Fraud-Detection-How-Machine-Learning-Systems-Help-Reveal-Scams-in-Fintech-Healthcare-and-eCommerce

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Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerce4. Setting an express verification. Usingensembles requires a great deal of computingpower and time to crunch all data. If you wereto check all transactions, the time spent oncalculations may harm user experience. That’swhy a good practice is to make verificationin two steps. The express verification impliessimple anomaly detection or anotherstraightforward ML method to divide alltransactions into regular and suspicious ones.As regular transactions don’t require furtherverification, the system approves them. Thosethat look suspicious are sent for advancedverification through a complex ensemble.16
Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerce4. Supervised Fraud DetectionMethodsIn terms of actual machine learning methods, there are five commonly used types. We’ll cover onlysupervised learning methods as they can be applied in building complex ensembles.4.1 Random ForestsRandom forest (or an ensemble of decision trees) isan algorithm which builds decision trees toclassify the data objects. The model selects avariable that enables the best splitting of recordsand repeats the splitting process multiple times.As a result, if we were to visualize how thealgorithm works, the image would look like atree. To make predictions more precise, datascientists train multiple decision trees on randomsubsets from a general dataset. To decide whethertransaction looks like a fraud, trees vote, and themodel provides a consensus judgment.on future data. Another problem is the datasetbalance. If a dataset contains mostly normaltransactions and just a small fraction of fraudulentones, the accuracy may decrease.Pros: Besides their simplicity and speed, randomforests can be used with different types of data,including credit card numbers, dates, IP addresses,postal codes, etc. They are considered precisepredictors that can work even with datasets thathave missing records.Cons: Sometimes engineers stumble over theproblems with overfitting. Overfitting meansthat the model over-remembers the patterns inthe training dataset and fails to make predictionsRandom forest or ensemble of decisiontrees model traiming set17
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Fraud-Detection-How-Machine-Learning-Systems-Help-Reveal-Scams-in-Fintech-Healthcare-and-eCommerce

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