Fraud Detection Techniques Using Data Mining – A Review

Reorganized Collected Literature

The enhancement of the technology have been helping in maintaining a proper data transfer over the network. Identity crime has been one if the major fraud and theft cases in the organizations.  Fraud cases have been increasing all over the world due to the enhancement of technology.The negative use of the technology have been creating chances for hackers to breach into various networks for taking data form it (Odetola et al. 2018).  The data theft have been increasing over the network. Cloning has been another problem faced by the organizations and users.

These acts have been performed in credit cards and passports cloning. Data and information regarding credit card credentials and fake passports have been created in the market. Fraud behavior of any person need to be detected in the organizations. Fraud has been an intentional theft act performed by any person in an organization or in the society. It has been referred to abuse of profit of an organization in legal platform.

1.1.1 Reorganized Collected Literature

Research Problem	Sub-problem Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Collected Literature
Skimming	Financial fraud	Agrawal, S. and Agrawal, J., 2015. survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, pp.708-713. Save, P., Tiwarekar, P., Jain, K.N. and Mahyavanshi, N., 2017. A novel idea for credit card fraud detection using decision tree. International Journal of Computer Applications, 161(13).
	Application fraud	Tran, P.H., Tran, K.P., Huong, T.T., Heuchenne, C., HienTran, P. and Le, T.M.H., 2018, February. Real Time Data-Driven Approaches for Credit Card Fraud Detection. In Proceedings of the 2018 International Conference on E-Business and Applications (pp. 6-9). ACM.
	…	Mahmoudi, N. and Duman, E., 2015. Detecting credit card fraud by modified Fisher discriminant analysis. Expert Systems with Applications, 42(5), pp.2510-2516.
Cloning	Identity theft	Prabakaran, S. and Mitra, S., 2018, April. Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning. In Journal of Physics: Conference Series (Vol. 1000, No. 1, p. 012046). IOP Publishing.
	…

1.2 Types of Methodology

1.2.1 Qualitative and Quantitative Methodologies

Quantitative and Qualitative strategies are the two kinds of procedures in the research. Qualitative research systems centers on the nature of things and parts of information. Qualitative research help in investigating associations with the segments of research. Qualitative research centers on the secondary methodologies of the information accumulation (Agrawal and Agrawal 2015). Information gathering should be finished utilizing the secondary sources including on the web diaries, articles and books (Zissis and Lekkas 2012).

The sample size of the Qualitative research should be high to accumulate more pertinent data with respect to the research theme. The example should be enormous that hemps in guaranteeing consideration of all discernments incorporated into the research theme. The utilization of online review helps in gathering enormous measure of information and data with respect to the subject (Jokar, Arianpoo and Leung 2016). Irregular examining strategy has been utilized for choosing taking part for the study in quantitative methodology. Measurable strategies are utilized in investigating quantitative information in a research.

1.2.2 Type of Chosen Methodology

In this research, the researcher has chosen qualitative research approach. The qualitative research approach manages the secondary wellsprings of information and data identified with the fraud detection techniques using data mining process (Save,  Tiwarekar Jain and Mahyavanshi 2017). The utilization of qualitative research strategies have been helping in keeping up a sharp way to deal with the points of interest of the fraud detections in organizations. The qualitative methodology has helped in gathering information from online diaries and articles.

Research Problem

1.3 Review of the Existing Methodologies 

1.3.1 Selected Sub-Problem

The research has focused on the identity theft using cloning method.  Information started from Qualitative data has been kept up and checked appropriately. The legitimacy and dependability factors in information accumulation should be finished. However, this compose information are taken from distributed journals and articles. Qualitative research helps in getting top to bottom points of interest of the research theme including fraud detection (Jain 2017). The validity of data collected has been properly maintained. Data has been collected from secondary sources including books, journals and articles. Journals have been taken from 2012 published journals. The view of the members have been thought about appropriately. This sort of methodology causes in comprehensive way to deal with the segments of fraud detection.

1.3.2 Analysis of the Selected Methodologies

We should take a plain : “DOTNETSPIDER IS A LEADING  FOR DOT NET COMMUNITY” and we need to scramble the plain  utilizing “Caesar Cipher”, utilizing key/secret word as “9”. Caesar Cipher just takes numeric secret key, and just between 0-25.

So our plain’s figure/encoded  will progress toward becoming: “MXCWNCBYRMNA RB J UNJMRWP FNKBRCN OXA MXC WNC LXVVDWRCH”.

