Abstract: An embodiment establishes a product design database based at least in part on product design data received from a product image repository, wherein the product design data is representative of a plurality of product designs. The embodiment ranks each product design based on a ranking metric derived from market data corresponding to existing retail products. The embodiment constructs a product design ranking based on a relative ranking between each of the product designs that have been ranked. The embodiment generates a new product design using a machine learning model based on the plurality of product designs and the product design ranking. The embodiment evaluates whether the new product design satisfies a design constraint. The embodiment, upon an evaluation that the new product design satisfies the design constraint, stores the new product design in the product design database. The embodiment displays the new product design via an interface.

Abstract: A method and corresponding system to determine an optimized credit limit assignment using reinforcement learning techniques in order to maximize a reward function for a given bank. A reinforcement learning module is configured to use a set of user profiles and an associated set of risk profiles to determine an initial credit limit assignment. Based on this initial credit limit assignment, an updated set of user profiles and an associated set of updated risk profiles are generated. The reinforcement learning module can use these updated sets of user profiles and associated risk profile as inputs to determine an optimized credit limit assignment that maximizes the reward function for the given bank.

Abstract: A method and corresponding system to determine an optimized credit limit assignment using reinforcement learning techniques in order to maximize a reward function for a given bank. A reinforcement learning module is configured to use a set of user profiles and an associated set of risk profiles to determine an initial credit limit assignment. Based on this initial credit limit assignment, an updated set of user profiles and an associated set of updated risk profiles are generated. The reinforcement learning module can use these updated sets of user profiles and associated risk profile as inputs to determine an optimized credit limit assignment that maximizes the reward function for the given bank.