Application / Account Opening Fraud

More accurately score applicants for fraud to drive down manual reviews and increase auto-acceptance rates

Reduce false positive rates to ensure only applications that require manual reviews are sent to fraud analysts 

Improve the customer experience during digital onboarding

Extend fraud prevention coverage earlier in the account lifecycle

Understand customer behavior and KYC to better recognize illegitimate customers and create identity risk scores

Determine fraud risk before a customer’s application is accepted or declined

Use application (document or digital based) data and monitor all payment channels to dtect complex fraud emanating from stole and synthetic ID, as well as mule activity, with high accuracy

Document classification to identify ghost invoices from companies that do not exist whose identity a fraudster assumes; identify legitimate invoices from forged ones

Logistic regression to determine if the result of reviewing customer data is fraud or not fraud

Decision Trees

Random Forest

Neural Networks