So how could we land at the figure/scrambled ?

Presently take a word from the plain , for instance: “DOTNETSPIDER”

It is a kind of supplant figure in which each letter in the plaintext is supplanted by a letter with a settled position isolated by a numerical esteem utilized as a “key”.

So we need to take each letter or character,In instance of “DOTNETSPIDER”: the estimation of ‘D’ is 3, so include 3 by 9 (since we have utilized ‘9’ as our key/secret phrase)

Along these lines, 3 + 9 = 12

Presently allude to the table above, now whose esteem is 12, its ‘M’.

So here „D? gets supplanted by „M?

Let?s take another incentive from “DOTNETSPIDER” ? „T?: the estimation of „T? is 19, so include 19 by 9 (since we have utilized „9? as our key/secret word)

Along these lines, 19 + 9 = 28,

Here you can see the esteem isn’t on the rundown.

So at whatever point the esteem is more prominent than „25?, simply subtract the incentive with „26?.

Along these lines, 19 + 9 = 28, and now the esteem is more prominent than 25, in this way, 28 – 26 = 2.

Presently allude to the table above, now whose esteem is 2, its „C?. So here „T? gets supplanted by „C?

Sub-problem

Additionally,

O (14) + 9 = 23(X)

T (19) + 9 = 28 – 26 = 2(C)

N (13) + 9 = 22(W)

E (4) + 9 = 13(N)

T (19) + 9 = 28 – 26 = 2(C)

1.3.3 Relevance of the Research Problem

The research refers to the methodology used in the fraud detection by data mining process. Data has been collected form qualitative method and data text have been taken from various online journals and books. The research has been relevant and reliable to its objectives and aim (Tran et al. 2018). The research has focused on fraud and theft detection using data mining techniques. Major problems of the data mining has been the theft and fraud cases. Fraud can be of different forms including bribery, securities fraud, theft, mortgage and scams.

Online data theft has been one of the major problems over the internet. Fraud detection techniques have been used in the methodology for ensuring security (Hines and Youssef 2018). The validity of data collected has been properly maintained. Data has been collected from secondary sources including books, journals and articles. Journals have been taken from 2012 published journals. The view of the members have been thought about appropriately. All the journals have its proper author name and publishing house.  This verifies the reliability and validity of data and information collected from various sources.

1.3.4 Summary of the Reviewed Methodologies

Literature #	Research Problem	Methodology	Your Sub-problem	Relevance
Literature1	Skimming	Qualitative	Financial Fraud	Valid
Literature2	Cloning	Qualitative	Identity theft	Valid

1.4 Proposed Methodology 

1.4.1 Identification and Justification

Quantitative research manages the amount of the information assembled. Quantitative research strategy manages crude information gathered from members. By and large, online overviews are best to gather quantitative information and data with respect to the research theme (Ristov, Gusev and Kostoska 2012). The research has been focused on the fraud and theft detection. The use of various journals and articles have been helping in collecting in the data and information. The fraud and theft detection have been a major problems for the organization in the market.  The use of the data mining techniques have been helping in providing solutions to the fraud cases (Prabakaran and Mitra 2018).

Data and information has been helping in performing analysis method of the fraud detection techniques.  Data has been collected from secondary sources including books, journals and articles. Journals have been taken from 2012 published journals. This has been a great factor in order to maintain a keen approach in the fraud detection techniques. Various research journals and articles have been used in order to collect data and information.   

Collected Literature

1.4.2 Benefits and Limitations

There are different advantages of utilizing a Qualitative strategies and methodologies. This methodology helps in giving a detailed data about the research point. Qualitative methodology helps in characterizing relationship among data preparing with execution. It helps in giving a more profound methodology about the remarks of the research that aides in keeping up sharp way to deal with the improvement of the theory of the research (Zhou et al. 2018).

Fraud detection techniques have been used in the methodology for ensuring security. All the journals have its proper author name and publishing house.  This verifies the reliability and validity of data and information collected from various sources. Qualitative research concedes about finding members and experience to pick up learning with respect to look into theme (Xiao and Xiao 2013). It has an adaptable structure that may help in keeping up guide way to deal with information gathering from interviews and online sources.

