A system and method includes soft-segment based rules optimization that can mitigate the overall false positives while maintaining 100% true positive detection. The soft clustering allows real-time re-assignment of an account to a dominate archetype behavior, as well as rule optimization based on a logical order with more relaxation on thresholds for the most inefficient rules is performed within each archetype. The rule optimization provides false positive reduction compared to a baseline rule system. The method can be used to reduce false positives for any rule-based detection system in which the same true positive detection is required.

Various methods, apparatuses/systems, and media for implementing a multi-tier intercommunication module (MTIM) for an automatic teller machine (ATM) to prevent fraudulent and forced transactions are provided. A processor within the MTIM verifies whether an anomaly withdrawal transaction is occurring based on analysis of a user's normal banking behavior. When anomaly transactions are detected, the processor transmits a notification to the user's registered Multi-Factor Authentication (MFA) device to verify whether the transaction is fraudulent. Additionally, the MTIM provides a continuous feedback loop of user location data when forced transactions have been triggered. Forced card transactions are allowed completion and the MTIM sends a custom security code (CSC) to the user's registered MFA mobile device. An application module, running on this MFA device, upon receiving this CSC will automatically send continuous push notifications on pre-determined time deltas containing location data of the user to a security module operated by a local authority.

Tracking account positions in a real-time payments system is provided. Each of a plurality of participants is linked to a funding agent account via an ACH network, thereby creating a plurality of linked participants. Each of the plurality of linked participants is provided access to a current prefunded position of the funding agent through the ACH network. An unsettled financial position of each of the plurality of participants is compared to the current prefunded position of the funding agent. A determination is made whether to perform a financial settlement for a corresponding one of the plurality of participants based on the comparison. The financial settlement for the corresponding participant is performed when it is determined that the unsettled financial position of the corresponding participant is not greater than the current prefunded position of the funding agent.

A system comprising: a check image Record (CIR) database configured to store, for each of a plurality of accounts, a CIR, wherein each CIR comprises feature information related to features extracted from a plurality of reference checks associated with the associated account of the plurality of accounts; memory configured to store instructions; and a processor coupled with the CIR database and the memory, the processor configured to run the instructions, which cause the processor to: receive an image of a test check associated with an account of the plurality of accounts, extract feature information from the image, compare the features with the feature information stored in the CIR database for the account associated with the test check, generate a fraud score, and update each CIR by determining.

A system, method and computer program product for computer based open innovation, includes an asset valuation device receiving asset information regarding tangible or non-tangible assets, and generating a valuation signal, based thereon; a self-executing code device receiving the valuation signal, and generating a self-executing code signal, based thereon; an air router device having both low band radio, and internet router channels for redundant internet communications, and a malicious code removal device for scrubbing malicious code from data received, receiving the valuation signal, and generating a node voting request signal, based thereon; a mesh network having node devices receiving the node voting request signal, and generating vote confirmation signals, based thereon; and computing devices connected to each of the respective node devices, and configured to perform non-fungible token (NFT) generation based on the assets, including tracking respective ownership and valuation of the assets, based on the asset information.

Disclosed are methods and systems for quantifying degrees of association between blockchain addresses in a weighted-linked database. The method may include: obtaining a node data set comprising one or more nodes and edges; associating a first node of the node data set with a first weight factor; identifying a first edge of the node data set, wherein the first edge comprises data indicating a source node address corresponding to the first node, a target node address corresponding to a second node of the one or more nodes in the node data set, and a first edge weight; determining a source value for the second node based on the first weight factor and the first edge weight; generating, a risk value for the second node based on the source value; and presenting, on a GUI, graphical depictions of the first node, the second node, and the first edge.

Electronic transfers of value may be secured via verification of locations of parties, and/or progressive trust building, while maintaining privacy of sensitive personal information of parties via separate communications by parties with a payment network server. Locations may be fixed geolocations, proximities, and/or positions relative to markers/beacons that are in motion. Transfers may be made from party to party, party to place and place to party, and/or party to a virtual point-of-sale at a place. Transferred items may be electronic funds in fiat and/or crypto currencies, Non-Fungible Tokens (NFTs)/data associated NFTs, electronic data, and/or securities/financial instruments, for example. Transactions may be multiparty and may comprise multiple exchanges of value.

A computing system can associate a customer device of a customer with a financial transaction record and the merchant, the financial transaction record indicative of a first purchase from the merchant by the customer, transmit a first query to the customer device prompting the customer to input information regarding an aspect of the first purchase, the first query including a description of a predetermined product parameter of the financial transaction record indicative of the first purchase from the merchant by the customer, authenticating, by the computing system, the first request by determining that the customer-input response to the first query corresponds to the established aspect of the first purchase in accordance with a predetermined accuracy threshold, and authorizing, by the computing system, connection of the customer device to the network provided by the merchant based at least in part on the first request being authenticated.

Disclosed are systems and methods for data breach identification. The method may include: generating virtual card number (VCN) data sets; storing the VCN data sets on a first database; receiving one or more compromised VCN data sets stored on a second database and obtained from a scan of unindexed websites; comparing the compromised VCN data sets with the VCN data set stored on the first database to determine whether the VCN data sets have been compromised; for each compromised VCN data set, training the recurrent neural network (RNN) to associate the compromised VCN data sets with one or more sequential patterns found within the compromised VCN data sets to generate a trained RNN; receiving a first VCN data set from the first database; determining whether the first VCN data set matches a compromised VCN data set; and transmitting a message indicating the determination to a user or provider device.

Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.