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  Published Paper Details:

  Paper Title

CrediGuard: An AI Driven Fraud Detection Solution

  Authors

  Rutvik Dnyanoba Patil,  Suraj Jotiram Shinde,  Prof. Tushar Waykole

  Keywords

credit card fraud, fraudulent activities, SVM (SUPPORT VECTOR MACHINE), Harr cascade Algorithm, Face Recognition.

  Abstract


In this day and age the Mastercard extortion is the greatest issue and presently there is need to battle against the Visa misrepresentation. "Visa extortion is the most common way of cleaning messy cash, accordingly making the wellspring of assets as of now not recognizable." On consistent schedule, the monetary exchanges are made on gigantic sum in worldwide market and subsequently identifying charge card misrepresentation movement is testing task. As prior (Against Mastercard extortion Suite) is acquainted with distinguish the dubious exercises yet it is relevant just on individual exchange not for other financial balance exchange. To Conquers issues of we propose AI technique utilizing 'Underlying Closeness', to recognize normal credits and conduct with other financial balance exchange. Location of charge card misrepresentation exchange from huge volume dataset is troublesome, so we propose case decrease strategies to lessens the information dataset and afterward find sets of exchange with other financial balance with normal ascribes and conduct.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTAF02030

  Paper ID - 261114

  Page Number(s) - 146-150

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Rutvik Dnyanoba Patil,  Suraj Jotiram Shinde,  Prof. Tushar Waykole,   "CrediGuard: An AI Driven Fraud Detection Solution", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.146-150, May 2024, Available at :http://www.ijcrt.org/papers/IJCRTAF02030.pdf

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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
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