International Journal of Modern Education and Computer Science (IJMECS)
ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)
Published By: MECS Press
IJMECS Vol.14, No.1, Feb. 2022
Detection of False Income Level Claims Using Machine Learning
Full Text (PDF, 602KB), PP.65-77
Data driven social security fraud detection has been given limited attention in research. Recently, social schemes have seen significant expansion across many developing countries including India. The fundamental aims of social schemes are to alleviate poverty, enhance the quality of life of the most vulnerable and offer greater chances to those relegated to the fringe of society to engage more enthusiastically in the society. Although governments channel billions of dollars every year in support of these social schemes, quite significant number of the eligible people are excluded from the program mainly through fraud and dishonesty. Although fraud is considered an illegal offence and morally reprehensible, it is unfortunate that the prevalence of fraud in social benefit schemes is rampant and a significant challenge to address. In this paper, we studied the viability of machine learning techniques in identifying fraudulent transactions in the context of social schemes. We focus on the detection of the false income level claims made by the fake beneficiaries to get the privileges of government scheme. We used the standard classifiers like Logistic Regression, Decision Trees, Random Forests, Support Vector Machine (SVM), Multi-Layer Perceptron and Naïve Bayes to identify fake beneficiaries of the government scheme from those deserving people. The results show that the Random Forest Classifier perform best providing an accuracy of 99.3% with F1 score of 0.99. The outcome of this research can be used by the government agencies entrusted with the management of the schemes to wade out the abusers and provide the required benefits to the right and deserving recipients.
Cite This Paper
Anil Kumar K.M, Bhargava S, Apoorva R, Jemal Abawajy, "Detection of False Income Level Claims Using Machine Learning", International Journal of Modern Education and Computer Science(IJMECS), Vol.14, No.1, pp. 65-77, 2022.DOI: 10.5815/ijmecs.2022.01.06
Ghosh, S. and Reilly, D. L. 1994. Credit card fraud detection with a neural network in Proceedings of the27th Annual Hawaii International Conference on System Science vol.3.
Minegishi, Tatsuya & Niimi, Ayahiko. (2013). Proposal of Credit Card Fraudulent Use Detection by Online-type Decision Tree Construction and Verification of Generality. International Journal for Information Security Research.
G. R. Faulhaber,O Design of service systems with priority reservation O in Conf. Rec. 1995 IEEE Int. Conf. Communications, pp. 38.
Sherly K.K," A Comparative Assessment of Supervised Data Mining Techniques for Fraud Prevention", TIST. Int. J. Sci. Tech. Res., Vol.1 (2012), 1-6.
Varre Perantalu, Bhargav Kiran-"Credit card Fraud Detection using Predictive Modeling: a Review".
O. S. Yee, S. Sagadevan, N. Hashimah, A. Hassain, Credit Card Fraud Detection Using Machine Learning As Data Mining Technique, vol.10,no. 1, pp.23-27.
Sahil Dhankhad, et al., Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study", IEEE International information Reuse and Integration or Data Science, pp122-125,2018
Mohammed J. Zaki , Wagner Meira Jr, Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, New York, NY, 2014
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning ,The MIT Press,2016
J.O. Awoyemi, A. O. Adetunmbi, S. A. Oluwadare, "Credit card fraud detection using machine learning techniques: A comparative analysis", 2017 International Conference on Computer Networking and Informatics (ICCNI), pp.1-9, 2017.
Planning Commission. TPDS Definition. url: https://www.gktoday.in/gk/targeted- public-distribution networks
CH Shah. Programme Evaluation Organisation, Planning Commission" Evaluation Report on First Year'sWorking of Community Projects"(Book Review)". In Indian Journal of Agricultural Economics 9.2 (1954), p.54
Travis Oliphant, NumPy: A guide to Numpy, USA: Trelgol Publishing.
Scikit-learn: Machine Learning in Python, Pedregosaetal, JMLR12, pp.2825-2830, 2011.
J.D.Hunter,"Matplotlib: A2D Graphics Environment", Computing in Science & Engineering, vol.9, no.3, pp.90-95, 2007.
Clifton Phua, Vincent C.S.Lee, Kate Smith Miles and Ross W. Gayler, "A Comprehensive Survey of Data Mining-based Fraud Detection Research"', CoRR, abs/1009.6119, 2010.
S.Bhattacharyya, S.Jha, K.Tharakunnel and J.C.Westland, Data mining for credit card fraud: A comparative study,"Decis. Support Syst., vol.50,no.3, pp.602-613,2011.
