEEE Intelligent Systems, vol. 14, no. 4, pp. 32-43, 1999.

[18] Jonathan Engel,” Teaching Feed-Forward Neural Networks by Simulated Annealing”, Norman Bridge Laboratory

of Pllysics 161-33, California Institute of Technology, Pasadena, CA 91125, USA Complex, Systems 2, 1988.

[19] Mohamed Benaddy and Mohamed Wakrim,”Simulated Annealing Neural Network for Software Failure

Prediction”, International Journal of Software Engineering and Its Applications Vol.6, No. 4, October, 2012.

[20] Li, Y.H., Jain, A.K.: Classification of Text Documents. The Computer Journal. vol. 41, pp. 537--546 (1998).

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