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Machine learning helps identify patterns from study of existing fraudulent cases. By detecting discrepancies in IP addresses, billing, etc. our system generates a “risk number” for transactions which ultimately decides which account to inspect further.
There are two major advantages to this approach. Firstly, the algorithm reduces human intervention and goes on identifying potential fraud cases by mining, sorting, and studying the transaction data. Secondly, since this is a smarter approach than manual testing, not only are number of people necessary perform this vigilance reduced, but the number of non-fraud cases that are inspected also goes down.
Benefits
Available manpower
Targeted case inspection
Reduction of inspection of non-fraudulent transactions (false positives)
Identification of new fraud cases and patterns