A method for providing online security may include: (1) receiving, by a validation computer program executed by a trusted entity backend for a trusted entity, a call from a web browser executed on a customer electronic device browsing a webpage for an online entity, the call comprising an online entity identifier for the online entity and a session identifier, wherein the webpage for the online entity may include a hidden <iframe> comprising code that causes the web browser to execute the call; (2) confirming, by the validation computer program, that a cookie for the trusted entity may be stored on the customer electronic device; and (3) returning, by the validation computer program, a first value indicating that the customer electronic device is known to the trusted entity or a second value indicating that the customer electronic device is not known to the trusted entity based on the confirmation.

Methods and apparatuses for supporting transactions of products are provided. One of the methods comprises, receiving, by a second terminal, information of a seller that is selling the products, from a first terminal, requesting, by the second terminal, verification of the seller's information from a server, in response to successful verification of the seller's information, acquiring, by the second terminal, a transaction identifier for the products from the server, measuring, by the second terminal, a distance from the first terminal creating, by the second terminal, transaction data including the measured distance and the transaction identifier, requesting, by the second terminal, verification of the transaction data from the server and in response to receiving a message indicating successful verification of the transaction data from the server, performing, by the second terminal, a payment routine for the products.

A system for generating a contention scheme includes a computing device, the computing device configured to obtain a solvency signature as a function of a solvency entity, determine a solvency grouping as a function of the solvency signature, identify a null element as a function of the solvency grouping, wherein identifying the null element further comprises receiving a regulation element as a function of a regulation database, and identifying the null element as a function of the regulation element and the solvency grouping, produce a weighted vector as a function of the null element, and generate a contention scheme as a function of the weighted vector.

Embodiments provide methods and systems for dynamic spend policy optimization. Method performed by server system includes receiving payment authorization request for payment transaction initiated by cardholder from acquirer. The payment authorization request includes transaction data. The method includes determining spend variables associated with cardholder based on transaction data and identifying at least one cardholder segment from a plurality of cardholder segments based on the spend variables and a clustering model. The at least one cardholder segment is associated with cardholder. The method includes accessing spend policy rules applicable to the payment transaction based on the transaction data and determining optimal spend threshold values corresponding to the spend policy rules applicable to the payment transaction based on the at least one identified cardholder segment and a reinforcement learning (RL) model. The method includes generating spend policy recommendation for cardholder based on the optimal spend threshold values.

Aspects of the disclosure relate to a smart contactless card to detect real-time suspicious card readers or other fraudulent devices. Prior to a transaction, the smart contactless card detects suspicious card readers or fraudulent devices. An alert may be generated upon detection of any suspicious or fraudulent card reader. In some arrangements, the smart contactless card may utilize machine learning models or machine learning capabilities to detect suspicious card readers. The smart contactless card may pair with other smart contactless cards to detect and alert users to suspicious card readers or other fraudulent devices. The paired smart contactless cards may share information regarding suspicious card readers or fraudulent devices over a semi-autonomous data-sharing network. A vulnerability score may be generated and used to determine if a card reader or other payment device is suspicious.

Aspects described herein may allow for detecting fraudulent transaction requests using light sensors on smartcards. A computing device may receive, from a terminal, a request to approve a transaction. The request may comprise a timestamp corresponding to a time when the transaction was requested, a transaction type associated with the transaction, and an identification of a card associated with the transaction. The computing device may also receive, from one or more light sensors associated with the card, light data. The light data indicates an amount of ambient light during a time period when the transaction was requested. The computing device may determine, based on the transaction type, a model of expected ambient light. If the light data corresponds to the model, the computing device may send, based on the determination that the light data corresponds to the model, an approval of the request.

A method for transaction of digital asset is provided, including receiving digital asset transaction request information from a user terminal, verifying, by a blockchain network, the transaction request information, and when the verifying the transaction request information is completed, generating, by the blockchain network, information associated with the digital asset, in which the information associated with the digital asset includes a reputation of the digital asset.

Aspects described herein may allow for determining network membership to facilitate detecting fraudulent messages. A computing device may receive, from one or more terminals at a first location, a plurality of interactive messages during a pre-determined time period. Each interactive message may comprise personally identifiable information of a user. The computing device may store a first record and a second record that indicate interactive messages were received from a group of users in temporal proximity to each other at the first location and the second location respectively. The computing device may send a request to confirm users in the subset are members of a network and update a membership list based on a reply received from the user. If further messages are received from devices outside the membership list, an alert may be sent to alert the possibility of a fraudulent message.

Aspects described herein may allow for determining network membership to facilitate detecting fraudulent messages. A computing device may receive, from one or more terminals at a first location, a plurality of interactive messages during a pre-determined time period. Each interactive message may comprise personally identifiable information of a user. The computing device may store a first record and a second record that indicate interactive messages were received from a group of users in temporal proximity to each other at the first location and the second location respectively. The computing device may send a request to confirm users in the subset are members of a network and update a membership list based on a reply received from the user. If further messages are received from devices outside the membership list, an alert may be sent to alert the possibility of a fraudulent message.

A computer-implemented method of authenticating the identity of a user is provided, where the user is associated with a computer signature and is in possession of a cell phone. The method involves obtaining a current geographical location of the cell phone, determining if the computer signature is associated in a database with a stored geographical location of the phone, and, if the computer signature is associated in the database with a stored geographical location, comparing the stored geographical location to the current geographical location of the phone.