Systems and methods for block rate matching when updating a machine learning model based fraud detection system

A system, comprising:

• a transaction system comprising a transaction data store, a transaction manager, a fraud detection computer system comprising a first machine learning (ML) model, and a first processor and a first memory storing executable instructions that when executed by the first processor causes the first processor to perform operations, comprising:
• continuously receiving a first set of transactions from a network,
• receiving from a computer system of a merchant a threshold,
• storing the threshold and a first block rate,
• storing the first set of transactions for the merchant in the transaction data store,
• receiving, via the transaction manager, a first transaction associated with the merchant from the network, and
• sending first parameters associated with the first transaction to the fraud detection computer system, and adding the received first transaction to the first set of transactions stored in the transaction data store;
• the fraud detection computer system comprising a second processor and second memory storing executable instructions that when executed by the second processor causes the second processor to perform operations, comprising:
• generating a score for the received first transaction utilizing the received first parameters and the first ML model and denying or approving the first transaction by comparing the score to the threshold;
• a communication interface connected to the transaction system, the fraud detection computer system and a block rate matching system;
• the block rate matching system comprising a fraud model testing manager, a block rate testing manager, a third processor and a third memory storing executable instructions that when executed by the third processor causes the third processor to perform operations, comprising:
• storing a second ML model, and, via the fraud model testing manager:
• accessing, via the fraud model testing manager and over the communication interface, the first set of transactions in the transaction data store, generating, via the fraud model testing manager, a plurality of candidate thresholds and scoring, via the fraud model testing manager for each candidate threshold in the plurality of candidate thresholds, each transaction in the set of transactions, utilizing, via the fraud model testing manager, the second ML model and determining, via the fraud model testing manager, whether each transaction in the set of transactions would be approved or denied,
• accessing, via the fraud model testing manager over the communication interface, transactions of the received first set of transactions received after the plurality of candidate thresholds were generated, applying, via the fraud model testing manager, each of the generated candidate thresholds to transactions received after the plurality of candidate thresholds were generated, and determining, via the fraud model testing manager for each of the plurality of candidate thresholds, a second block rate, by dividing a number of transactions that were blocked by a number of received transactions,
• determining, via the fraud model testing manager, an adjusted threshold from the plurality of candidate thresholds based on the first block rate and the second block rate, and
• communicating, via the block rate testing manager over the communication interface, the second model and the adjusted threshold to the fraud detection computer system;
• wherein the first memory stores further executable instructions that when executed by the first processor causes the first processor to perform further operations, comprising:
• receiving, via the transaction manager, a second transaction associated with the merchant from the network, sending, via the transaction manager, second parameters associated with the second transaction to the fraud detection computer system, and storing, via the transaction manager, the received second transaction in the transaction data store;
• and
• wherein the second memory stores further executable instructions that when executed by the second processor causes the second processor to perform further operations, comprising:
• replacing, the first ML model with the communicated second ML model and the threshold with the communicated adjusted threshold;
• and
• generating a score for the received second transaction utilizing the received second parameters and the communicated second ML model, and denying or approving the second transaction by comparing the score generated utilizing the second parameters and the communicated second ML model to the communicated adjusted threshold.