# US 10,504,028 B1 · Techniques to use machine learning for risk management

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- Number: US 10,504,028 B1
- Document ID: US10504028B1
- Status: Granted
- Kind: B1
- Application number: 16/393,070
- Filed: 2019-04-24
- Granted: 2019-12-10
- Earliest claimed: 2019-04-24
- Assignees: Capital One Services, LLC
- Applicants: Capital One Services, LLC
- Inventors: Yvette Jackson; Jeffrey Capelli; Jennelle Spurlock
- Web page: https://patentgrep.com/patent/US10504028B1

## CPC

- G06N 5/048: Fuzzy inferencing
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06F 16/9532: Query formulation
- G06N 20/00: Machine learning
- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Various embodiments are generally directed to techniques to build and train a machine learning model with features of which at least one feature corresponds to risk indicia and at least one other feature corresponds to a data source. These features, in general, provide data (e.g., values) indicating a degree of relevance between a particular record and a risk assessment of that particular record's subject matter. User refinement in the form of user selections and other interactions with the particular record and other records provide insights into proper risk management.

## Claims

### Claim 1

An apparatus, comprising:
a processing circuit; and
logic stored in computer memory and executed on the processing circuit, the logic operative to cause the processing circuit to:
perform feature extraction on records across a plurality of datasets, features corresponding to the feature extraction comprise at least one feature associated with risk indicia and at least one feature associated with a particular data source of at least one of the plurality of datasets;
build a machine learning model based upon data generated during the feature extraction on the records, the machine learning model comprising risk assessment data for the records across the plurality of datasets;
identify a set of records from the records across the plurality of datasets, each record of the set of records having risk assessment data that exceeds a baseline threshold;
return at least one record of the set of records in response to search queries; and
modify the risk assessment data in the machine learning model in response to user selections corresponding to the at least one returned record.

### Claim 2 (depends on 1)

The apparatus of claim 1 further comprising logic operative to modify content of the at least one returned record in response to user selections corresponding to the at least one returned record.

### Claim 3 (depends on 1)

The apparatus of claim 1 further comprising logic operative to process, during a search query, a user selection of the at least one returned record.

### Claim 4 (depends on 3)

The apparatus of claim 3 further comprising logic operative to update a risk assessment value of the at least one returned record.

### Claim 5 (depends on 4)

The apparatus of claim 4 further comprising logic operative to modify a search query and instruct a search engine to perform a search on the modified search query.

### Claim 6 (depends on 5)

The apparatus of claim 5 further comprising logic operative to perform at least one of adding or removing search terms to the search query to generate the modified search query.

### Claim 7 (depends on 6)

The apparatus of claim 6 further comprising logic operative to automatically replace the search query with the modified search query.

### Claim 8 (depends on 1)

The apparatus of claim 1 further comprising logic operative to process backend validation data to update the risk assessment data of the machine learning model.

### Claim 9 (depends on 1)

The apparatus of claim 1 further comprising logic operative to identify groups of relevant datasets based upon the risk assessment data in the machine learning model.

### Claim 10

A computer-implemented method executed on a processing circuit, comprising:
generating feature extraction data from records across a plurality of datasets, features corresponding to the feature extraction data comprise at least one feature associated with risk indicia and at least one feature associated with a particular data source of at least one of the plurality of datasets;
building a machine learning model based upon the feature extraction data on the records, the machine learning model comprising risk assessment data for the records across the plurality of datasets;
identifying a set of records from the records across the plurality of datasets, each record of the set of records having risk assessment data that exceeds a baseline threshold;
returning at least one record of the set of records in response to search queries; and
modifying the risk assessment data in the machine learning model in response to user selections corresponding to the at least one returned record.

### Claim 11 (depends on 10)

The computer-implemented method of claim 10 further comprising processing, within search results, a user selection of a record of the set of records wherein the user selection indicates a degree of relevance upon the record.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11 further comprising updating a risk assessment value of the record.

### Claim 13 (depends on 12)

The computer-implemented method of claim 12 further comprising modifying the search query and instructing a search engine to perform a search on the modified search query.

### Claim 14 (depends on 13)

The computer-implemented method of claim 13 further comprising performing at least one of adding or removing search terms to the search query to generate the modified search query.

### Claim 15 (depends on 14)

The computer-implemented method of claim 14 further comprising prioritizing the plurality of datasets based upon the updated risk assessment value of the record.

### Claim 16 (depends on 10)

The computer-implemented method of claim 10 further comprising incorporating the machine learning model into a search engine.

### Claim 17 (depends on 10)

The computer-implemented method of claim 10 further comprising modifying content of the at least one returned record in response to user selections corresponding to the at least one returned record.

### Claim 18

At least one non-transitory computer-readable storage medium comprising instructions that, when executed, cause a system to:
generate feature extraction data from records across a plurality of datasets, features corresponding to the feature extraction data comprise at least one feature associated with risk indicia and at least one feature associated with a particular data source of at least one of the plurality of datasets;
build a machine learning model based upon the feature extraction data on the records, the machine learning model comprising risk assessment data for the records across the plurality of datasets;
execute a clustering process on the records across the plurality of datasets to identify a set of records, each record of the set of records having risk assessment data that exceeds a baseline threshold;
return at least one record of the set of records in response to search queries;
modify the risk assessment data in the machine learning model in response to user selections corresponding to the at least one returned record; and
execute the clustering process on the records across the plurality of datasets to identify a new set of records.

### Claim 19 (depends on 18)

The non-transitory computer-readable storage medium of claim 18, comprising instructions that when executed cause the system to: identify relevant search terms for the search queries using the machine learning model.

### Claim 20 (depends on 18)

The non-transitory computer-readable storage medium of claim 18, comprising instructions that when executed cause the system to: prioritize the plurality of datasets based upon the modified risk assessment data.