Fraud Detection In Machine Learning
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.
The financial services industry and the industries that involve financial transactions are suffering from fraud-related losses and damages. Fraud Detcction Technique used to reduce the loss of it
Fraud Detection In different Senerious
Insurance claims analysis for fraud detection: Insurance companies spend several days to weeks assessing a claim, but the insurance business is still affected by scams. These are:
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property damage
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car insurance scams
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fake unemployment claims
Anti-fraud solutions for medical claims and healthcare: Healthcare and medical insurance is a rich area for fraud schemes due to a complex and bureaucratic process, which requires many approvals, verifications, and other paperwork.
The most common scams are fake claims that use:
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false or invalid social security numbers,
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claims duplication,
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billing for medically unnecessary tests,
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fake diagnosis, etc
Fraud prevention solutions in eСommerce: The eCommerce scam is closely linked to payments. So in it need mostly two types of detection:
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Identity theft
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Merchant scams
Fraud detection in banking and credit card payments:
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Duplicate transactions.
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Data credibility assessment.
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Account theft and unusual transactions.
Preventing loan application fraud:
Detect fraudsters to get access to
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IDs,
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photos,
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addresses,
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and mobile phone numbers
Machine learning for anti-money laundering:
It used to detect:
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Regulators,
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banks,
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and investment firms
Why is machine learning suited to fraud detection?
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Super fast
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Scalable
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Efficient (and cheap!)
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More accurate
How Model Works
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Input data
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Extract features
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Train algorithm
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Create model