Payment Network Data Trees for Backward Threat Sequencing
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Solution Overview
Problem
Existing threat detection algorithms in payment networks are limited by the need to process transaction data in a linear time-ordered sequence, which restricts the ability to handle large volumes of transactions and prevents scaling and efficient data tree generation.
Innovation Solution
A method to generate data trees by storing transactions in a backwards direction, allowing un-ordered transaction data to be sequenced correctly within the tree, enabling high-rate processing of large volumes and complex data structures for threat detection algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If data trees are built from a linear time ordered stream of received transaction data, then the data tree structure maintains correct temporal sequencing, but the system cannot process large volumes of transactions at high rate and cannot scale in a distributed manner
Solution Approach 1:
The patent inverts the traditional approach by building data trees backwards in time rather than forwards. Instead of starting from the earliest transaction and adding subsequent transactions chronologically, the system starts from the current time and adds transactions by tracing backwards through the transaction stream. This inversion enables parallel processing and distributed scaling while maintaining temporal accuracy through explicit timestamp validation at each insertion point.
Solution Approach 2:
The system performs preliminary actions by pre-processing transaction data into a format suitable for backward tree construction, and by pre-establishing the temporal context needed for correct sequencing. Transactions are prepared with metadata and timestamps that enable efficient backward insertion without requiring sequential processing of the entire transaction history.
2Loss of information
If the entire SQL database is used to generate data trees, then complete transaction history is available for analysis, but the process is costly in terms of time and computational resources
Solution Approach 1:
The patent extracts only the necessary transaction data needed for the current data tree being constructed, rather than processing the entire SQL database. By extracting and processing transactions in a targeted manner based on the specific tree being built and the backward insertion approach, the system maintains completeness of relevant transaction history while dramatically reducing computational overhead and generation time.
3Ease of operation
If data trees are built dependent on when transaction data is received, then the system can process transactions as they arrive, but the payment network cannot scale up the generation and building of data tree networks in a distributed manner
Solution Approach 1:
By inverting the construction direction to build trees backwards from the present, the patent enables multiple distributed systems to independently construct trees from their local transaction views without requiring centralized coordination. Each distributed node can process transactions as they arrive and build local backward trees, which can then be merged or queried independently, enabling scalable distributed architecture while maintaining real-time processing.
Data Source
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AI summary
The present invention relates to a computer implemented method of generating a data tree 240 for use with a threat detection algorithm 155 configured to detect threats in a payment network 110. The method comprises receiving, and storing in a data tree 240, first transaction data 220 comprising details of a source account 221 and details of a destination account 222 of a first transaction 220. At a later point in time, second transaction data 230 comprising details of a source account 231 and details of a destination account 232 of a second transaction is received. It is determined whether the destination account 232 of the second transaction is the same account as the source account 221 of the first transaction, and if so the source account 231 of the second transaction as stored as a parent node to the first transaction data 220 in the data tree 240. The method further comprises inputting, into a threat detection algorithm 155, the data tree 240.