US 10,650,128 B2Granted

Methods and systems for automatically configuring user authentication rules

AssigneeMastercard International Incorporated

InventorsDawid Nowak, Ashley Waldron, Laurie Nicoletti

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Abstract

Systems and processes for automatically configuring user authentication rules for each of a plurality of users for use in transactions. A neural network engine receives first party preferences data from a first party computer that includes user authentication requirement criteria associated with a plurality of transaction types, and receives at least two of user behavior data, user historical data, authenticator data associated with a mobile device of the user, and mobile device metadata. The neural network engine then generates an output value based on this data, transmits the output value to a score comparator for comparison to a required score specified by the first party, and receives feedback data from the score comparator when the output value is not within a tolerance of the required score. When the output value is within the tolerance, then the neural network engine generates user authentication rules recommendations and transmits them to the first party computer.

Claims8

Dependent claims

A method for automatically determining user authentication criteria, comprising:

receiving, by a neural network engine from a first party computer, first party preferences data comprising user authentication requirement criteria associated with a plurality of different transaction types, the user authentication criteria comprising a fraudulent transaction tolerance associated with each transaction type of the plurality of different transaction types;
receiving, by the neural network engine, authenticator data associated with a mobile device of a user, mobile device metadata, and at least one of user behavior data and user historical data;
generating, by the neural network engine, an output value based on the first party preferences data, the authenticator data associated with the mobile device of the user, the mobile device metadata, and at least one of the user behavior data and the user historical data;
transmitting, by the neural network engine to a score comparator, the output value for comparison to a required score specified by the first party;
receiving, by the neural network engine from the score comparator, feedback data when the output value is not within a tolerance of the required score;
generating, by the neural network engine, an updated output value based on the feedback data;
transmitting, by the neural network engine to the score comparator, the updated output value for comparison to the required score;
receiving, by the neural network engine from the score comparator, a match indication;
generating, by the neural network engine, user authentication rules recommendations associated with the user for each of the plurality of different transaction types;
and
transmitting, by the neural network engine, the user authentication rules recommendations to the first party computer.

8 more elements

A user authentication rules recommendation system comprising:

a user mobile device comprising at least one authenticator;
a first party computer;
and
a rules recommender computer system operably connected to the first party computer and in communication with the user mobile device, the rules recommender computer system comprising:
a neural network engine operably connected to a plurality of classifiers;
a score comparator operably connected to the neural network engine;
and
a first party rules engine operably connected to the score comparator;
and wherein the neural network engine of the rules recommendation computer system is configured to:
receive first party preferences from the first party computer, the first party preferences data comprising user authentication requirement criteria associated with a plurality of different transaction types, the user authentication criteria comprising a fraudulent transaction tolerance associated with each transaction type of the plurality of different transaction types;
receive authenticator data associated with a mobile device of the user, mobile device metadata, and at least one of user behavior data and user historical data;
generate an output value based on the first party preferences data, the authenticator data associated with the mobile device of the user, the mobile device metadata, and at least one of the user behavior data and the user historical data;
transmit the output value to the score comparator for comparison to a required score specified by the first party;
receive feedback data from the score comparator when the output value is not within a tolerance of the required score;
generate an updated output value based on the feedback data;
transmit the updated output value to the score comparator for comparison to the required score;
receive a match indication from the score comparator;
generate user authentication rules recommendations associated with the user for each of the plurality of different transaction types;
and
transmit the user authentication rules recommendations to the first party computer.

17 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/787,097Filed 18 Oct 2017
Published: US 2019/0114404 A118 Apr 2019
Granted: 12 May 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification15 codes

G06F 21/316
User authentication: by observing the pattern of computer usage, e.g. typical user behaviour
G06F 21/31
Authentication, i.e. establishing the identity or authorisation of security principals: user authentication
G06N 3/02
Computing arrangements based on biological models: neural networks
G06N 3/042
Architecture, e.g. interconnection topology: knowledge-based neural networks; Logical representations of neural networks