Stripe, Inc.

Tara Seshan

Adam Eagle

Michael Manapat

Isaac Hepworth

Mike Towber

Clinton Carjoel Blackburn

Victoria Martinez

Systems, methods, and apparatuses for implementing user customizable risk management tools with statistical modeling and a recommendation engine within a computing environment are provided. A system may include, for example, means for evaluating the performance of a user rule for fraud prevention, in which the system receives a plurality of purchase transactions for the user; analyzes each purchase transaction received to generate a fraud likelihood score; receives the rule that specifies conditions when the system is to accept or reject transactions regardless of the fraud likelihood score generated by the system; transmits a historical analysis to the user based on the received rule; receives an input from the user to activate the rule; monitors performance of the rule; and transmits a recommendation to the user to retain or cancel the activated rule based on the monitored performance. Other related embodiments are disclosed.

Systems, methods, and apparatuses for implementing user customizable risk management tools with statistical modeling and recommendation engine

In an example embodiment, information about one or more failed payment attempts via an electronic payment processing system is obtained. One or more features are extracted from the information. Then, for each of a plurality of potential candidate retry time points, the one or more features and the potential candidate retry time point are fed into a dunning model, the dunning model trained via a machine-learning algorithm to produce a dunning score indicative of a likelihood that a retry attempt at an input retry time point will result in a successful payment processing. The dunning scores for the plurality of potential candidate retry time points are used to select a desired retry time point. Then the electronic payment processing system is caused to attempt to reprocess a payment associated with one of the failed payment attempts at a time matching the desired retry time point.

Optimized dunning using machine-learned model

In an example embodiment, a payment processor automatically generates a virtual account number that can be used by customers to perform push payments, such as ACH, wire transfer, and direct deposits. This virtual account number may be surfaced to customers via invoices sent by service or product providers. The payment processor is then able to automatically match the payment sent to the virtual account number and the amount owed on the invoice.

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San Francisco, CA, United States of America

Tara Seshan