US 2026/0289568 A1Published application

Stacking of heterogeneous machine learning models for detection of fraudulent transactions

AssigneeActimize Ltd.

InventorsSaurab GODSE, Ankush SHRIKHANDE, Amol KOKATE

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Abstract

A system is adapted to automatically detect fraudulent transactions. The system includes a fraud management computer system programmed for: receiving a plurality of transactions, some legitimate and some fraudulent; with the transactions and a K-fold training module, training a heterogeneous group of machine learning models to classify the transactions as legitimate or fraudulent, where the K-fold training module generates, for each machine learning model, a mean and a standard deviation; with the means and standard deviations, determining a weight for each machine learning model; and, with the weights for each machine learning model, defining a meta machine learning model to classify the transactions as legitimate or fraudulent; receiving a real-time transaction and, with the trained machine learning models, generating determinations of whether it is legitimate or fraudulent; with the meta machine learning model and the determinations, classifying the real-time transaction as legitimate or fraudulent; and, if fraudulent, generating an alert.

Claims20

Dependent claims

A system adapted to automatically detect fraudulent transactions, the system comprising:

a fraud management computer system having a processor and a non-transitory computer readable medium operably coupled thereto, the fraud management computer system comprising a transactions database, the non-transitory computer-readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise:
over a first period of time, with the transactions database, receiving a plurality of transactions, each transaction comprising a plurality of features, wherein some of the transactions are legitimate and some of the transactions are fraudulent;
with the plurality of transactions and a K-fold training module, training a heterogeneous plurality of machine learning models to classify the transactions as legitimate or fraudulent, wherein each machine learning model of the heterogeneous plurality of machine learning models is of a different type than other machine learning models of the heterogeneous plurality of machine learning models,
wherein each respective machine learning model of the heterogeneous plurality of machine learning models generates a respective weight for each feature of the plurality of features;
wherein the K-fold training module generates, for each respective machine learning model of the heterogeneous plurality of machine learning models, a mean and a standard deviation, wherein the mean and the standard deviation are of precision values or variability values of the respective machine learning model;
with the mean and the standard deviation for each respective machine learning model of the heterogeneous plurality of machine learning models, determining a weight of the respective machine learning model;
with the weights for each respective machine learning model, and the classifications made by each respective machine learning model, defining a meta machine learning model to classify the transactions as legitimate or fraudulent;
receiving a real-time transaction;
with the trained machine learning models of the heterogeneous plurality of machine learning models, generating a plurality of determinations of whether the real-time transaction is legitimate or fraudulent;
with the meta machine learning model and the plurality of determinations, classifying the real-time transaction as legitimate or fraudulent;
and
if the real-time transaction is fraudulent, generating an alert for the real-time transaction;
and
displaying the alert to a user.

11 more elements

A computer-implemented method, comprising, with a fraud management computer system having a processor and a non-transitory computer readable medium operably coupled thereto, the fraud management computer system comprising a transactions database:

over a first period of time, with the transactions database, receiving a plurality of transactions, each transaction comprising a plurality of features, wherein some of the transactions are legitimate and some of the transactions are fraudulent;
with the plurality of transactions and a K-fold training module, training a heterogeneous plurality of machine learning models to classify the transactions as legitimate or fraudulent, wherein each machine learning model of the heterogeneous plurality of machine learning models is of a different type than other machine learning models of the heterogeneous plurality of machine learning models,
wherein each respective machine learning model of the heterogeneous plurality of machine learning models generates a respective weight for each feature of the plurality of features;
wherein the K-fold training module generates, for each respective machine learning model of the heterogeneous plurality of machine learning models, a mean and a standard deviation, wherein the mean and the standard deviation are of precision values or variability values of the respective machine learning model;
with the mean and the standard deviation for each respective machine learning model of the heterogeneous plurality of machine learning models, determining a weight of the respective machine learning model;
with the weights for each respective machine learning model, and the classifications made by each respective machine learning model, defining a meta machine learning model to classify the transactions as legitimate or fraudulent;
receiving a real-time transaction;
with the trained machine learning models of the heterogeneous plurality of machine learning models, generating a plurality of determinations of whether the real-time transaction is legitimate or fraudulent;
with the meta machine learning model and the plurality of determinations, classifying the real-time transaction as legitimate or fraudulent;
and
if the real-time transaction is fraudulent, generating an alert for the real-time transaction;
and
displaying the alert to a user.

10 more elements

Dependencies are inferred from the claim text.