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E-Banking Fraud Detection System Using Stacking method (On Ensemble Learning)

Mallikarjun H, Rishith C, Rakesh S V, Sandeep Kumar K, Vinay Varade Gowda K

Abstract


Due to rapid growth of technology we have almost every work  every field which can be done through internet. E-Commercials and stock trading, are some of the leading transaction platforms in this era. People use different mode of money transactions through Credit, Debit card and UPI’s. These mode of payments made the transaction simple and quicker . However, there are many ambiguity in the system of these transactions which cause the online frauds. Hence, Online transaction Fraud detection system is very important for all banking  networks to eliminate their loses.  The most commonly used detection strategies are Neural networks, K- Nearest Neighbor, Support Vector Machine, Fuzzy system, Call Trees, and other genetic algorithms. These algorithms are often used alone or meta learning techniques to make classifiers. This project presents detecting fraud using one of the Ensemble method called stacking in machine learning  to eliminate some of the limitations occurred in the above stated methods which evaluates every methodology supported bond criterion.

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References


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