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              <text>AI Based Technologies for Digital and Banking Fraud During Covid-19</text>
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          <name>Subject</name>
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              <text>Artificial intelligence; COVID 19; Digital frauds; Digital transactions; Pandemic</text>
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              <text>The only viral thing today is the Covid 19 virus, which has severely disrupted all the economic activity around globe because of which all the businesses are experiencing irrespective of its domain or country of origin. One such major paradigm shift is contactless business, which has increased digital transaction. This in turn has given hackers and fraudsters a lot of space to perform digital scams line phishing, spurious links, malware downloads etc. These frauds have become undesirable part of increased digital transactions, which needs immediate attention and eradication from the system with instant results. In this pandemic situation where, social distancing is key to restrict the spread of the virus, digital payments are the safest and most appropriate payment method, and it needs to be safe and secure for both the parties. Artificial intelligence can be a saviour in this situation, which can help combat the digital frauds. The present study will focus on the different kinds of frauds which customers and facing, and most possible ways Artificial intelligence can be incorporated to identify and eliminate such kind of frauds to make digital payments more secure. Findings of the study suggest that inclusion of AI did bring a change in the business environment. AI used for entertainment has become an essential part in business. Transfiguration from process to platform focused business. The primary requirement of AI is to study the customer experience and how to give a better response for improving the satisfaction. But recently AIs are used not only for customer support, but its been observed that businesses have taken it as marketing strategy to increase demand and sales.  2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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              <text>Sinha M.; Chacko E.; Makhija P.</text>
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              <text>Studies in Computational Intelligence, Vol-1038, pp. 443-459.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2022-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-030-99079-4_17" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-030-99079-4_17&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131828509&amp;amp;doi=10.1007%2F978-3-030-99079-4_17&amp;amp;partnerID=40&amp;amp;md5=9cbc602c17710f39812a9fe753545b1a" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131828509&amp;amp;doi=10.1007%2f978-3-030-99079-4_17&amp;amp;partnerID=40&amp;amp;md5=9cbc602c17710f39812a9fe753545b1a&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 1860949X</text>
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              <text>Online</text>
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              <text>Sinha M., CHRIST University, Bangalore, India; Chacko E., Jain (Deemed to be) University, Bangalore, India; Makhija P., Jain (Deemed to be) University, Bangalore, India</text>
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