Financial Fraud Detection Mechanisms to Overcome Trust Issues within Trade Segments
Amit Chhabra1, Radhika2

1Radhika, Department of Computer Science & Technology, Guru Nanak Dev University, Amritsar, India.
2Amit Chhabra, Assistant Professor, Department of computer Science & Technology, Guru Nanak Dev University, Amritsar, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 28, 2020. | Manuscript published on May 10, 2020. | PP: 663-668 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5651059720/2020©BEIESP | DOI: 10.35940/ijitee.G5651.059720
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Abstract: Frauds in modern era are cause of concern in almost every field of life. Credit card, money laundering and bank frauds are common and technology has to play important part in overcoming this issue. This paper provides insight into financial frauds leading from malicious users in trading network. To this end several techniques are researched over. To start with price based fraud detection is discussed and then similarity matrix, linear binary patterns, support vector machine and random forest in the field of fraud detection are elaborated. This paper highlights pros and cons of each of such techniques. Dataset required determining classification accuracy of these approaches is synthetically driven. Execution time while determining frauds is critical entity and similarity matrix approach is fast and accurate as compared to random forest, support vector and linear binary patterns. Parameters: Classification Accuracy, Execution time Implementation tool: Matlab 2018 Achievement: support vector machine results are closer to similarity matrix based approach in terms of classification accuracy but execution time of similarity based approach is much less and hence this algorithm is considered better in determining financial frauds. 
Keywords: Financial Frauds, LBP, RF, SM, classification accuracy, execution time
Scope of the Article: classification