Bhargava S

Work place: Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, Mysuru, Karnataka, India



Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computational Learning Theory, Computer Architecture and Organization, Data Structures and Algorithms


Bhargava S is currently working in Hewlett Packard Enterprise, Bengaluru as a Software Development Engineer. He graduated from SJCE in 2019. He worked with Dr. Anil Kumar for the conducting experiments and documented results for this research paper as a part of his interest in research in Machine Learning and Data analytics.

Author Articles
Detection of False Income Level Claims Using Machine Learning

By Anil Kumar K.M Bhargava S Apoorva R Jemal Abawajy

DOI:, Pub. Date: 8 Feb. 2022

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.

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