Abstract: A system receives a request for payment of a transaction between a vendor and a consumer, and sends a first request to a database associated with the online service for historical transactions and personal attributes of the vendor concurrently with sending a second request to a number of third-party services for credit information and personal attributes of the consumer. The system receives information responsive to the first and second requests from the database and the third-party services, respectively, and obtains a risk score for the transaction based on an application of one or more risk assessment rules to the received information by a machine learning model trained with at least the historical transactions and the personal attributes of the vendor. In some aspects, the system determines whether to advance funds to the vendor, prior to requesting payment from a consumer account, based at least in part on the risk score.

Abstract: The present invention relates to an improved process for the preparation of 4-amino-N-tert-butyl-4,5-dihydro-3-isopropyl-5-oxo-1H-1,2,4-triazole-1-carboxamide (Amicarbazone). The present invention more particularly relates to an improved process for the preparation of 4-amino-N-tert-butyl-4,5-dihydro-3-isopropyl-5-oxo-1H-1,2,4-triazole-1-carboxamide (Amicarbazone) by coupling of 4-Amino-2,4-dihydro-5-(1-methylethyl)-3H-1,2,4-triazol-3-one (TAZ) and poor quality tert-Butyl isocyanate (TBIC) with purity less than or equal to 98%, preferably less than or equal to 85%.

Abstract: A method for computing system learning of a merchant category code includes accessing a social media platform using administrative information of a merchant to obtain social media information of the merchant, and performing natural language processing on the social media information to add key terms to a merchant terminology corpus defined for the merchant. The key terms in the first merchant terminology corpus are normalized. For each key term in the first merchant terminology corpus and for each merchant category code, a frequency value for the merchant category code is calculated to obtain multiple frequency values. The method further includes aggregating frequency values across the first key terms in the first merchant terminology corpus to select the matching merchant category code of the merchant, and applying the matching merchant category code to the merchant.