A Layered Approach to Fraud Analytics for NFC-Enabled Mobile Payment System

Vishwakarma, Pinki Prakash; Tripathy, Amiya Kumar; Vemuru, Srikanth

doi:10.1007/978-3-319-72344-0_9

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…It calculates the likelihood that, the data item will be classified to a specific class. It is popular because of the probability based classification, which provides the base for the accuracy improvements in classification [22]. It is very useful when the data items to be classified have independent existence for their class and does not have any dependency with other objects of the same class.…”

Section: Naï Ve Bayesmentioning

confidence: 99%

Accuracy Based Fault Tolerant Two Phase - Intrusion Detection System (TP-IDS) Using Machine Learning and HDFS

Jadhav¹,

Pellakuri²

2021

RIA

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Section: Naï Ve Bayesmentioning

confidence: 99%

Accuracy Based Fault Tolerant Two Phase - Intrusion Detection System (TP-IDS) Using Machine Learning and HDFS

Jadhav¹,

Pellakuri²

2021

RIA

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“…Acquisition Processing: For most associations taking after a strict methodology on acquirement includes numerous means to minimize hazard and backing successful supplier administration. Virtual Intelligent Machine [8][10] can be depended upon to take after the procedure required as well as manage the numerous inquiries that are raised amid the procedure by purchasers and suppliers alike in an opportune way. Straightforwardness: It is a key and Virtual Intelligent Machine guarantees a full review trail of execution focuses and can be depended upon to guarantee solicitations are checked and handled in an auspicious way Expert Advisor for Field Engineers Those working in the oil and gas industry frequently confront perilous working conditions and should investigate hardware issues in under perfect conditions.…”

Section: Working Of Virtual Intelligent Machinementioning

confidence: 99%

“…Virtual Intelligent Machine is an artificial intelligence [8] platform created by Dube's managed IT services firm, a virtual agent [4] avatar poised to redefine how enterprises operate by automating and enhancing a wide range of business processes. The product of an obsessive and still-ongoing 16-year developmental cycle, everyone at speaks about Virtual Intelligent Machine using feminine pronouns-leverages cognitive technologies to interface with consumers and colleagues in astoundingly human terms, parsing questions, analyzing intent and even sensing emotions to resolve issues more efficiently and effectively than flesh-and-blood customer service representatives.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Artificial Intelligence Traveling with Virtual Intelligent Machine: A Review

2020

IJATCSE

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Today Artificial Intelligence is the sizzling topic which is effectively used for intelligent systems. As the population increasing day by day the mobile users are also increasing rapidly so there is problem for companies to receive the calls from customers, to overcome this problem some companies in Europe, New York has initiated the solution that can solve the problem to some extent and named it as an Virtual Intelligent Machine which can work as human and take the job in call center and receive the calls from customers. In this paper, we are going to discuss about the latest challenges, issues and its functional model of Virtual Intelligent Machine in today's scenario.

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“…Vishwakarma et al [15] propose an approach for fraud analytics for the NFC-enabled mobile payment system. A multi-layer solution is presented where each subsequent layer is responsible for separated parts of fraud analysis.…”

Section: Fraud Discovery Approachesmentioning

confidence: 99%

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

Diadiushkin

Sandkuhl

Maiatin

2019

J. Complex Syst. Inform. Model. Q.

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Financial industries are undergoing a digital transformation of their products, services, overall business models. Part of this digitalization in banking aims at automating most of the manual work in payment handling and integrating the workflows of involved service providers. The focus of the work presented in this paper is on fraud discovery and steps to fully automate it. Fraud discovery in financial transactions has become an important priority for banks. Fraud is increasing significantly with the expansion of modern technology and global communication, which results in substantial damages for the banks. Instant payment (IP) transactions cause new challenges for fraud detection due to the requirement of short processing time. The paper investigates the possibility to use artificial intelligence in IP fraud detection. The main contributions of our work are (a) an analysis of problem relevance from business and literature perspective, (b) a proposal for technological support for using AI in fraud detection of instant payment transactions, and (c) a feasibility study of selected fraud detection approaches.

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A Layered Approach to Fraud Analytics for NFC-Enabled Mobile Payment System

Cited by 7 publications

References 14 publications

Accuracy Based Fault Tolerant Two Phase - Intrusion Detection System (TP-IDS) Using Machine Learning and HDFS

Accuracy Based Fault Tolerant Two Phase - Intrusion Detection System (TP-IDS) Using Machine Learning and HDFS

The Impact of Artificial Intelligence Traveling with Virtual Intelligent Machine: A Review

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

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