There Is Only One Solution To The Bitcoin Scaling Debate

Forbes - Tech

The Bitcoin scaling debate goes on and on. The argument is not over whether Bitcoin should scale up: clearly, if it is to become a serious challenger to mainstream payments providers such as Visa and central bank RTGS systems such as Fedwire, it must be able to handle daily transaction volumes in the billions. No, the question is how it should scale up. There are basically two camps: those who follow the original thinking of Bitcoin's creator, Satoshi Nakamoto, that all transactions should be on-chain and democratically validated, and those who think that the way forward is to take most transactions off-chain, leaving only large transactions (perhaps made up of thousands of netted small transactions) on the main blockchain. There is little doubt that the network could scale up to handle Fedwire volumes.


Multiple perspectives HMM-based feature engineering for credit card fraud detection

arXiv.org Artificial Intelligence

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.


Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

arXiv.org Machine Learning

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspectives, namely (i) The sequence contains or doesn't contain a fraud (ii) The sequence is obtained by fixing the card-holder or the payment terminal (iii) It is a sequence of spent amount or of elapsed time between the current and previous transactions. Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sequences is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection. In extension to previous works, we show that this approach goes beyond ecommerce transactions and provides a robust feature engineering over different datasets, hyperparameters and classifiers. Moreover, we compare strategies to deal with structural missing values.


Improving Fraud Detection: Rules versus Models - Feedzai

#artificialintelligence

It is standard practice in managing payments to block potentially fraudulent transactions via a set of rules. These rules can be very effective in mitigating fraud risk, and practitioners in the industry are comfortable with this approach. Quite often these rules are able to mitigate the losses from fraudulent transactions without producing a correspondingly high alarm rate.


Security researcher scrapes millions of Venmo transactions

Daily Mail - Science & tech

Venmo's privacy settings have once again been proven to be faulty. A computer science student was able to scrape data from seven million transactions made on the PayPal-owned peer-to-peer payments app, according to TechCrunch. The student, Dan Salmon, said he published the transactions in order to alert users that they should set their payments to private. Venmo's privacy settings have once again been proven to be faulty. 'I am releasing this dataset in order to bring attention to Venmo users that all of this data is publicly available for anyone to grab without even an API key,' Salmon wrote in a post sharing his research.