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DESCRIPTION:With the new trend of Online Shopping and Online Platforms for 
 transactions\, the number of Credit Card based transactions increased trem
 endously. However\, there have been a lot of cases where illegal use of De
 bit/Credit Cards for making Fraudulent Transactions. Credit card companies
  have been paying a lot of attention to providing the best service for the
 ir customers by having process enhancements and pro-actively looking into 
 transactions before making them through. Global financial losses related t
 o payment cards are estimated to reach $34.66 billion in 2022\, according 
 to The Nilson Report\, a newsletter that tracks the payment industry. Rela
 ted to the negative impacts of credit card fraud activities\, and financia
 l and product losses\, it’s easy for merchants and users to feel victimi
 zed and helpless. Machine Learning Models can work well in detecting such 
 Fraudulent actions when they are trained on a large quantity of historical
  data and then fine-tuned depending on validation and evaluation metrics.\
 n\nSpeaker(s): Priyanka Singh\, \n\nToronto\, Ontario\, Canada\, Virtual: 
 https://events.vtools.ieee.org/m/312336
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o
 rg/m/312336
ORGANIZER:reza.dibaj@ieee.org
SEQUENCE:1
SUMMARY:Credit Card Fraud Detection – Students Research in ML and DL at D
 urham College
URL;VALUE=URI:https://events.vtools.ieee.org/m/312336
X-ALT-DESC:Description: <br /><p>With the new trend of Online Shopping and 
 Online Platforms for transactions\, the number of Credit Card based transa
 ctions increased tremendously. However\, there have been a lot of cases wh
 ere illegal use of Debit/Credit Cards for making Fraudulent Transactions. 
 Credit card companies have been paying a lot of attention to providing the
  best service for their customers by having process enhancements and pro-a
 ctively looking into transactions before making them through. Global finan
 cial losses related to payment cards are estimated to reach $34.66 billion
  in 2022\, according to The Nilson Report\, a newsletter that tracks the p
 ayment industry. Related to the negative impacts of credit card fraud acti
 vities\, and financial and product losses\, it&rsquo\;s easy for merchants
  and users to feel victimized and helpless. Machine Learning Models can wo
 rk well in detecting such Fraudulent actions when they are trained on a la
 rge quantity of historical data and then fine-tuned depending on validatio
 n and evaluation metrics.</p>
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