CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING? A. Project Overview: Create a proposal for a machine learning project by doin

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 71

Available Online at www.ijcsmc.com

International Journal of Computer Science and Mobile Computing

A Monthly Journal of Computer Science and Information Technology

ISSN 2320–088X IMPACT FACTOR: 7.056

IJCSMC, Vol. 10, Issue. 4, April 2021, pg.71 – 79

CREDIT CARD FRAUD DETECTION

USING MACHINE LEARNING

Mr. Thirunavukkarasu.M 1 ; Achutha Nimisha

2 ; Adusumilli Jyothsna

3

1 Assistant Professor, Dept. of CSE, SCSVMV (Deemed to be University), Kanchipuram, TamilNadu, India

([email protected]) 2 Student, Dept. of CSE, SCSVMV (Deemed to be University), Kanchipuram, TamilNadu, India ([email protected])

3 Student, Dept. of CSE, SCSVMV (Deemed to be University), Kanchipuram, TamilNadu, India ([email protected])

DOI: 10.47760/ijcsmc.2021.v10i04.011

———————————————————————***——————————————————————— Abstract- This Project is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are

drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to

trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this

proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of

the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming

difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of

technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology; it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit

card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a

random forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided

current data-set. Then augment the accuracy of the result data. Proceeded with the application of processing of some of the

attributes provided which can find affected fraud detection in viewing the graphical model of data visualization. The

performance of the techniques is gauged based on accuracy, sensitivity, and specificity, precision. The results is indicated

concerning the best accuracy for Random Forest are unit 98.6% respectively.

Keywords— Random forest algorithm, Criminal transactions, Credit card

1. INTRODUCTION

Nowadays Credit card usage has been drastically increased across the world, now people believe in

going cashless and are completely dependent on online transactions. The credit card has made the digital

transaction easier and more accessible. A huge number of dollars of loss are caused every year by the

criminal credit card transactions. Fraud is as old as mankind itself and can take an unlimited variety of

different forms. The PwC global economic crime survey of 2017 suggests that approximately 48% of

organizations experienced economic crime. Therefore, there’s positively a necessity to unravel the matter of credit card fraud detection. Moreover, the growth of new technologies provides supplementary ways in

which criminals may commit a scam. The use of credit cards is predominant in modern day society and

credit card fraud has been kept on increasing in recent years. Huge Financial losses have been fraudulent

effects on not only merchants and banks but also the individual person who are using the credits. Fraud

may also affect the reputation and image of a merchant causing non-financial losses that. For example, if a

cardholder is a victim of fraud with a certain company, he may no longer trust their business and choose a

competitor. Fraud Detection is the process of monitoring the transaction behavior of a cardholder to

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 72

detect whether an incoming transaction is authentic and authorized or not otherwise it will be detected as

illicit. In a planned system, we are applying the random forest algorithm for classifying the credit card

dataset. Random Forest is an associate in the nursing algorithmic program for classification and regression.

Hence, it is a collection of decision tree classifiers. The random forest has an advantage over the decision

tree as it corrects the habit of over fitting to their training set. A subset of the training set is sampled

randomly so that to train each individual tree and then a decision tree is built, each node then splits on a

feature designated from a random subset of the complete feature set. Even for large data sets with many

features and data instances, training is extremely fast in the random forest and because each tree is

trained independently of the others. The Random Forest algorithm has been found to provide a good

estimate of the generalization error and to be resistant to overfitting.

1.1 ADVANTAGES

 Random Forest selects the best feature rather than the most important feature among a random subset of data resulting in a better model.

 Thus having a binary classification of fraud i.e. positive case (value 1) and non-fraud i.e. negative case (value 0) for the target category in the transaction amount.

There are various fraudulent activities detection techniques has implemented in credit card transactions

have been kept in researcher minds to methods to develop models based on artificial intelligence, data

mining, fuzzy logic and machine learning. Credit card fraud detection is a very troublesome, but also a

popular problem to solve. In our proposed system we built the credit card fraud detection using Machine

learning.

