17 results about "Chargeback" patented technology

Systems, methods, and computer program products for using machine learning to determine whether to dispute a chargeback request are provided. A dispute processing system receives a chargeback request for a transaction, transaction data and service provider data associated with the transaction. The dispute processing system incorporates the transaction and service provider data into a template. Next, the dispute processing system uses a machine learning framework to generate machine learning scores that indicates a likelihood of successfully winning the chargeback request and avoiding pre-arbitration. Using the machine learning scores, the transaction data, the service provider data, and at least one dispute processing rule, the dispute processing system determines a likelihood of successfully challenging the chargeback request. Based on the likelihood of successfully contesting the chargeback request, the dispute processing system generates a contestation document from the template, and submits the contestation document to contest the chargeback request.

A method for establishing a cross-issuer chargeback fraud detection system includes receiving a fraud analysis request for one or more chargebacks from a payment processor using an Application Programming Interface (API) over a computer network, extracting identifying information of transactions associated with the one or more chargebacks from the fraud analysis request, searching for a fraud analysis profile linked to the extracted identifying information in a profile database, determining whether the fraud analysis profile linked to the extracted identifying information exists in the profile database, upon determining that the fraud analysis profile linked to the extracted identifying information does not exist in the profile database, retrieving historical transaction data associated with the extracted identifying information from a historical transaction database, retrieving reported fraudulent activities from one or more financial institutions associated with the extracted identifying information, and aggregating the retrieved historical transaction data and reported fraudulent activities.

Systems and methods are disclosed for generating a prediction on chargeback representment based on probability data and / or results from a plurality of machine learning models. The method includes receiving data associated with at least one disputed transaction for at least one user, wherein the received data includes user-specific information and / or merchant-specific information. The received data is processed to calculate a probability of success in a chargeback representment for the at least one disputed transaction. A prediction is calculated based, at least in part, on the probability of success, one or more results from a plurality of machine learning models, or a combination thereof. A presentation is generated of at least one recommendation on the chargeback representment based, at least in part, on the prediction in a user interface of at least one device associated with the at least one user.

Systems and methods for evaluating risks for physical locations are disclosed. A request for risk evaluation of transactions associated with a physical location located within a predetermined region including a plurality of neighboring locations is received. A spatial model is implemented to generate a first risk score based on chargeback data associated with the physical location and chargeback data of the plurality of neighboring locations. A temporal model is implemented to generate a second risk score for the physical location. A final risk score for the physical location is generated based on the first risk score and the second risk score. The final risk score is transmitted to a computing device for detecting fraudulent transactions associated with the physical location in response to the request.

A method for optimizing refunds for suspected or detected fraudulent transactions includes receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor extracting identifying information of transactions associated with the chargeback transaction from the chargeback analysis request, searching for a chargeback analysis profile in a profile database, determining whether the chargeback analysis profile exists in the profile database, upon determining that the chargeback analysis profile does not exist in the profile database, obtaining a new fraud analysis profile, determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback, determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback after a proactive electronic refund transaction, and generating a proactive electronic refund transaction based on the first probability and the second probability.

Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant. The notification message includes the merchant health and compliance risk scores, the merchant terminal risk scores, the merchant chargeback risk scores, and the merchant activity risk scores.

Examples manage chargeback requests between issuers and acquirers. A method includes identifying an issuer and an acquirer involved in an active dispute case, computing a first count of wins for the issuer at a particular stage in a dispute resolution process using historical dispute cases; computing a number of resolved cases at the first stage; computing a win percentage for the issuer based on the first count of wins and the number of resolved cases; weight the win percentage; add the weighted win percentage with other weighted win percentages, thereby resulting in a dispute score; and transmitting the dispute score for display to one of the issuer and the acquirer, the dispute score representing a likelihood of one of the issuer and the acquirer in prevailing in the active dispute case over the other of the issuer and the acquirer.

The present invention discloses a dynamic subscription management system and method utilizing artificial intelligence. The system comprises a computing device configured to preauthorize a customer's payment instrument, forecast potential chargebacks using machine learning, estimate future cash flow, detect fraudulent activity using AI models, and dynamically adjust subscription terms based on customer characteristics. The computing device generates user interfaces displaying analytics and recommendations, and can automatically implement optimizations. The method includes acquiring real-time transaction data, analyzing attributes using machine learning to predict optimal payment processors, routing transactions accordingly, and dynamically adjusting subscriptions based on AI analysis of customer behavior. The system and method aim to prevent fraud, reduce chargebacks, optimize payment routing, and improve customer retention.

A system / method includes a processor configured to: generate, via a first machine learning (ML) model, a database query for chargeback dispute resolution for a financial transaction between a customer account and a merchant account; retrieve, using the database query, an account behavior description for one or both of the customer account or the merchant account; generate a dispute resolution prompt based on the account behavior description; submit the dispute resolution prompt to a second ML model comprising an LLM to generate a resolution recommendation; and approve or deny a chargeback corresponding to the financial transaction based on the resolution recommendation.

