Calculating return window

A method, comprising:

• executing, on a processor, instructions that cause the processor to perform operations associated with product return, the operations comprising:
• identifying a product purchased by a user from a merchant from transaction data of the user;
• predicting a return window of the merchant for the product with a first machine learning model trained with data scraped from merchant websites, industry standards, or transaction data that captures time between when products from merchants are purchased and a credit is issued for return of the products, wherein the first machine learning model outputs a return window that is a length of time and a confidence score that captures a likelihood that the return window is correct;
• predicting a likelihood that the product is a candidate for return with a second machine learning model trained on transaction data that captures purchase debits and return credits for products and merchants, and return history of the user;
• and
• notifying the user of the return window and confidence score for the product when the likelihood that the product is a candidate for return satisfies a predetermined threshold.

A computer-implemented method, comprising:

• identifying a product purchased by a user from a merchant based on transaction data of the user from a financial institution;
• predicting a return window of the merchant for the product with a first machine learning model trained with one or more of data scraped from merchant websites, industry standards, or historical transaction data of multiple users, wherein the first machine learning model outputs a return window that is a number of days and a confidence score that captures a likelihood that the return window is correct;
• predicting a likelihood that the product is a return candidate with a second machine learning model trained on the historical transaction data of the user capturing purchase debits and return credits for products, merchant type, and product type;
• and
• informing the user of the return window and confidence score for the product when the likelihood the product is a return candidate satisfies a predetermined threshold.