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 eCommerce1. Machine Learning vs. Rule-BasedSystems in Fraud DetectionThe machine learning (ML) approach to frauddetection has received a lot of publicity in recentyears and shifted industry interest from rulebasedfraud detection systems to ML-basedsolutions. What are the differences betweenmachine learning and rule-based approaches?The rule-based approach. Fraudulentactivities in finance can be detected by lookingat on-surface and evident signals. Unusually,large transactions or the ones that happen inatypical locations obviously deserve additionalverification. Purely rule-based systems entailusing algorithms that perform several frauddetection scenarios, manually written by fraudanalysts. Today, legacy systems apply about300 different rules on average to approve atransaction. That’s why rule-based systemsremain too straightforward. They requireadding/adjusting scenarios manually and canhardly detect implicit correlations. On topof that, rule-based systems often use legacysoftware that can hardly process the real-timedata streams that are critical for the digitalspace.ML-based fraud detection. However, there arealso subtle and hidden events in user behaviorthat may not be evident, but still signal possiblefraud. Machine learning allows for creatingalgorithms that process large datasets withmany variables and help find these hiddencorrelations between user behavior and thelikelihood of fraudulent actions. Anotherstrength of machine learning systems comparedto rule-based ones is faster data processingand less manual work. For example, smartalgorithms fit well with behavior analytics forhelping reduce the number of verification steps.4
Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerceRule-based vs ML-based fraud detection systemsThough, while rule-based systems are inferior toML-driven ones, they still dominate the market.Leading financial institutions, however, alreadyuse the ML technology to combat fraudsters.For instance, MasterCard integrated machinelearning and AI to track and process suchvariables as transaction size, location, time,device, and purchase data. The systemassesses account behavior in each operationand provides real-time judgment on whethera transaction is fraudulent. The project aimsat reducing the number of false declines inmerchant payments. The recent study showsthat false declines make merchants lose about$118 billion per year while clients’ loss is about$9 billion per year. It’s the largest area forfraud in financial services. So fraud preventionis a strategic goal for banking and paymentsindustries.Feedzai, a fintech company, claims that a finetunedmachine learning solution can detect upto 95 percent of all fraud and minimize the costof manual reconciliations, which accounts nowfor 25 percent of fraud expenditures. Capgeminiclaims that fraud detection systems usingmachine learning and analytics minimize fraudinvestigation time by 70 percent and improvedetection accuracy by 90 percent.These facts prove the benefits of using machinelearning in anti-fraud systems.5
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Fraud-Detection-How-Machine-Learning-Systems-Help-Reveal-Scams-in-Fintech-Healthcare-and-eCommerce

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