Credit Card Fraud Detection Performance Improvement using Advanced Super Gradient Boosting Algorithm
V. Sudheer Goud1, P. Premchand2

1V. Sudheer Goud, Professor, Department of Computer Science in Holy Mary Institute of Technology and Science (HITS), (V) Bogaram, (M) Keesara, Medchal .Dist, Telangana, India.
2P. Premchand, Professor, Department of Computer Science and Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana State, India
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 179-184 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3457049620/2020©BEIESP | DOI: 10.35940/ijitee.F3457.049620
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Abstract: Credit card fraud introduces to the physical loss of a credit card or the destruction of sensitive credit card data. Several text mining procedures can be used for disclosure. This investigation reveals several algorithms that can be used to analyze transactions as a fraud or as a real background. This paper represents the possibility of fraudulent transactions in the prevalence and meaning of credit card usage also, Credit card fraud data collection was used in the investigation. Since the dataset was largely unbalanced, SMOTE (Synthetic Minority oversampling Technique) is applying for an overdose. In addition, jobs selected, and the data set divided into two parts, training data and test data. In this paper, The Advanced Super Gradient Boostingbased Text mining Algorithm (ASGB) suggested to detect the fraud transaction in Credit card transactions. ASGB is a Decision-Tree-Based Ensemble Text mining algorithm that utilizes a gradient boosting framework. In forecast difficulties, including unstructured data (Images, Text, etc.), artificial neural networks tend to exceed all other algorithms or structures. The proposed algorithms used in the experiment were the Hidden Markov Model, Random Forest, Gradient Boosting, and Enhanced Hidden Markov Model. The Experimental Results show that proposed algorithms, a well tuned ASGB classifier outperforms all of them. And it presents better Precision is 99.1%, and Recall is 99.8%, F-measure is 99.5%. 
Keywords: Credit card fraud detection, Text Mining, SMOTE, HMM, GB, Random Forest, and ASGB
Scope of the Article: VLSI Algorithms