Fraud detection system: A survey
The increment of computer technology use and the continued growth of companies have
enabled most financial transactions to be performed through the electronic commerce …
enabled most financial transactions to be performed through the electronic commerce …
How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark
The core goal of this paper is to identify guidance on how the research community can better
transition their research into payment card fraud detection towards a transformation away …
transition their research into payment card fraud detection towards a transformation away …
Credit card fraud detection in card-not-present transactions: Where to invest?
Online shopping, already on a steady rise, was propelled even further with the advent of the
COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online …
COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online …
Facilitating user authorization from imbalanced data logs of credit cards using artificial intelligence
An effective machine learning implementation means that artificial intelligence has
tremendous potential to help and automate financial threat assessment for commercial firms …
tremendous potential to help and automate financial threat assessment for commercial firms …
A systematic review of data mining approaches to credit card fraud detection
I Mekterović, L Brkić, M Baranović - WSEAS transactions on business and …, 2018 - croris.hr
Sažetak Credit card fraud is a serious and ever-growing problem with billions of dollars lost
every year due to fraudulent transactions. Fraud has always been present and will always …
every year due to fraudulent transactions. Fraud has always been present and will always …
Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
Background A new algorithm has been developed to enable the interpretation of black box
models. The developed algorithm is agnostic to learning algorithm and open to all structural …
models. The developed algorithm is agnostic to learning algorithm and open to all structural …
Business Continuity and Disaster Recovery Strategies as Resilience Tools after Cyberattacks in Toxic Enterpreneurship Ecosystems
L Raimi - Cybersecurity for Decision Makers, 2023 - taylorfrancis.com
In the field of economics, strategic management, and environmental management, the
concepts of business continuity (BC) and disaster recovery (DR) are pervasive and widely …
concepts of business continuity (BC) and disaster recovery (DR) are pervasive and widely …
Secure framework for e-commerce applications in cloud environment
S Shrivastava, RK Pateriya - Improving E-Commerce Web …, 2018 - igi-global.com
There has been a massive increase in the use of the internet for shopping and payment. The
ease and availability of the internet has accelerated the growth of online applications. E …
ease and availability of the internet has accelerated the growth of online applications. E …
[PDF][PDF] Interpretation and mining of statistical machine learning (Q) SAR models for toxicity prediction.
SJ Webb - 2015 - researchgate.net
Abstract Structure Activity Relationship (SAR) modelling capitalises on techniques
developed within the computer science community, particularly in the fields of machine …
developed within the computer science community, particularly in the fields of machine …
Making a reinforcement learning agent believe
K Häming, G Peters - Artificial Neural Networks and Machine Learning …, 2012 - Springer
We recently explored the benefits of a reinforcement learning agent which is supplemented
by a symbolic learning level. This second level is represented in the symbolic form of …
by a symbolic learning level. This second level is represented in the symbolic form of …