Fraud Risk Scheme :
A fraudster combines real identity elements (e.g., a stolen Social Security Number) with fictitious ones (e.g., name, address) to create a credible profile. This "fake" customer deposits a small amount, uses the account’s associated card responsibly over several months to build a transaction history, and then applies for a large loan — disappearing without repayment.
Detection :
Detection of data inconsistencies: Algorithms flag discrepancies (e.g., a Social Security Number issued in one state while the address is in another, or an age inconsistent with the SSN issue date).
Cross-checking with reliable databases: Queries to credit rating agencies and government databases help confirm the actual existence and consistency of the profile.
Network relationship analysis: Detects if the phone number, email address, or postal address is associated with numerous other suspicious profiles.
Behavioral monitoring: Alerts triggered by abnormal activity from a "new" customer, such as multiple or rapid credit applications shortly after account opening.
Prevention :
Robust authentication: Implementation of strict identity verification processes (e.g., document analysis with forgery detection, biometric verification).
Proactive credit monitoring: Monitoring credit applications made under a Social Security Number but with different names or birthdates.
Staff training: Raising awareness among banking advisors to spot red flags indicating a synthetic identity during onboarding.
Delayed credit granting: Establishing a "maturation" period before allowing access to significant credit products.
Share Your Feedback :
What tools, techniques, and processes are used for detection and prevention?
