# US 2019/0244148 A1 · Computer architecture for characterizing and managing risk

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- Number: US 2019/0244148 A1
- Document ID: US20190244148A1
- Status: Published application
- Kind: A1
- Application number: 16/270,984
- Filed: 2019-02-08
- Published: 2019-08-08
- Earliest claimed: 2018-02-08
- Assignees: GeminiRE
- Applicants: GeminiRE
- Inventors: Jeffrey Bernard Scott; Subhashish Dutta; Peter Laurence Forester; Jennifer Lee Gravelle; Douglas Bruce Collins; Travis Cole Rosecrans
- Web page: https://patentgrep.com/patent/US20190244148A1

## CPC

- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- G06N 3/0499: Feedforward networks
- G06N 3/08: Learning methods
- G06N 20/00: Machine learning
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system, method and program product for optimizing a risk transfer strategy for a resource provider. A system is disclosed having: an interface for accessing event data from a resource provider; a machine learning system that analyzes the event data at different risk levels and detects and quantifies negative correlations among the different risk levels; and a risk transfer optimization system that generates an optimized risk transfer strategy for the resource provider based on detected negative correlations.

## Claims

### Claim 1

A risk management processor for optimizing a risk transfer strategy within a domain of resource providers, comprising:
an interface for accessing event data from a resource provider;
a machine learning system that analyzes the event data at different risk levels and detects and quantifies negative correlations between the different risk levels; and
a risk transfer optimization system that generates an optimized risk transfer strategy for the resource provider based on detected negative correlations.

### Claim 2 (depends on 1)

The risk management processor of claim 1, wherein the interface further accesses industry wide event data.

### Claim 3 (depends on 2)

The risk management processor of claim 2, wherein the machine learning system uses the industry wide event data to cluster event data within at least one risk level to generate a set of clusters.

### Claim 4 (depends on 3)

The risk management processor of claim 3, wherein the machine learning system identifies negative correlations between clusters in the at least one risk level and other risk levels.

### Claim 5 (depends on 4)

The risk management processor of claim 4, wherein the clusters include at least one of location based clusters and time based clusters.

### Claim 6 (depends on 1)

The risk management processor of claim 1, wherein the negative correlations involve an opposite behavior pattern of event data.

### Claim 7 (depends on 1)

The risk management processor of claim 1, wherein the risk transfer optimization system iteratively combines, recalibrates and tests different combinations of clusters and risk levels until a cost savings is achieved.

### Claim 8

A method for optimizing a risk transfer strategy for a resource provider within a domain, comprising:
accessing event data from a resource provider;
analyzing the event data at a high risk level and a low risk level;
clustering event data within the low risk level based on domain level event data to generate a set of clusters;
detecting negative correlations between event data in the set of clusters and event data in the high risk level; and
generating an optimized risk transfer strategy for the resource provider based on detected negative correlations.

### Claim 9 (depends on 8)

The method of claim 8, wherein the domain level event data comprises industry event data.

### Claim 10 (depends on 8)

The method of claim 8, wherein the clusters include at least one of location based clusters and time based clusters.

### Claim 11 (depends on 8)

The method of claim 8, wherein the negative correlations involve an opposite behavior pattern of event data within the high and low risk levels.

### Claim 12 (depends on 8)

The method of claim 8, wherein generating the risk transfer strategy includes iteratively combining, recalibrating and testing different risk levels and clusters until an optimal result is achieved.

### Claim 13 (depends on 8)

The method of claim 8, wherein the resource provider is selected from a group consisting of: an information technology provider, a cloud resource provider, an autonomous vehicle service, a manufacturer, an energy provider, and an insurance provider.

### Claim 14 (depends on 8)

The method of claim 8, wherein event data includes events having a triggering condition and an outcome.

### Claim 15

A computerized platform for managing risk for resource providers, comprising:
a non-catastrophic risk analyzer that:
evaluates historical event data from a resource provider to determine frequency and volatility data;
generates a set of clusters of event data based on industry event data;
applies the frequency and volatility data to selected clusters to determine cost and risk parameters;
sets boundaries conditions to the cost and risk parameters; and
generates a non-catastrophic risk transfer strategy;
a catastrophic risk analyzer that generates a catastrophic risk transfer strategy based on budget and threshold requirements; and
a risk transfer optimization system that iteratively recalibrates and combines the non-catastrophic and catastrophic risk transfer strategies into a comprehensive risk transfer strategy until an optimized cost savings is achieved, wherein a recalibration includes altering the cost and risk parameters and the catastrophic risk transfer strategy.

### Claim 16 (depends on 15)

The computerized platform of claim 15, wherein the clusters include at least one of location based clusters and time based clusters.

### Claim 17 (depends on 15)

The computerized platform of claim 15, wherein an optimized result is achieved when a negative correlation occurs.

### Claim 18 (depends on 17)

The computerized platform of claim 17, wherein a negative correlation occurs in response to opposite behavior patterns of event data within non-catastrophic and catastrophic event data.

### Claim 19 (depends on 15)

The computerized platform of claim 15, wherein the resource provider is selected from a group consisting of: an information technology provider, a cloud resource provider, an autonomous vehicle service, a manufacturer, an energy provider, and an insurance provider.

### Claim 20 (depends on 15)

The computerized platform of claim 15, wherein event data is selected from a group consisting of: failures, overloads, accidents, breakdowns, or claims.