Enhancing explainability of risk scores by generating human-interpretable reason codes

Patent metadata

Field	Value
Publication number	US-2022005041-A1
Application number	US-202016920642-A
Country	US
Kind code	A1
Filing date	Jul 3, 2020
Priority date	Jul 3, 2020
Publication date	Jan 6, 2022
Grant date	—

Abstract

This disclosure relates to systems and methods for identifying risky merchants associated with an electronic payment service. In some implementations, a risk assessment system receives a set of features indicative of one or more risks posed by a merchant enrolled in the electronic payment service, where each feature of the set of features indicative of one or more financial attributes of the merchant. The risk assessment system determines a risk score for the merchant based on the set of features using a trained machine learning model, determines a Shapely additive explanation (SHAP) score for each feature of the set of features, and then divides the set of features into multiple groups of features based on a mapping between the features and their respective indicated financial attributes. The risk assessment system determines a pseudo-SHAP score for each group of features by summing the SHAP scores determined for the features in the respective group of features, and then determines a financial risk or a risk of fraud posed by the merchant based on the determined risk score and the pseudo-SHAP scores.

First claim

What is claimed is:

1 . A method for identifying risky merchants associated with an electronic payment service, the method performed by a risk assessment system and comprising:
receiving a set of features indicative of one or more risks posed by a merchant enrolled in the electronic payment service, each feature of the set of features indicative of one or more financial attributes of the merchant; determining a risk score for the merchant based on the set of features using a trained machine learning model; determining a Shapely additive explanation (SHAP) score for each feature of the set of features; dividing the set of features into multiple groups of features based on a mapping between each feature and the corresponding one or more financial attributes; determining a pseudo-SHAP score for each group of features by summing the SHAP scores determined for the features in the respective group of features; and determining a financial risk or a risk of fraud posed by the merchant based on the determined risk score and the pseudo-SHAP scores.

2 . The method of claim 1 , wherein the one or more financial attributes includes one or more of a duration of time between chargebacks to the merchant, a number of chargebacks to the merchant during a time period, a dollar amount of each chargeback to the merchant during the time period, an amount of outstanding debt of the merchant, a number of missed or insufficient payments by the merchant during a time period, a number of credit card authorization declines for the merchant during the time period, a credit score of the merchant, a length of credit history of the merchant, an amount of credit available to the merchant, a type of business handled by the merchant, or a type of customers associated with the merchant.

3 . The method of claim 1 , wherein the machine learning model is trained using one or more historical sets of features associated with the merchant.

4 . The method of claim 1 , wherein each group of features corresponds to a different category of financial attributes.

5 . The method of claim 1 , wherein the mapping is based on similarities between the attributes indicated by each feature of the set of features.

6 . The method of claim 1 , further comprising:
determining an impact value for each group of features, the impact value indicating a degree to which the respective group of features contributed to the risk score.

7 . The method of claim 1 , further comprising:
determining a weighting factor for each feature of the set of features based at least in part on the one or more financial attributes indicated by the respective feature; and applying the weighting factors to one or more of the features prior to determining the risk score.

8 . The method of claim 1 , further comprising:
providing the risk score, the pseudo-SHAP scores determined for the groups of features, and one or more financial transactions of the merchant to one or more human risk analysts associated with the electronic payment service.

9 . The method of claim 8 , further comprising:
receiving, from the one or more human risk analysts, an estimated accuracy of the risk score based at least in part on the pseudo-SHAP scores.

10 . A risk assessment system for identifying risky merchants enrolled in an electronic payment service, the risk assessment system associated with the electronic payment service and comprising:
one or more processors; and a memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the risk assessment system to perform operations comprising:
receiving a set of features indicative of one or more risks posed by a merchant enrolled in the electronic payment service, each feature of the set of features indicative of one or more financial attributes of the merchant;
determining a risk score for the merchant based on the set of features using a trained machine learning model;
determining a Shapely additive explanation (SHAP) score for each feature of the set of features;
dividing the set of features into multiple groups of features based on a mapping between each feature and the corresponding one or more financial attributes;
determining a pseudo-SHAP score for each group of features by summing the SHAP scores determined for the features in the respective group of features; and
determining a financial risk or a risk of fraud posed by the merchant based on the determined risk score and the pseudo-SHAP scores.

11 . The risk assessment system of claim 10 , wherein the one or more financial attributes includes one or more of a duration of time between chargebacks to the merchant, a number of chargebacks to the merchant during a time period, a dollar amount of each chargeback to the merchant during the time period, an amount of outstanding debt of the merchant, a number of missed or insufficient payments by the merchant during a time period, a number of credit card authorization declines for the merchant during the time period, a credit score of the merchant, a length of credit history of the merchant, an amount of credit available to the merchant, a type of business handled by the merchant, or a type of customers associated with the merchant.

12 . The risk assessment system of claim 10 , wherein the machine learning model is trained using one or more historical sets of features associated with the merchant.

13 . The risk assessment system of claim 10 , wherein each group of features corresponds to a different category of financial attributes.

14 . The risk assessment system of claim 10 , wherein the mapping is based on similarities between the attributes indicated by each feature of the set of features.

15 . The risk assessment system of claim 10 , wherein execution of the instructions causes the risk assessment system to perform operations further comprising:
determining an impact value for each group of features, the impact value indicating a degree to which the respective group of features contributed to the risk score.

16 . The risk assessment system of claim 10 , wherein execution of the instructions causes the risk assessment system to perform operations further comprising:
determining a weighting factor for each feature of the set of features based at least in part on the one or more financial attributes indicated by the respective feature; and applying the weighting factors to one or more of the features prior to determining the risk score.

17 . The risk assessment system of claim 10 , wherein execution of the instructions causes the risk assessment system to perform operations further comprising:
providing the risk score, the pseudo-SHAP scores determined for the groups of features, and one or more financial transactions of the merchant to one or more human risk analysts associated with the electronic payment service.

18 . The risk assessment system of claim 10 , wherein execution of the instructions causes the risk assessment system to perform operations further comprising:
receiving, from the one or more human risk analysts, an estimated accuracy of the risk score based at least in part on the pseudo-SHAP scores.

19 . An apparatus for identifying risky merchants enrolled in an electronic payment service, the apparatus associated with the electronic payment service and comprising:
means for receiving a set of features indicative of one or more risks posed by a merchant enrolled in the electronic payment service, each feature of the set of features indicative of one

Assignees

• Intuit Inc

Inventors

• Chang Eva Diane
• Bosnjakovic Dusan

Classifications

• G06Q20/4016 Primary

  involving fraud or risk level assessment in transaction processing · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06F18/2113

  by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation · CPC title

• G06Q20/02

  involving a neutral party, e.g. certification authority, notary or trusted third party [TTP] · CPC title