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 eCommerceDuplicate transactions. A common scammethod is creating transactions close to originalor making a copy of a transaction. For instance,a company tries to charge a counterpart twicewith the same invoice by sending it to differentbranches.Rule-based systems currently used constantlyfail to distinguish between error or unusualtransactions from real fraud. For example, acustomer can accidentally push a submissionbutton twice or simply decide to buy twice moregoods. The system should differentiate suspiciousduplicates from human errors. While duplicatetesting can be implemented by conventionalmethods, machine learning approaches willincrease accuracy in distinguishing erroneousduplicates from fraud attempts.Monitoring metrics for behavior-based fraud detection solutionsAccount theft and unusual transactions. Much offraud detection in payments is focused on userbehavior analysis during transactions.a higher level of fraud likelihood. To check thistransaction, the system will send a verificationrequest to a card owner.For example, a client visits a specific supermarketat 9-10 pm every night. It’s located near the client’shouse. The payment sum varies from $10 to $40.Every two days the client also drives to a gasstation.Once a transaction occurs in a different part oftown in a bar and the sum is $40, the algorithmwill consider this activity suspicious and assignDescriptive stats like averages, standarddeviations, and high/low values are very usefulfor analyzing behavior. These metrics allow forcomparing separate transactions with personalor intra-group benchmarks. Payments with largestandard deviations look suspicious. So, a goodpractice is to send a request for verification toan account owner if such deviations occur.10
Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerce2.5 Preventing Loan Application FraudLending is sensitive to scams that abuse personalinformation. Just a decade ago, it was difficultfor fraudsters to get access to IDs, photos,addresses, and mobile phone numbers. Today,nearly all data can be found in social networksor elsewhere on the Internet. This makes the lifeof financial institutions more difficult. Fraudstersbecome smarter, and loan applications requiremore rigorous assessment, while clients want toget money as soon as possible.Personal details counterfeiting. A commontype of fraud is often based on providing falsepersonal information. Scammers providepersonal details with a number of misspellingissues or misrepresentations of income or creditqualifications. It makes debt collection moredifficult. The problem can be solved in two ways.The first one entails reviewing the customerrelationship history with a bank looking forinconsistencies and quickly verifying recordfields via open APIs.The second path is more sophisticated andrequires building a scoring model or calculatingfraud probability. Scoring models calculate thefraud possibility of the record and grade itagainst a standard scale. It helps assess whichapplications are more likely to be fraudulent.Machine learning and advanced analytics solvethe problem of fraud probability assessment byclassifying applications into groups.Such solutions allow for minimizing costs byreducing the need to verify each application andconcentrating the efforts on the risky loans only.It also improves general credit scoring – theprocess of grading customer creditworthiness –by distinguishing fraudsters from badborrowers. Drawing a distinct line betweenfraudsters and problematic borrowers alsoensures better credit statistics.An Experian case study with a European bankshowed the following benefits of scoringsystems:••Focus on 5 percent of applications allowed fordetecting 52 percent of fraud cases;••Machine learning analytic systemsdemonstrated 9 times higher detection scorethan portfolio average.11
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Fraud-Detection-How-Machine-Learning-Systems-Help-Reveal-Scams-in-Fintech-Healthcare-and-eCommerce

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