With the advancement of machine learning techniques. Machine learning has been recognized as a no-hit

live for fraud detection. A great deal of data is transferred throughout on-line transaction processes,

resulting in a binary result: genuine or fraudulent. Online businesses are able to identify fraudulent

transactions accurately because they receive chargebacks on them. Within the sample fraudulent datasets,

features are constructed. These area unit information points like the age and price of the client account, as

well as the origin of the credit card. There are many options and everyone contributes, to varying extents,

towards the fraud probability. Note, the degree within which every feature contributes to the fraud score

isn't determined by a fraud analyst, but is generated by the artificial intelligence of the machine which is

driven by the training set. So, in regard to the card fraud, if the use of cards to commit fraud is proven to

be high, the fraud weighting of a transaction that uses a credit card will be equally so. However, if this were

to diminish, the contribution level would parallel. Simply put, these models self-learn while not express

programming like with manual review. Credit card fraud detection using Machine learning is done by

deploying the classification and regression algorithms. We use a supervised learning algorithm such as

Random forest algorithm to classify the fraud card transaction online or by offline. Random fore st is an

advanced version of the Decision tree. The random forest has better efficiency and accuracy than the other

machine learning algorithms. Random forest aims to reduce the previously mentioned correlation issue by

choosing only a subsample of the feature space at each split. Essentially, it aims to make the trees de-

correlated and prune the trees by setting a stopping criterion for node splits

1.2 SCOPE OF THE PROPOSED WORK

In this proposed project we designed a protocol or a model to detect the fraud activity in credit card

transactions. This system is capable of providing most of the essential features required to detect

fraudulent and legitimate transactions. As technology changes, it becomes difficult to track the Modeling

and pattern of fraudulent transactions. With the rise of machine learning, artificial intelligence and other

relevant fields of information technology, it becomes feasible to automate this process and to save some

of the intensive amount of labour that is put into detecting credit card fraud.

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 73

2. SOFTWARE AND HARDWARE REQUIREMENT

2.1 Hardware

 OS – Windows 7, 8 and 10 (32 and 64 bit)  RAM – 4GB

2.2 Software

 Python  Anaconda

3. SYSTEM ARCHITECTURE

Fig 1 System Architecture

4. LITERATURE SURVEY

Fraudulent Detection in Credit Card System Using SVM & Decision Tree (Vijayshree B. Nipane, Poonam S.

Kalinge, Dipali Vidhate, Kunal War, Bhagyashree P. Deshpande): With growing advancement in the

electronic commerce field, fraud is spreading all over the world, causing major financial losses. In the

current scenario, Major cause of financial losses is credit card fraud; it not only affects tradesperson but

also individual clients. Decision tree, Genetic algorithm, Metalearning strategy, neural network, HMM are

the presented methods used to detect credit card frauds. In contemplating system for fraudulent

detection, artificial intelligence concept of Support Vector Machine (SVM) & decision tree is being used to

solve the problem. Thus by the implementation of this hybrid approach, financial losses can be reduced to

greater extent.

Machine Learning Based Approach to Financial Fraud Detection Process in Mobile Payment System

(Dahee Choi and Kyungho Lee): Mobile payment fraud is the unauthorized use of mobile transaction

through identity theft or credit card stealing to fraudulently obtain money. Mobile payment fraud is a fast –

growing issue through the emergence of smartphone and online transition services. In the real world, a

highly accurate process in mobile payment fraud detection is needed since financial fraud causes financial

loss. Therefore, our approach proposed the overall process of detecting mobile payment fraud based on

machine learning, supervised and unsupervised method to detect fraud and process large amounts of

financial data. Moreover, our approach performed sampling process and feature selection process for fast

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 74

processing with large volumes of transaction data and to achieve high accuracy in mobile payment

detection. F-measure and ROC curve are used to validate our proposed model. 5. PURPOSE OF THE

PROJECT We propose a Machine learning model to detect fraudulent credit card activities in online

financial transactions. Analyzing fake transactions manually is impracticable due to vast amounts of data

and its complexity. However, adequately given informative features, could make it is possible using

Machine Learning. This hypothesis will be explored in the project. To classify fraudulent and legitimate

credit card transaction by supervised learning Algorithm such as Random forest. To help us to get

awareness about the fraudulent and without loss of any financially.