K.Randhawa, C.K.Loo, M.Seera, C.P.Lim, and A.K.Nandi, Credit Card Fraud Detection Using Ada Boost and Majority Voting, "IEEE Access,vol.6,pp.14277- 14284,2018
M.Seera, C.P.Lim, K.S.Tan, and W.S.Liew, Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks, "Neurocomputing, vol.249, pp.337-344
Adrian and Banarescu, "Detecting and Preventing Fraud with Data Analytics", Emerging Markets Queries in Finance and Business 2014, EMQFB2014, 24-25October 2014, Bucharest, Romania.
Lynnette Purda and David Skillicorn, "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection", contemporary research, volume32, issue3, Pages: 815-1318, 2015.
EWT Ngai, Y Hu, YH Wong, Y Chen, X Sun - Decision support systems, "The application of data mining techniques in financial fraud detection: A classification frame- work and an academic review of literature", Decision support systems, 2011-Elsevier Volume50, Issue3, February 2011, Pages 559-569.
Zhou H, Chai H. & Qiu M. Frontiers Information Technologies Electronic Engg. (2018)19:1537
Jurgovsky J, Granitzer M, Ziegler K, et al., 2018. Sequence classification for credit-card fraud detection. Expert Syst Appl, 100:234-245.
Veronique Van Vlasselaer, Cristian Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu Monique Snoeck and Bart Baesens, "APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions", Decision SupportSystemsVolume75,July2015,Pages 38
Johannes Jurgovsky, Michael Granitzer, Konstantin Ziegler, Sylvie Calabretto, Pierre-Edouard Portier, Liyun He-Guelton, and Olivier Caelen, "Sequence classification for credit-card fraud detection", Expert Systems with Applications Volume 100, 15 June 2018, Pages 234-245.
Shantanu Rajora, Dong-Lin Li, , Chandan Jha, Neha Bharill, Om Prakash Patel, Sudhanshu Joshi, DeepakPuthal and Mukesh Prasad, "A Comparative Study of Machine Learning Techniques for Credit Card FraudDetection Based on Time Variance", IEEE Symposium Series on Computational Intelligence (SSCI), 2018,Pages1958-1963.
Y. Bouzembrak, B. Steen, R. Neslo, J. Linge, V. Mojtahed and H.J.P. Marvin, Development of food fraud media monitoring system based on textmining", Food Control, Volume 93, November 2018, Pages 283-296
Hans J.P. Marvin, Esmee M. Janssen, Yamine Bouzembrak, Peter J.M. Hendriksen, and Martijn Staats, Big data in food safety: An overview", Critical Reviews In Food Science And Nutrition 2017, Vol.57, No.11, 2286-2295.
Maja Puh and Ljiljana Brkić, “Detecting Credit Card Fraud Using Selected Machine Learning Algorithms “International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 20-24 May 2019, DOI:10.23919/MIPRO.2019.8757212, Opatija, Croatia.
Vaishnavi Nath Dornadula and S Geetha, “Credit Card Fraud Detection using Machine Learning Algorithms”, International Conference on Recent Trends in Advanced Computing 2019, India, 2019
Doaa Hassan, "The Impact of False Negative Cost on the Performance of Cost Sensitive Learning: A Case Study in Detecting Fraudulent Transactions", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.2, pp.18-24, 2017. DOI: 10.5815/ijisa.2017.02.03
Hamza O. Salami, Ruqayyah S. Ibrahim, Mohammed O. Yahaya,"Detecting Anomalies in Students' Results Using Decision Trees", International Journal of Modern Education and Computer Science (IJMECS), Vol.8, No.7, pp.31-40, 2016.DOI: 10.5815/ijmecs.2016.07.04
Sandeepkumar hegde, Monica R Mundada, "Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector", International Journal of Intelligent Systems and Applications (IJISA), Vol.11, No.7, pp.10-19, 2019. DOI: 10.5815/ijisa.2019.07.02
Ch.Suresh, K.Thammi Reddy, N. Sweta,"A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.5, pp.37-43, 2016. DOI: 10.5815/ijitcs.2016.05.04
Sunil Kappal, "Deplyoing Advance Data Analytics Techniques with Conversational Analytics Outputs for Fraud Detection", International Journal of Mathematical Sciences and Computing (IJMSC), Vol.5, No.1, Pp.42-52, 2019.DOI: 10.5815/ijmsc.2019.01.04