Pointpredictive, Inc.

Fair Isaac Corporation

Opera Solutions Usa, LLC

Gregory Gancarz

Timothy J Grace

Frank J McKenna

Michael J Kennedy

Scott Michael Zoldi

Yonghui Chen

Joseph P Milana

Linyu Yang

David Griegel

Bo Zhang

Jenny Zhang

Zuohua Zhang

Kathleen Marie Crowe

Danfeng Li

The present disclosure relates generally to a risk-based fraud identification and risk analysis system. For example, the system may receive application data from a first borrower user, determine a segment associated with the application data, apply application data to one or more machine learning (ML) models, and receive a score based at least in part upon output of the ML model.

Risk-based machine learning classifier

A computer-implemented method and system for visualizing card transaction fraud analysis is presented. Transaction data and account data related to one or more payment card accounts is stored in a database. The transaction data includes a fraud score. A computer processor generates one or more of a plurality of visualizations of activity of at least one suspicious account from the one or more payment card accounts for display in a graphical user interface, each of the plurality of visualizations providing at least a graphical representation of the transaction data and which is selectable from a menu provided by the computer processor in the graphical user interface. The visualizations assist in case judgment of the one or more payment cards.

Visualization for payment card transaction fraud analysis

The present disclosure relates generally to a multi-layer fraud identification and risk analysis system. For example, the system may receive application data from a first borrower user, apply application data to one or more machine learning (ML) models, and receive a first score based at least in part upon output of the ML model that is associated with the first borrower user. The system may aggregate scores associated with multiple borrower users to a cumulative dealer user level. The aggregated first scores associated with the dealer user, as well as other corresponding application data, may be provided as input to a second ML model. Output from the second ML model may be associated with the dealer user as a second score.

Multi-layer machine learning classifier

The present disclosure relates generally to a multi-layer fraud identification and risk analysis system. For example, the system may receive a plurality of first scores associated with borrower users and a dealer user based at least in part upon output of the first ML model. The system may receive a request from a lender user device for a second score, where the dealer user and the lender user device are associated according to a correlative score. The plurality of applications and the correlative score may be used as input to the second ML model that quantifies the risk of the dealer user specifically to the lender user, based on attributes associated with the application data, dealer user, and/or lender user. Output from the second ML model may be provided to the lender user device.

Multi-layer machine learning classifier with correlative score

Risk-based machine learning classsifier

A system for automatically recommending treatments for delinquent accounts in collections is provided. The system includes one or more sub-models for analyzing and scoring delinquent accounts and comparing them to historical accounts to identify similar historical accounts. The system can select and recommend one or more treatments for the delinquent account based on treatments for the historical accounts that may have previously provided a favorable outcome for a similar account. The system may test the efficacy of new treatments and incorporate the results into the memory, thereby learning and adapting to new treatments and account behavior.

Recommender engine for collections treatment selection

A merchant profile builder (MPB) is provided. The complete merchant profiling process is split into three pieces: Data Preprocessing, Weekly Summary Calculation, and Merchant Profile Calculation. In the Data Preprocessing stage, authorization data and daily fraud data are processed on a weekly basis to yield: an authorization extract file and fraud authorization data, each of which are sorted by a merchant key. In the Weekly Summary Calculation stage, the files output by the Data Preprocessing stage are used as input to compile basic statistics for each merchant based only on data for one week. There are separate weekly summaries for authorizations and fraud authorizations. In the Merchant Profile Calculation stage, the weekly summaries from the 16 most recent weeks are combined together and averages are calculated and output a set of merchant profiles and a set of default profiles.

Method and apparatus for a merchant profile builder

Growing community of inventors

San Diego, CA, United States of America

Gregory Gancarz