Past these favorable circumstances, there are some e constraints of the Qualitative methodology. This methodology does not give data of some logical sensitive and spotlights on encounters (Gray, and Debreceny 2014). Nonetheless, different researchers have less need to the Qualitative methodologies because of low believability (Luna et al. 2012). The example size of the Qualitative methodology is little contrast with quantitative methodology. Consequently, the legitimacy of the information has been constantly flawed.

1.4.3 Framework and Explanation

The above figure describes about a framework of the fraud and theft detection technique. This figure contains eight components including attacker. The attacker directly attacks the encryption by substitution method that can be controlled by enhancing security in encrypting technology. After that, data is transferred n the data mining table where proper allocation of the data has been done (Dutta, Gupta and Narayan 2017). The data is secured by allocating keys including private and public keys. After allocating keys, data is transferred to the receiver end. The data is decrypted in the received end with the help of private and public keys.  This help in maintaining a secured approached to the data transfer in the network. After decrypting, data is received at the receiver end.

List of References

Agrawal, S. and Agrawal, J., 2015. Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, pp.708-713.

Ahmad, T., Chen, H., Wang, J. and Guo, Y., 2017. Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renewable and Sustainable Energy Reviews.

Skimming

Baesens, B., Van Vlasselaer, V. and Verbeke, W., 2015. Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection. John Wiley & Sons.

Dutta, S., Gupta, A.K. and Narayan, N., 2017, October. Identity Crime Detection Using Data Mining. In Computational Intelligence and Networks (CINE), 2017 3rd International Conference on (pp. 1-5). IEEE.

Gray, G.L. and Debreceny, R.S., 2014. A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), pp.357-380.

Hines, C. and Youssef, A., 2018, April. Machine Learning Applied to Rotating Check Fraud Detection. In Data Intelligence and Security (ICDIS), 2018 1st International Conference on (pp. 32-35). IEEE.

Jain, V., 2017. Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining. International Journal of Information Technology, 9(3), pp.303-310.

Jeong, S.H., Kim, H., Shin, Y., Lee, T. and Kim, H.K., 2015. A Survey of Fraud Detection Research based on Transaction Analysis and Data Mining Technique. Journal of the Korea Institute of Information Security and Cryptology, 25(6), pp.1525-1540.

Jokar, P., Arianpoo, N. and Leung, V.C., 2016. Electricity Theft Detection in AMI Using Customers’ Consumption Patterns. IEEE Trans. Smart Grid, 7(1), pp.216-226.

Mahmoudi, N. and Duman, E., 2015. Detecting credit card fraud by modified Fisher discriminant analysis. Expert Systems with Applications, 42(5), pp.2510-2516.

Masud, M., Thuraisingham, B. and Khan, L., 2016. Data mining tools for malware detection. Auerbach Publications.

Odetola, T.A., Mohammed, H., Hasan, S.R. and Eberle, W., 2018. Anomaly Detection In IoT Devices Using Data Mining Techniques. Proceedings of Student Research and Creative Inquiry Day, 2.

Pal, D. and Pal, S., 2018. Fraud Detection in Health Insurance Domain: A Big Data Application with Data Mining Approach. Journal of Innovation and Research Vol, 1(1).

Prabakaran, S. and Mitra, S., 2018, April. Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning. In Journal of Physics: Conference Series(Vol. 1000, No. 1, p. 012046). IOP Publishing.

Prajapati, U., Sangal, N. and Patole, D., 2016. Fraud Website Detection using Data Mining. International Journal of Computer Applications, 141(3).

Save, P., Tiwarekar, P., Jain, K.N. and Mahyavanshi, N., 2017. A novel idea for credit card fraud detection using decision tree. International Journal of Computer Applications, 161(13).

Tran, P.H., Tran, K.P., Huong, T.T., Heuchenne, C., HienTran, P. and Le, T.M.H., 2018, February. Real Time Data-Driven Approaches for Credit Card Fraud Detection. In Proceedings of the 2018 International Conference on E-Business and Applications (pp. 6-9). ACM.

Viegas, J.L., Esteves, P.R., Melício, R., Mendes, V.M.F. and Vieira, S.M., 2017. Solutions for detection of non-technical losses in the electricity grid: a review. Renewable and Sustainable Energy Reviews, 80, pp.1256-1268.

Zhou, X., Cheng, S., Zhu, M., Guo, C., Zhou, S., Xu, P., Xue, Z. and Zhang, W., 2018. A state of the art survey of data mining-based fraud detection and credit scoring. In MATEC Web of Conferences (Vol. 189, p. 03002). EDP Sciences.