Systems and methods for transferring value. A method includes posting, by a user device to a blockchain, a deposit transaction comprising a deposit value and conditions and updating, by the user device, a state according to a transaction amount. The method also includes transmitting, by the user device, a state update of the state to a server computer and responsive to transmitting the state update, and receiving, by the user device, a payment complete message comprising a tuple from the server computer. The method also includes verifying, by the user device, the conditions and processing, by the user device, a server deposit transaction on the blockchain in response to verifying.

A payment terminal, including: a display, an input device configured to generate input signals indicative of a user input, a payment instrument reader configured to receive payment instrument data from a financial payment instrument, a secure processor connected to the payment instrument reader, a main processor connected to the display, the main processor separate and distinct from the secure processor, a secure input processor connected to the input device and the secure processor, wherein the secure processor is operable between an unsecured mode, wherein the secure processor sends input coordinates based on the input signals received from the secure input processor to the main processor; and a secured mode, wherein the secure input processor ceases input coordinate forwarding to the main processor.

Methods, apparatuses and systems are defined for the use of a clearinghouse device in conjunction with remote online signature validation for signature validated or notarized electronic documents. The clearinghouse applies machine learning techniques to generate one or more verification and validation scores associated with signature validation using identification elements and information supplied by a signatory of the electronic document. The verification and validation scores are used to confirm proper execution of the signature validation and generate an electronic signature validation or notarization on the electronic documents.

The various embodiments described herein include methods, devices, and systems for imaging identification information. In one aspect, a video stream of a credential document is obtained via a camera. It is determined whether the video stream of the credential document meets one or more imaging criteria. In accordance with a determination that the imaging criteria are not satisfied, a first indication that the credential document is not ready to be imaged is presented. In accordance with a determination that the imaging criteria are satisfied a second indication that the credential document is ready to be imaged is presented.

Event data of a first entity is accessed. The first entity has been flagged as having a predefined status. The event data corresponds to a plurality of events involving the first entity that occurred within a predefined first time period. Based on the accessing of the event data, behavioral data of the first entity is generated. The behavioral data is formatted as a data sequence. A machine learning model is trained using the behavioral data of the first entity as training data. Using the trained machine learning model, a determination is made as to whether a second entity has the predefined status.

A fraud detection platform may receive transaction data relating to a transaction conducted by a user with a transaction card. The fraud detection platform may receive, from a biometric sensor of the transaction card, biometric data relating to one or more biometric characteristics of the user during the transaction. The fraud detection platform may receive, from an accelerometer of the transaction card, card movement data relating to a measure of shaking of the transaction card by the user during the transaction. The fraud detection platform may process the transaction data, the biometric data, and the card movement data, with a fraud detection model, to determine a fraud score associated with the transaction. The fraud detection platform may perform one or more actions based on the fraud score.

Systems and methods for generating tokenized reputation scores are described. In one example, a service provider system receives data describing user activity associated with a service provider account and determines that the user activity is eligible for a reputation reward of reputation tokens. The service provider system initiates a calculation of the reputation reward based on the data describing the user activity. Further, the service provider system verifies a transfer of reputation tokens to a blockchain account address associated with the service provider account, where the reputation tokens are based on the reputation reward calculated. The service provider system generates a reputation score based on an amount of reputation tokens associated with the blockchain account address and generates an indication of the reputation score. The indication of the reputation score is displayed in relation to a representation of the service provider account.

Methods, systems, and computer program products for implementing a payment consolidation process for travel management systems. A payment request including a travel record identification is received at a payment consolidation server via a payment consolidation user interface. A payment identification is associated with the travel record identification, and the payment consolidation user interface provides access to a plurality of payment processor servers through a single interface. The payment consolidation server obtains eligible payment processor information associated with the travel record identification from a reservation system. The payment consolidation server accesses a first software development kit (SDK) from a first payment provider and a second SDK from a second payment provider based on the eligible payment processor information. The payment consolidation server then updates the payment consolidation user interface with a first interface element associated with the first SDK and a second interface element associated with the second SDK.

This disclosure relates to systems and methods of risk detection in an electronic network. The method may include receiving a first portion of session context information for an interaction of an individual over a limited bandwidth network, the first portion including a session identifier for the interaction. The method may include retrieving a second portion of session context information for the interaction over a cloud data pipe using the session identifier, in response to receiving the first portion. The method may include accessing, a data structure representing connected knowledge of the individual. The method may include receiving, via a distributed system, security reputation data including aggregated information from a plurality of members. The method may include analyzing the session context information using the security reputation. The method may include generating a security risk score for the interaction based on the session context information, data structure, and the security reputation data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for payment services are provided. One of the methods includes: receiving a payment request, the payment request containing a user identifier and operation data generated in a current transaction corresponding to the user identifier; determining, according to the operation data, at least one operation hopping sequence in the current transaction, the operation hopping sequence containing at least one operation hopping event; obtaining, from a set of risk parameters corresponding to the user identifier, a risk parameter corresponding to the operation hopping event in the operation hopping sequence, risk parameters in the set of risk parameters being obtained by adjusting risk parameters used for risk identification in a previous transaction according to operation data generated in the previous transaction corresponding to the user identifier; and adjusting the obtained risk parameter according to the at least one operation hopping sequence.