A Predictive Analytics with Machine Learning Algorithms for Real Time Financial Data

Fraud has come a trillion-bone assiduity moment. Certain fiscal institutions have devoted brigades of sphere experts and data scientists that are assigned with detecting fraudulent exertion. To find frauds, data scientists constantly employ intricate statistical models. But there are a lot of downsides to this strategy. Since fraud discovery isn't real- time, fraudulent exertion are constantly discovered only after the factual fraud has taken place. These methodologies are prone to mortal crimes. In addition, it requires precious, largely professed sphere expert brigades and data scientists. nonetheless, the delicacy of homemade fraud discovery methodologies is low and due to that, it's veritably delicate to handle large volumes of data. More frequently, it requires time- consuming examinations into the other deals related to the fraudulent exertion in order to identify fraudulent exertion patterns.Fraud has come a trillion-bone assiduity moment. Certain fiscal institutions have devoted brigades of sphere experts and data scientists that are assigned with detecting fraudulent exertion. To find frauds, data scientists constantly employ intricate statistical models. But there are a lot of downsides to this strategy. Since fraud discovery isn't real- time, fraudulent exertion are constantly discovered only after the factual fraud has taken place. These methodologies are prone to mortal crimes. In addition, it requires precious, largely professed sphere expert brigades and data scientists. nonetheless, the delicacy of homemade fraud discovery methodologies is low and due to that, it's veritably delicate to handle large volumes of data. More frequently, it requires time- consuming examinations into the other deals related to the fraudulent exertion in order to identify fraudulent exertion patterns.

  • Research Type: Policy Research
  • Paper Type: Report Paper
  • Vol.7 , Issue 1 , Pages: 21 - 24, Jan 2025
  • Published on: 21 Jan, 2025
  • Issue Type: Regular
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  • Cite Score
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    100

  • No. of authors
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    75

  • No. of Downloads
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    43

  • Cite Score
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    100

  • No. of authors
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    75

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    43

About Authors:
Lakshmiprasanna
India
Viswam Engineering College

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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: Lakshmiprasanna, lakshmiprasanna0026@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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