Granted Patent B2

US 12,619,920 · App. 17/877,533 · Granted May 5, 2026

Inventor: Ioannis Giokas (Jersey City, NJ)

Assignee: Verge Capital Limited

G06N20/20G06Q20/4016

Abstract

Claims (65)

1 . A method of training a machine-learning (ML) algorithm to detect and prevent exposure, the algorithm trained by a meta-model system comprising a memory device, the memory device comprising computer readable instructions and a processing device operatively coupled with the memory device for executing the computer readable instructions to cause the processing device to:

(i) access from the memory device an exposure meta-model comprising a real-time exposure detection and prevention engine and an interaction database comprising interaction exposure decision data and interaction exposure feedback data;

(ii) receive from at least one sub-system a sub-system best performing exposure model;

(iii) update the exposure meta-model in response to feedback data via a feedback loop from the at least one sub-system that is non-collocated with the meta-model system associated with the exposure meta-model transmitting its best performing exposure model trained on local transaction data to the meta-model system, and by using a plurality of machine learning techniques to ensemble a plurality of classification models, thereby resulting in a plurality of ensembling results and subsequently stacking the plurality of ensembling results in order to increase accuracy; and

(iv) transmit the updated exposure meta-model to the at least one sub-system, wherein the at least one sub-system is communicatively coupled to and non-collocated with the meta-model system and comprises a sub-system memory device comprising computer readable instructions and a sub-system processing device operatively coupled with the sub-system memory device for executing the computer readable instructions to cause the sub-system processing device to:

(i) receive the updated exposure meta-model transmitted from the meta-model system;

(ii) access a sub-system exposure model comprising a real-time exposure detection and prevention engine and a interaction database comprising interaction exposure decision data and interaction exposure feedback data; and

(iii) update, in real-time, the sub-system exposure model with the updated exposure meta-model transmitted from the meta-model system.

2 . The method of claim 1 , further comprising:

(a) receiving, by the at least one sub-system, new transaction data;

(b) applying, by the at least one sub-system, the real-time exposure detection and prevention engine to the new interaction data, thereby resulting in a new interaction exposure decision;

(c) communicating, by the at least one sub-system, the new interaction exposure decision to the interaction database; and

(d) communicating, by the at least one sub-system, new interaction exposure feedback data to the interaction database.

3 . The method of claim 2 , further comprising:

(e) training, by the at least one sub-system, the interaction exposure decision data and interaction exposure feedback data of the interaction database using a plurality of machine learning techniques, thereby resulting in trained data; and

(f) updating, by the at least one sub-system, the real-time exposure detection and prevention engine based at least in part on the trained data.

4 . The method of claim 3 , further comprising:

(g) identifying, by the at least one sub-system, a best performing exposure model; and

(h) transmitting, by the at least one sub-system, the best performing exposure model to the meta-model system.

5 . The method of claim 1 , wherein using the plurality of machine learning techniques comprises ensembling a plurality of classification models, thereby resulting in a plurality of ensembling results and subsequently stacking the plurality of ensembling results in order to increase accuracy.

6 . The method of claim 5 , wherein the plurality of classification models comprises at least one selected from the group consisting of a gradient boosting model, a random forest model, an isolation forest model, an isolation forest model alongside a multi-layer neural network model, and/or an isolation forest model alongside a genetic algorithm.

7 . A distributed adaptive learning transaction fraud detection and prevention system comprising:

(a) a meta-model system comprising a memory device comprising computer readable instructions and a processing device operatively coupled with the memory device for executing the computer readable instructions to cause the processing device to:

access from the memory device a fraud meta-model comprising a real-time fraud detection and prevention engine and a transaction database comprising transaction fraud decision data and transaction fraud feedback data;

receive from at least one sub-system a sub-system best performing fraud model;

update the fraud meta-model in response to feedback data via a feedback loop from the at least one sub-system that is non-collocated with the meta-model system associated with the fraud meta-model transmitting its best performing exposure model trained on local transaction data to the meta-model system; and

transmit the updated fraud meta-model to the at least one sub-system; and

(b) at least one sub-system communicatively coupled and non-collocated with the meta-model system comprising a memory device comprising computer readable instructions and a processing device operatively coupled with the memory device for executing the computer readable instructions to cause the processing device to:

(i) receive the updated fraud meta-model transmitted from the meta-model system;

(ii) access a sub-system fraud model comprising a real-time fraud detection and prevention engine and a transaction database comprising transaction fraud decision data and transaction fraud feedback data; and

(iii) update, in real-time, the sub-system fraud model with the updated fraud meta-model transmitted from the meta-model system.

