Abstract: A method of evaluating a merchant cash advance option may include receiving time series information including a plurality of signals representing transactions of merchants and customers where at least one of the signals relates to product returns. The method may further include aggregating the time series information, generating a plurality of trained models associated with the product returns, employing a selected model to forecast return volume and repurchase volume for a given merchant, determining a risk assessment value for the given merchant, and, if the risk assessment value is below a risk threshold, providing an offer of a merchant cash advance to the given merchant based on the forecast return volume and repurchase volume.

Abstract: A method of facilitating a product return transaction may include receiving product return data from a merchant indicating merchant-specific information on product returns to the merchant and receiving an indication of a product return request from a customer. The method may further include employing a trained model to determine a likelihood of return rating associated with physical receipt of a returned product associated with the product return request based on the product return data, responsive to the likelihood of return rating exceeding a threshold, extending a credit to the customer based on a value of the returned product, and enabling the customer to initiate a product repurchase using the credit.

Abstract: A method of recommending products in association with product returns may include receiving a return request from a customer associated with a returned product and receiving search results based on a search for relevant products to the returned product. The method may further include filtering the search results based on a likelihood of return rating of at least some of the relevant products, and displaying a plurality of recommended products based on the filtering of the search results.