| CPC G06Q 20/4016 (2013.01) [G06Q 20/36 (2013.01); G06Q 20/389 (2013.01); G06Q 40/03 (2023.01)] | 20 Claims |
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1. A computer-implemented method, comprising:
accessing, by a server system from a transaction database via a network, payment transaction data and cardholder risk data associated with a cardholder, the payment transaction data comprising a plurality of transaction variables associated with past payment transactions performed at one or more Point of Interaction (POI) terminals within a particular time window;
generating, by the server system, cardholder profile data associated with the cardholder based, at least in part, on the plurality of transaction variables and the cardholder risk data, the cardholder profile data comprising: (a) wallet transaction features associated with a set of crypto-wallets, (b) spend behavioral pattern features of the cardholder for a plurality of merchant terminals associated with a plurality of merchants, and (c) cardholder transaction features according to merchant risk profiles;
determining, by the server system, a plurality of account-level risk scores associated with the cardholder based, at least in part, on the cardholder profile data, the plurality of account-level risk scores determined by a plurality of trained machine learning models, the plurality of account-level risk scores comprising: (a) a wallet reload risk score, (b) an account reissuance risk score, and (c) a transaction channel risk score, the plurality of trained machine learning models comprising: (a) a wallet reload risk model, (b) an account reissuance risk model, and (c) a transaction channel risk model;
wherein determining the plurality of account-level risk scores comprises:
generating, by the server system using the wallet reload risk model, the wallet reload risk score associated with the cardholder and a crypto-wallet, the wallet reload risk score indicating a likelihood of the cardholder performing risky wallet transactions with the crypto-wallet in a subsequent time interval based, at least in part, on the wallet transaction features associated with the set of crypto-wallets;
generating, by the server system using the account reissuance risk model, the account reissuance risk score based, at least in part, on the spend behavioral pattern features of the cardholder, the account reissuance risk score indicating a probability of one or more payment cards of the cardholder to be reissued within the subsequent time interval, wherein the spend behavioral pattern features comprise one or more of: (a) merchant features based on common point of purchase (CPP) compromise risk scores of the plurality of merchants, (b) cardholder interaction features with the plurality of merchants classified into a set of merchant classes, (c) cardholder risk features, or (d) payment network related risk features; and
generating, by the server system using the transaction channel risk model, the transaction channel risk score associated with the cardholder based, at least in part, on the merchant risk profiles including one or more of a location and a processor of a risky merchant where a payment card number was stolen, the transaction channel risk score indicating a probability of the one or more payment cards of the cardholder being compromised while transacting with the risky merchant within a subsequent time period; and
transmitting, via the network by a recommendation engine associated with the server system, a recommendation message to an issuer server associated with the cardholder based, at least in part, on the plurality of account-level risk scores for performing an action in response to the recommendation message.
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