Fraud Detection with Machine Learning: Preventing 5 Most Common Fraud Types

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Roman SPD activities: Tech Writer
on the timeline of SPD Group

Apr 13, 2020

Fraud Detection with Machine Learning becomes possible due to the ability of ML algorithms to learn from historical fraud patterns and recognize them in future transactions. Machine Learning algorithms appear more effective than humans when it comes to the speed of information processing. Also, ML algorithms are able to find sophisticated fraud traits that a human simply cannot detect.

Works faster. Rule-based systems imply creating exact written rules to “tell” the algorithm which types of operations seem normal and should be permitted, and which shouldn’t be because they seem suspicious. However, writing rules takes a lot of time. Also, manual interaction in the E-Commerce world is so dynamic that things can change significantly within a few days. Here Machine Learning fraud detection methods will come in handy to learn new patterns.

Scale. ML methods show a better performance along with the growth of the dataset to which they are fitted — meaning the more samples of fraudulent operations they are trained on, the better they recognize fraud. This principle does not apply to rule-based systems as long as they never evolve themselves. Also, a data science team should be aware of the risks linked to fast model scaling; if the model did not detect fraud and marked it incorrectly, this will lead to false negatives in future.

Efficiency. Machines can take routine tasks and the dirty work of manual analysis, while specialists will only spend time making more high-level decisions.

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Articles authored by Roman SPD