The invention relates to the technical field of electronic commerce transaction security, and discloses a credit card payment refusal early-warning, anti-distinguishing and claim processing method, which can inform a merchant about payment refusal before the payment refusal is initiated through a payment refusal early-warning technology of a trade company, so that the merchant has enough time to process, payment refusal is eliminated through active refund, and the payment refusal efficiency is improved. By stopping delivery, interrupting transaction and avoiding loss, automatically collecting information required for defense by a trade company through a technical means, identifying a payment refusal reason and combining with understanding of card group rules and card issuing bank defense requirements, a set of complete and professional defense materials is arranged for merchants, so that the defense complaint rate is greatly improved, and the risk of disqualification is reduced. A trade company cooperates with an insurance company to provide services such as merchant responsibility judgment and merchant loss pricing for the insurance company, and a payment refusing insurance scheme based on a payment refusing process is designed for the insurance company, so that the merchant obtains collection safety through insurance buying.

A chargeback request processing method. The method is executed by a payment verification device, and comprises: receiving a payment evidence provision request for a completed payment sent by a chargeback request processing device, wherein the payment evidence provision request carries a payment identifier of the completed payment, the payment evidence provision request is sent by the chargeback request processing device upon reception of a chargeback request carrying the payment identifier, the completed payment is a payment that is made on the basis of a first biometric feature and has been successfully made, and the first biometric feature is a biometric feature of a first portion of a payer acquired during the payment process of the completed payment (S301); on the basis of the payment identifier, acquiring a second biometric feature corresponding to the completed payment, the second biometric feature being a biometric feature of a second portion of the payer acquired during the payment process of the completed payment (S302); and sending to the chargeback request processing device payment evidence information comprising the second biometric feature, such that the chargeback request processing device processes the chargeback request on the basis of the degree of matching between the biometric feature in the payment evidence information and a biometric feature of a chargeback requester (S303).

Examples manage chargeback requests between issuers and acquirers. A method includes identifying an issuer and an acquirer involved in an active dispute case, computing a first count of wins for the issuer at a particular stage in a dispute resolution process using historical dispute cases; computing a number of resolved cases at the first stage; computing a win percentage for the issuer based on the first count of wins and the number of resolved cases; weight the win percentage; add the weighted win percentage with other weighted win percentages, thereby resulting in a dispute score; and transmitting the dispute score for display to one of the issuer and the acquirer, the dispute score representing a likelihood of one of the issuer and the acquirer in prevailing in the active dispute case over the other of the issuer and the acquirer.

Systems and methods are disclosed for generating a prediction on chargeback representment based on probability data and / or results from a plurality of machine learning models. The method includes receiving data associated with at least one disputed transaction for at least one user, wherein the received data includes user-specific information and / or merchant-specific information. The received data is processed to calculate a probability of success in a chargeback representment for the at least one disputed transaction. A prediction is calculated based, at least in part, on the probability of success, one or more results from a plurality of machine learning models, or a combination thereof. A presentation is generated of at least one recommendation on the chargeback representment based, at least in part, on the prediction in a user interface of at least one device associated with the at least one user.

A method and system are provided for issuing new financial cards to customers and for determining whether to approve chargeback requests made by customers. The method and system utilize a chargeback database that is accessible by multiple card issuers. The chargeback database uses customer identifiers that are unique for each customer in the database. Card issuers are able to search the database to retrieve historical chargeback data involving a particular customer. The historical chargeback data includes chargeback requests made to any card issuer with access to the database.

A method for optimizing refunds for suspected or detected fraudulent transactions includes receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor, extracting identifying information of transactions associated with the chargeback transaction from the chargeback analysis request, searching for a chargeback analysis profile in a profile database, determining whether the chargeback analysis profile exists in the profile database, upon determining that the chargeback analysis profile does not exist in the profile database, obtaining a new fraud analysis profile, determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback, determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback after a proactive electronic refund transaction, and generating a proactive electronic refund transaction based on the first probability and the second probability.

A system / method includes a processor configured to: generate, via a first machine learning (ML) model, a database query for chargeback dispute resolution for a financial transaction between a customer account and a merchant account; retrieve, using the database query, an account behavior description for one or both of the customer account or the merchant account; generate a dispute resolution prompt based on the account behavior description; submit the dispute resolution prompt to a second ML model comprising an LLM to generate a resolution recommendation; and approve or deny a chargeback corresponding to the financial transaction based on the resolution recommendation.

A method for optimizing refunds for suspected or detected fraudulent transactions includes receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor extracting identifying information of transactions associated with the chargeback transaction from the chargeback analysis request, searching for a chargeback analysis profile in a profile database, determining whether the chargeback analysis profile exists in the profile database, upon determining that the chargeback analysis profile does not exist in the profile database, obtaining a new fraud analysis profile, determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback, determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback after a proactive electronic refund transaction, and generating a proactive electronic refund transaction based on the first probability and the second probability.