AI News, BOOK REVIEW: Fraud prevention in peer-to-peer (P2P) transaction networks using Neural Nets: A Node Embedding approach
- On Saturday, October 13, 2018
- By Read More
Fraud prevention in peer-to-peer (P2P) transaction networks using Neural Nets: A Node Embedding approach
Since the emergence of digital banking and online shopping, it has never been easier for companies, banks, and customers to trade goods and transfer money.
For this reason, P2P platforms strive to prevent fraudulent transactions by letting users rate each other after completing a transaction using perception or trust scores.
Using recently-developed methods for representation learning on graphs, I built a classifier that discriminates between honest sellers and fraudsters in order to estimate the likelihood that a seller will commit fraud in a future transaction.
Even though anonymity allows users to keep their sensitive data private, there are serious disadvantages that relate to fraudulent transactions: Given the problems described above and the fact that users are anonymous, it would seem like we can only count on perception scores to discriminate between good sellers and fraudsters.
After all, unlike banks and other financial institutions that work on fraud prevention technologies, we don’t have any additional information about our users such as their credit scores or their financial history.
As shown below, the Node2Vec 2D projections clearly separate nodes into two regions, as would be expected from the fact that users come from two different marketplaces (Bitcoin OTC and Bitcoin Alpha) To make more complete node representations, I concatenated 6 additional node features to each Node2Vec vector.
For each transaction in the network, I created a 40-dimensional vector representing a buyer-seller pair and I fed these vectors into a Neural Net (NN) with the following layers: For the purpose of training this NN under supervised learning, I labeled the fraud transaction class as 1 and the honest transaction class as 0.
Because of the class imbalance (89% of the training examples are honest transactions or 0's), I used bootstrapping to train the NN with a different balanced subset of training examples every 10 epochs for a total of 150 epochs.
- On Thursday, September 19, 2019
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