8 . The system of claim 7 , wherein the at least one sub-system has a processing device for executing the computer readable instructions further to cause the processing device to:

(a) receive new transaction data;

(b) apply the real-time fraud detection and prevention engine to the new transaction data, thereby resulting in a new transaction fraud decision;

(c) communicate the new transaction fraud decision to the transaction database; and

(d) communicate new transaction fraud feedback data to the transaction database.

9 . The system of claim 8 , wherein the at least one sub-system has a processing device for executing the computer readable instructions further to cause the processing device to:

(a) train the transaction fraud decision data and transaction fraud feedback data of the transaction database using a plurality of machine learning techniques, thereby resulting in trained data; and

(b) update the real-time fraud detection and prevention engine based at least in part on the trained data.

10 . The system of claim 9 , wherein the at least one sub-system has a processing device for executing the computer readable instructions further to cause the processing device to:

(a) identify a best performing fraud model; and

(b) transmit the best performing fraud model to the meta-model system.

11 . The system of claim 7 , wherein using the plurality of machine learning techniques comprises ensembling a plurality of classification models, thereby resulting in a plurality of ensembling results and subsequently stacking the plurality of ensembling results in order to increase accuracy.

12 . The system of claim 11 , wherein the plurality of classification models comprises at least one selected from the group consisting of a gradient boosting model, a random forest model, an isolation forest model, an isolation forest model alongside a multi-layer neural network model, and/or an isolation forest model alongside a genetic algorithm.

13 . A method for distributed adaptive learning transaction fraud detection and prevention using a meta-model system comprising a memory device comprising computer readable instructions and a processing device operatively coupled with the memory device for executing the computer readable instructions to cause the processing device to perform a set of actions and a sub-system operatively coupled with the meta-model system comprising a memory device comprising computer readable instructions and a processing device operatively coupled with the memory device for executing the computer readable instructions to cause the processing device to perform a second set of actions, the method comprising:

(a) accessing, by the meta-model system from the memory device, a fraud meta-model comprising a real-time fraud detection and prevention engine and a transaction database comprising transaction fraud decision data and transaction fraud feedback data;

(b) receiving, by the meta-model system and from at least one sub-system, a sub-system best performing fraud model;

(c) updating, by the meta-model system, the fraud meta-model in response to feedback data via a feedback loop from the at least one sub-system that is non-collocated with the meta-model system associated with the fraud meta-model transmitting its best performing exposure model trained on local transaction data to the meta-model system;

(d) transmitting, by the meta-model system, the updated fraud meta-model to the at least one sub-system;

(e) receiving, by the at least one sub-system, the updated fraud meta-model transmitted from the meta-model system;

(f) accessing, by the at least one sub-system, a sub-system fraud model comprising a real-time fraud detection and prevention engine and a transaction database comprising transaction fraud decision data and transaction fraud feedback data; and

(g) updating, by the at least one sub-system in real-time, the sub-system fraud model with the updated fraud meta-model transmitted from the meta-model system.

14 . The method of claim 13 , further comprising:

(i) receiving, by the at least one sub-system, new transaction data;

(j) applying, by the at least one sub-system, the real-time fraud detection and prevention engine to the new transaction data, thereby resulting in a new transaction fraud decision;

(k) communicating, by the at least one sub-system, the new transaction fraud decision to the transaction database; and

(l) communicating, by the at least one sub-system, new transaction fraud feedback data to the transaction database.

15 . The method of claim 14 , further comprising:

(m) training, by the at least one sub-system, the transaction fraud decision data and transaction fraud feedback data of the transaction database using a plurality of machine learning techniques, thereby resulting in trained data; and

(n) updating, by the at least one sub-system, the real-time fraud detection and prevention engine based at least in part on the trained data.

16 . The method of claim 15 , further comprising:

(o) identifying, by the at least one sub-system, a best performing fraud model; and

(p) transmitting, by the at least one sub-system, the best performing fraud model to the meta-model system.

17 . The method of claim 13 , wherein using the plurality of machine learning techniques comprises ensembling a plurality of classification models, thereby resulting in a plurality of ensembling results and subsequently stacking the plurality of ensembling results in order to increase accuracy.

18 . The method of claim 17 , wherein the plurality of classification models comprises at least one selected from the group consisting of a gradient boosting model, a random forest model, an isolation forest model, an isolation forest model alongside a multi-layer neural network model, and/or an isolation forest model alongside a genetic algorithm.

Assignments (1)

Continuity (2)

References Cited (37)