Patent ID: 11900385
Assignee: ACTIMIZE LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computerized-method for predicting a probability of fraudulent financial-account access, said computerized-method comprising:
a. building a Machine Learning (ML) sequence model by:
(i) retrieving one or more chronical-sequences of a preconfigured number of non-financial activities performed on each financial-account before conducting a financial activity during a latter preconfigured period and a label of fraud or non-fraud which is associated to each compatible financial-activity, from a customers-account database;
(ii) labeling each one or more chronical-sequences as fraud or non-fraud, based on the label of fraud or non-fraud which is associated to each compatible financial-activity that the chronical-sequence has preceded;
(iii) providing the labeled one or more chronical-sequences to a data-vectorization model to: (a) encode each non-financial activity in each chronical-sequence into a unique integer value and each chronical-sequence of the one or more chronical-sequences as a vector, thus yielding an array of vectors from the labeled one or more chronical-sequences; and (b) generate a dictionary of vector encodings from each non-financial activity type and compatible unique integer value;
(iv) exporting the dictionary of vector encodings to a persistent storage; and
(v) training the ML sequence model to predict fraud in a chronical-sequence vector, based on a sampling, wherein the sampling is the array of vectors;

b. implementing a forward propagation routine in an encapsulated environment that runs applications to mimic a process of the ML sequence model, wherein the forward propagation routine is mimicking processing of a chronical-sequence of a preconfigured number of non-financial activities sequence vector, layer by layer to generate a fraud probability score and using weights and biases which were extracted from each layer of the trained ML sequence model; and
c. exporting the extracted weights, biases to the persistent storage and converting the forward propagation routine to an executable for integration with a Fraud Management System, in a production environment,
wherein the Fraud Management System is operating the integrated executable to predict a probability of fraudulent financial-account access by providing a fraud probability score to each chronical-sequence of a preconfigured number of non-financial activities, provided in real-time.