5. PURPOSE OF THE PROJECT

We propose a Machine learning model to detect fraudulent credit card activities in online financial

transactions. Analyzing fake transactions manually is impracticable due to vast amounts of data and its

complexity. However, adequately given informative features, could make it is possible using Machine

Learning. This hypothesis will be explored in the project.

To classify fraudulent and legitimate credit card transaction by supervised learning Algorithm such as

Random forest. To help us to get awareness about the fraudulent and without loss of any financially.

5.1 PACKAGES

Which are being used for data exploration, pro processing and for using random forest algorithm are:

 NumPy: For simple arrays.  Pandas: For reading the file.  SciKit: Learn- for pre-processing.  Matplotlib or Seaborn: For plotting and representing confusion matrix colour format.  Tensor flow: For matrix format.

6. MODULES

 Data collection  Data pre-processing  Feature extraction  Evaluation model

6.1 Data Collection:

Data used in this paper is a set of product reviews collected from credit card transactions records. This

step is concerned with selecting the subset of all available data that you will be working with. ML problems

start with data preferably, lots of data (examples or observations) for which you already know the target

answer. Data for which you already know the target answer is called labelled data.

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 75

Fig. 2: Importing python packages for data exploration, preprocessing and for using random

6.2 Data Pre-processing

Pre-processing is the process of three important and common steps as follows:

 Formatting: It is the process of putting the data in a legitimate way that it would be suitable to work with. Format of the data files should be formatted according to the need. Most recommended format is

.csv files.

 Cleaning: Data cleaning is a very important procedure in the path of data science as it constitutes the major part of the work. It includes removing missing data and complexity with naming category and so on.

For most of the data scientists, Data Cleaning continues of 80% of work.

 Sampling: This is the technique of analyzing the subsets from whole large datasets, which could provide a better result and help in understanding the behavior and pattern of data in an integrated way

6.3 Data Exploration

Fig. 3: Data exploration

6.3.1 Pre-processing with python commands

STEP 1:

Fig. 4: Pre-processing

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 76

STEP2:

Fig. 5: Preprocessing Step 2

STEP 3: Acquired trained and testing dataset from the large dataset

Fig. 6: Training and testing data

Fig. 7: Process of training and testing data extraction

6.4 Data visualization

Data Visualisation is the method of representing the data in a graphical and pictorial way, data

scientists depict a story by the results they derive from analysing and visualising the data. The best tool

used is Tableau which has many features to play around with data and fetch wonderful results.

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 77

6.5 Feature extraction

Feature extraction is the process of studying the behavior and pattern of the analyzed data and

draw the features for further testing and training. Finally, our models are trained using the Classifier

algorithm. We use classify module on Natural Language Toolkit library on Python. We use the labelled

dataset gathered. The rest of our labelled data will be used to evaluate the models. Some machine learning

algorithms were used to classify pre-processed data. The chosen classifiers were Random forest. These

algorithms are very popular in text classification tasks.

6.6 Evaluation model

Model Evaluation is an essential part of the model development process. It helps to find the best model

that represents our data and how well the selected model will work in the future. Evaluating model

performance with the data used for training is not acceptable in data science because it can effortlessly

generate overoptimistically and over fitted models. To avoid overfitting, evaluation methods such as hold

out and cross-validations are used to test to evaluate model performance. The result will be in the

visualized form. Representation of classified data in the form of graphs. Accuracy is well-defined as the

proportion of precise predictions for the test data. It can be calculated easily by mathematical calculation

i.e. dividing the number of correct predictions by the number of total predictions.

