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

Recommendations

Info

Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerce2. Fraud Scenarios and TheirDetection2.1 Insurance Claims Analysis for Fraud DetectionInsurance companies spend several days toweeks assessing a claim, but the insurancebusiness is still affected by scams. The mostcommon issues are property damage, carinsurance scams, and fake unemploymentclaims. The ticket to successful detection is agood dataset and carefully selected models.Fake claims. Semantic analysis is a machinelearning task that allows for analyzing bothstructured, table-type data, and unstructuredtexts. The feature helps detect fake and falsifiedclaims in the insurance industry. For example,it improves car insurance claims processing.Machine learning algorithms analyze fileswritten by insurance agents, police, and clients,searching for inconsistencies in providedevidence. There are many hidden clues in thesetextual datasets. The rule-based engines don’tcatch the suspicious correlations in textual data,and fraud analysts can easily miss importantevidence in boring investigation files. That’s whyanalyzing claims is one of the most promisingspheres for machine learning applications.Duplicate claims and overstating repair cost.Smart ML-backed algorithms are also efficientin duplicate claims detection or inconsistenciesin car repair cost. Classifying data in repairclaims solves the problem by uncovering hiddencorrelations in claim records or even behaviorsof insurance agents, repair services, and clients.For example, the repair service company mayprovide higher pricing for the customers of aspecific agent.Let’s have a look at the results of the AI-basedresearch of insurance vehicles claims conductedby Wipro. The company explored four datasetswith such features as a vehicle style, clientgender, marital status, license type, injury type,loss date, claim date, police notification date,repair amount, sum insured, market value, etc.6
Fraud Detection: How Machine Learning Systems Help Reveal Scamsin Fintech, Healthcare, and eCommerceA pre-research analysis disclosed:••Fraudulent claims are more likely not reportedto police.••Old vehicles are more likely to be involved infraud.••Eighty percent of accidents that happen duringholidays involve fraud.••Scams are more likely to involve third partiesthan legitimate claims.Then the data was processed using five differentmachine learning algorithms: Logistic Regression,Modified Multi-Variate Gaussian, ModifiedRandomized Undersampling, Adjusted MinorityOversampling, and Adjusted Random Forest.Eventually, the best results were achieved by theModified Randomized Undersampling model thatshowed 79 percent accuracy.Machine learning models7
Page 1 and 2: Fraud Detection: How Machine Learni
Page 5: Fraud Detection: How Machine Learni

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?