Abstract: An inventive cleansing composition includes at least one polysaccharide derivative selected from holosides and a non-sulfated glycosaminoglycan, the at least one polysaccharide derivative, and a blend of two or more surfactants, the blend of two or more surfactants, selected from anionic surfactants, amphoteric/zwitterionic surfactants, non-ionic surfactants. The cosmetic composition may be a single phase, water-based composition, and is free or essentially free of silicones.

Abstract: In some examples, a system may receive first merchant data for a first merchant. For instance, the first merchant data may include one or more merchant characteristics associated with the first merchant. The system may assign the first merchant to a first group of merchants based at least in part on the first merchant sharing at least one of the merchant characteristics with the first group of merchants. The system may use a machine learning model to determine a customized timing at which to provide a loan offer to the first merchant. The customized timing n ay be based at least in part on loan data associated with one or more loans previously offered to the merchants in the first group of merchants. Further, the system may send the loan offer to a computing device of the first merchant based at least in part on the customized timing.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: Techniques for optimizing capital loan offers for merchants to maximize a potential benefit of the capital loans to the merchants. A service provider may receive merchant data and/or transaction data from a merchant. The service provider may determine merchant characteristics associated with the merchant based on the merchant data and/or transaction data. The service provider may associate the merchant with a merchant group, based on the merchant characteristics. The service provider may access data corresponding to capital loans previously offered to and/or accepted by merchants in the merchant group, to determine predictive factors of successful loans. Based on the predictive factors, the service provider may generate a capital loan for the merchant with a high probability of positive impact on a merchant business. The service provider may transmit a capital loan oiler to the merchant at a time determined to maximize acceptance and optimize a growth of the merchant business.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: Embodiments of the present invention relate to systems, methods, and computer program products for assessing student prospects. More specifically, embodiments of the present invention are directed to receiving transaction data from a transaction involving a consumer, comparing the transaction data to an education position rule, and determining an education position based at least partially on a comparison of the transaction data to an education position rule.

Abstract: Apparatus and methods for providing a budget envelope credit card are provided. An apparatus according to the invention may include a budget envelope credit card software engine and a rewards software engine that provides a reward scheme for use by the budget envelope credit card engine, the rewards scheme that defines a reward frequency.