7. ALGORITHM

7.1 Random Forest

Random forest is a supervised machine learning algorithm based on ensemble learning. Ensemble

learning is an algorithm where the predictions are derived by assembling or bagging different models or

similar model multiple times. The random forest algorithm works in a similar way and uses multiple

algorithm i.e. multiple decision trees, resulting in a forest of trees, hence the name "Random Forest". The

random forest algorithm can be used for both regression and classification tasks.

7.1.1 Advantages of using random forest

 The random forest algorithm is not biased and depends on multiple trees where each tree is trained separately based on the data, therefore biasedness is reduced overall.

 It’s a very stable algorithm. Even if a new data point is introduced in the dataset it doesn’t affect the overall algorithm rather affect the only a single tree.

 It works well when one has both categorical and numerical features.  The random forest algorithm also works well when data possess missing values, or when it’s not been scaled properly. Thus, using this Random forest algorithm and decision trees algorithm we have extracted

the accurate percentage of detection of fraud from the given dataset by studying its behavior. A confusion

matrix is basically a summary of prediction results or a table which is used to describe the performance of

the classifier on a set of test data where true values are known. It provides visualization of an algorithm’s performance and allows easy identification of classes. Thus, resulting in the computing of most

performance measures by giving insights not only the errors being made by the classification model but

also tells the type of errors being made. Trained Data and Testing Data is represented in a confusion matrix

which portrays:

 TP: True Positive which denotes the real data where customers are subjected to fraud and are used for training and were accurately predicted.

 TN: True Negative denotes the data which was not predicted and doesn’t match with the data which was subjected to the fraud.

 FP: False Positive is predicted but there is no possibility of the data to be subjected to the fraud.  FN: False Negative is not predicted but there is an actual possibility of the data who is subjected to fraud. Comprehensive 2D/3D plotting

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 78

Fig. 8 Confusion matrix for testing dataset

Fig. 9: Confusion matrix for testing dataset

Fig. 10: Accurate result extracted from the random forest classification and regression model using

decision trees

8. CONCLUSION

Hence, we have acquired the result of an accurate value of credit card fraud detection i.e.

0.9994802867383512 (99.93%) using a random forest algorithm with new enhancements. In comparison to

existing modules, this proposed module is applicable for the larger dataset and provides more accurate

results. The Random forest algorithm will provide better performance with many training data, but speed

during testing and application will still suffer. Usage of more pre-processing techniques would also assist.

Thirunavukkarasu.M et al, International Journal of Computer Science and Mobile Computing, Vol.10 Issue.4, April- 2021, pg. 71-79

© 2021, IJCSMC All Rights Reserved 79

Our future work will try to represent this into a software application and provide a solution for credit card

fraud using the new technologies like Machine Learning, Artificial Intelligence and Deep Learning.

REFERENCES [1]. https://towardsdatascience.com/the-random-forestalgorithm-d457d499ffcd [2]. https://www.xoriant.com/blog/productengineering/decision-trees-machine-learningalgorithm.html [3]. Gupta, Shalini, and R. Johari. ”A New Framework for Credit Card Transactions Involvin g Mutual Authentication

between Cardholder and Merchant.” International Conference on Communication Systems and Network Technologies IEEE, 2021:22-26.

[4]. Y. Gmbh and K. G. Co, “Global online payment methods: the Full year 2020,” Tech. Rep., 3 2020. [5]. Bolton, Richard J., and J. H. David. “Unsupervised Profiling Methodsfor Fraud Detection.” Proc Credit Scoring and

Credit Control VII (2020): 5– 7. [6]. Drummond, C., and Holte, R. C. (2019). C4.5, class imbalance, and cost sensitivity: why under-sampling beats

oversampling. Proc of the ICML Workshop on Learning from Imbalanced Datasets II, 1–8. [7]. Quah, J. T. S., and Sriganesh, M. (2020). Real-time credit card fraud detection using computational intelligence. Expert

Systems with Applications, 35(4), 1721-1732.