Guidance engine: an automated system and method for providing financial guidance

Patent metadata

Field	Value
Publication number	US-11023967-B1
Application number	US-201916680793-A
Country	US
Kind code	B1
Filing date	Nov 12, 2019
Priority date	Nov 12, 2019
Publication date	Jun 1, 2021
Grant date	Jun 1, 2021

Abstract

Aspects described herein may allow for receiving, by a detection server, a plurality of configuration parameters, wherein each configuration parameter includes a type of a risk and an associated level of the risk, with a corresponding automated remediation action for each configuration parameter. A remediation management framework authenticates the detection server for access to the remediation management framework and initiates a scanning of a system of interest, based on the plurality of configuration parameters, by the detection server, to identify one or more risk findings. The remediation management framework receives the identified one more risk findings; and matches each of the one or more risk findings with the plurality of configuration parameters, which then triggers by the remediation management framework, the corresponding automated remediation action associated with each of the one or more risk findings.

First claim

What is claimed is:

1. A system comprising:
a guidance user interface;
a data integration engine comprising:
one or more first processors; and
first memory storing first instructions that, when executed by the one or more first processors, cause the data integration engine to:
receive, via the guidance user interface, first input data associated with a user, wherein the first input data associated with the user comprises data corresponding to a user's monthly expenses, monthly income, emergency fund level, or high interest debt level;
receive, from one or more external data servers, second input data associated with the user, wherein the second input data associated with the user comprises data corresponding to a user's monthly expenses, monthly income, emergency fund level, or high interest debt level and wherein at least of a portion of the second input data is in a non-compliant format;
convert the non-compliant format of the portion of the second input data into a compliant format;
validate the converted portion of the second input data by generating a plurality of test cases to test compliance of the converted portion of the second input data and applying the plurality of test cases to the converted portion;
optimize the second input data by integrating the first input data into the second input data;
generate, based on the optimized second input data, first output data associated with an estimated committed monthly expenses of the user and an estimated monthly income of the user, wherein the estimated committed monthly expenses are associated with a total monthly fixed expense incurred by the user to cover essential needs, and the estimated monthly income comprises a gross, a net and a discretionary income;
generate, based on the optimized second input data, second output data associated with an estimated emergency fund level of the user and an estimated high interest debt level of the user, wherein the estimated emergency fund level is based on an amount of liquid assets available to cover the estimated committed monthly expenses, and wherein the estimated high interest debt level is associated with a loan with an interest rate above a pre-set threshold; and
generate, based on the optimized second input data, third output data associated with a confidence score, wherein the third output data comprises:
data indicating a variability score for each of the estimated committed monthly expenses, the estimated monthly income, the estimated emergency fund level, and the estimated high interest debt level, wherein the variability score is based on a ratio of an interquartile range (IQR) or standard deviation to a value, and wherein the value is the estimated committed monthly expenses, the estimated monthly income, the emergency fund level or the high interest debt level; and
data indicating a reasonableness score for each of the estimated committed monthly expenses, the estimated monthly income, the estimated emergency fund level, and the estimated high interest debt level; and

a decision tree engine comprising:
one or more second processors; and
second memory storing second instructions that, when executed by the one or more second processors, cause the decision tree engine to:
receive, from the data integration engine, the first output data associated with the estimated committed monthly expenses and the estimated monthly income, the second output data associated with the estimated emergency fund level and the estimated high interest debt level, and the third output data associated with a confidence score;
determine, based on the first output data, the second output data, and the third output data, a financial action for the user, wherein determining the financial action determines results for each branch of a decision tree that associates the financial action with combinations of values of the estimated committed monthly expenses, the estimated monthly income, the estimated emergency fund level, and the estimated high interest debt level; and
cause, via the guidance user interface, an output of the financial action, the estimated committed monthly expenses, the estimated monthly income, the estimated emergency fund level, the estimated high interest debt level, and the confidence score.

2. The system of claim 1 , wherein the first instructions cause the data integration engine to generate the plurality of test cases by generating test data configured to test a range of edge result cases associated with the converted portion of the optimized second input data.

3. The system of claim 1 , wherein the optimized second input data comprises one or more of the following:
user augmented data;
merchant category code;
credit bureau data;
geographic benchmark estimates;
a cost of living estimate; or
a housing cost estimate.

4. The system of claim 1 , wherein the second output data comprises one or more of the following:
an emergency fund savings goal based on a monetary value of one, three, or six months of committed monthly expenses;
an emergency fund shortfall based on an amount the user needs to save to reach the emergency fund savings goal;
an emergency fund save rate based on as a percentage of the discretionary income the user allocates to the emergency fund level; or
an emergency fund timescale based on a number of months to achieve the emergency fund savings goal.

5. The system of claim 1 , wherein the variability score indicates a variation, for each of the estimated committed monthly expenses, the estimated monthly income, the estimated emergency fund level, and the estimated high interest debt level, over time or between two or more sources of the first input data and the optimized second input data.

6. The system of claim 1 , wherein the first instructions that, when executed by the one or more first processors, cause the data integration engine to determine the financial action for the user by:
generating a twelve-cell matrix based on the estimated high interest debt level of (1) none, (2) moderate, and (3) high, and the estimated emergency fund level of (1) less than one month of the estimated committed monthly expenses, (2) less than three months of the estimated committed monthly expenses, (3) less than six months of the estimated committed monthly expenses, and (4) greater than six months of the estimated committed monthly expenses.

7. The system of claim 1 , wherein the first instructions that, when executed by the one or more first processors, cause the data integration engine to generate the third output data by:
generating the reasonableness score based on data indicating a cost of living data in a location associated with the user and data indicating a marital status of the user.

8. A method comprising:
receiving, by a data integration engine and via a guidance user interface, first input data associated with a user, wherein the first input data associated with the user comprises data corresponding to a user's monthly expenses, monthly income, emergency fund level, or high interest debt level;
receiving, by the data integration engine and from one or more external data servers, second input data associated with the user, wherein the second input data associated with the user comprises data corresponding to a user's monthly expenses, monthly income, emergency fund level, or high interest debt level and wherein at least of a portion of the second input data is in a non-compliant format;
converting, by the data integration engine, the non-compliant format of the portion of the second input data into a compliant format;
validating, by the data integration engine, the converted portion of the second input data by generating a plurality of test cases to test compliance of the converted portion of the sec

Assignees

• Capital One Services Llc

Inventors

• Schlesinger Katharine
• Rush John
• Yang Zheyu
• Davis Matthew

Classifications

• G06F16/258 Primary

  Data format conversion from or to a database · CPC title

• G06Q40/03 Primary

  Credit; Loans; Processing thereof · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N5/003

  Physics · mapped topic

• G06Q40/025 Primary

  Physics · mapped topic