Systems and methods for providing risk determination in a crypto currency transaction include receiving, through a network via a broadcast by a first payer device, a first crypto currency transaction that includes a first payee public address. A first request for a determination of risk associated with the first crypto currency transaction is then identified in the first crypto currency transaction, with the first request including risk criteria. A first payee involved in the first crypto currency transaction is then identified using the first payee public address, and first payee risk information is accessed via at least one external risk information database based on the identification of the first payee. If it is determined that the first payee risk information satisfies the at least one risk criteria in the first request, the first crypto currency transaction is provided for addition to a block in a crypto currency public ledger.

A method for fraud modeling based on shared computing device usage includes: storing transaction data entries, each including a transaction date and/or time, account identifier, and device identifier associated with a computing device; receiving a transaction message for a payment transaction, the transaction message including a specific device identifier, primary account number, and additional transaction data; identifying transaction data entries where the included device identifier corresponds to the specific device identifier; determining a fraud risk rating based on a number of unique account identifiers included in the identified transaction data entries over a predetermined period of time; and transmitting the transaction message and the determined fraud risk rating to a financial institution associated with the primary account number.

A method of tracing messages through a network of nodes is provided, the method comprising receiving message information corresponding to a first outbound message, the message information comprising a first source identifier and a first destination identifier and determining whether the first source identifier is associated with a set of messages in a storage unit, whereby when the first source identifier is associated with a set of messages, the method comprises producing a trace request, the trace request comprising the first destination identifier and an identifier identifying the set of messages associated with the first source identifier.

A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.

Systems for item validation and image evaluation are provided. In some examples, a system may receive an instrument and associated data. The instrument may be received and at least one of a bill pay profile and a user profile may be retrieved. The bill pay profile and user profile may each include a plurality of previously processed instruments that have been determined to be valid and/or authentic. The instrument may be compared to the plurality of previously processed instruments to determine whether one or more elements of the instrument being evaluated match one or more corresponding elements of the plurality of previously processed instruments. Matching or non-matching elements may be identified. In some examples, one or more user interfaces may be generated displaying the instruments and including any highlighting or enhancements identifying matching or non-matching elements.

A system for “tagging” funds identified on a blockchain and associating a weight value therewith. The tagging profile is developed into a propagation profile wherein weight values are inherited from the tagging profile s. Propagation profile funds may be diluted by combining with non-tagged funds, similar to how ink dilutes through water. A spending history of funds of interest is developed based on replaying the funds of interest against the global transaction history of the blockchain. It is determined whether the spending history intersects with the propagation profile, thus determining how closely the two sources of funds are economically to one another. Intersection triggers actions including alert notifications or transfer of funds on the blockchain.

Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.

A control method is executed by a first node holding a first distributed ledger in which a first blockchain is managed, and includes: obtaining, from a second node, a second blockchain managed by a second distributed ledger held by the second node, and comparing the second blockchain with the first blockchain; updating the first blockchain by adding the greater of at least one first different block contained in the first blockchain but not the second blockchain and at least one second different block contained in the second blockchain but not the first blockchain, after at least one common block, and adding at least one additional block containing at least one instance of transaction data contained in the lesser; and determining whether at least two instances of transaction data each containing at least two instances of contract information that conflict with each other are contained in the updated first blockchain.

An improved method and apparatus for determining if a financial transaction is fraudulent is described. The apparatus in one embodiment collects transactions off of a rail using promiscuous listening techniques. The method uses linear programming algorithms to tune the rules used for making the determination. The tuning first simulates using historical data and then creates a matrix of the rules that are processed through the linear programming algorithm to solve for the variables in the rules. With the updated rules, a second simulation is performed to view the improvement in the performance. The updated rules are then used to evaluate the transactions for fraud.