# US 2025/0148321 A1 · System and method for determining expected loss using a machine learning framework

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2025/0148321 A1
- Document ID: US20250148321A1
- Status: Published application
- Kind: A1
- Application number: 19/016,856
- Filed: 2025-01-10
- Published: 2025-05-08
- Earliest claimed: 2021-06-24
- Applicants: The Toronto-Dominion Bank
- Inventors: ILYA STANEVICH; MAXIME LAFLEUR-FORCIER; XULING WANG; PHILIPPE MEUNIER; MATHIEU JACOB; ROBERT CHAMOUN; BRUNO VEILLETE-COSSETTE
- Web page: https://patentgrep.com/patent/US20250148321A1

## CPC

- G06N 5/04: Inference or reasoning models
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G06Q 30/0205: {based on location or geographical consideration}
- G06Q 40/08: Insurance
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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

A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

## Claims

### Claim 1

A computer system for predicting an expected loss for a set of claim transactions received for processing at a server, the computer system comprising:
a computer processor; and
a non-transitory storage medium having instructions that when executed by the computer processor perform actions comprising:
predicting, at a first prediction model, a claim frequency of the set of claim transactions over a given time period, the first prediction model trained using historical frequency data and segment types, each segment type having corresponding subtypes further defining a type of claim;
predicting, at a second prediction model, claim severity of the set of claim transactions during the given time period, the second prediction model trained using historical severity data including average loss severity values based on the segment types and the corresponding subtypes;
identifying the expected loss for the set of claim transactions over the given time period by applying a product of outputs of the first prediction model and the second prediction model; and,
applying the first and the second prediction model, once trained, for predicting an expected loss for subsequent claim transactions associated with the subtypes.

### Claim 2 (depends on 1)

The computer system of claim 1, the actions further comprising: training the first and the second prediction model separately for each segment type having associated data sources for each of the historical frequency data, and the historical severity data.

### Claim 3 (depends on 1)

The computer system of claim 1, wherein the first and the second prediction model each utilize a machine learning model.

### Claim 4 (depends on 3)

The computer system of claim 3, wherein the machine learning model utilizes a single gradient boosted tree model.

### Claim 5 (depends on 3)

The computer system of claim 3, wherein the first prediction model applies a regression technique for characterizing distribution of the historical frequency data.

### Claim 6 (depends on 5)

The computer system of claim 5, where in the regression technique is Poisson regression.

### Claim 7 (depends on 3)

The computer system of claim 3, wherein the second prediction model applies a regression technique for characterizing distribution of the historical severity data.

### Claim 8 (depends on 7)

The computer system of claim 7, where in the regression technique is Gamma regression.

### Claim 9 (depends on 3)

The computer system of claim 3, further comprising collecting location and peril information relating to each of the set of claim transactions wherein the machine learning model is configured to receive claims having different segments, associated with different locations and different perils.

### Claim 10 (depends on 9)

The computer system of claim 9, the actions further comprising prior to predicting at the first prediction model, aggregating claim transactions relating to each segment type for subsequent input to each prediction model.

### Claim 11 (depends on 1)

The computer system of claim 1, wherein the first prediction model, and the second prediction model once trained are configured to receive a features dataset for each claim in the set of claim transactions, the features dataset comprising at least one of: client data, product data, user data, location data, claim related data, geographic behavior data, user experience data, policy data and account data.

### Claim 12 (depends on 11)

The computer system of claim 11, wherein the product data comprises vehicle data or residential property data, and the user data comprises driver data or owner data.

### Claim 13 (depends on 11)

The computer system of claim 11, wherein the policy data comprises at least one of: types of coverage, types of endorsements, aggregated policy features and discounts.

### Claim 14 (depends on 1)

The computer system of claim 1, the actions further comprising: aggregating sum of all claims for a particular account to generate a single claim in the set of claim transactions, the aggregating occurs between a time period when a policy change on the particular account.

### Claim 15 (depends on 14)

The computer system of claim 14, wherein the policy change comprises at least one of: policy modification, renewal, and cancellation.

### Claim 16 (depends on 1)

The computer system of claim 1, wherein the first predication model and the second prediction model are trained using different types of data that is dynamically updated and maintained.

### Claim 17

A non-transitory storage medium comprising instructions executable by a processor, the instructions comprising steps for the processor to:
receive a set of input claims having a dataset defining each claim;
extract a set of features associated with each input claim derived from the dataset;
apply, for each input claim, a prediction model to predict a loss cost based on extracting the set of features and to infer a claim type of the input claim as related to a segment type, wherein applying the prediction model comprises:
applying a first prediction model for predicting a claim frequency for each input claim from the set of features;
applying a second prediction model for predicting a claim severity for each input claim from the set of features; and
applying a product of each predicted one of the claim frequency and the claim severity via a third loss cost model for identifying the loss cost for each input claim from the set of features based on the segment type.

### Claim 18

A computer implemented method for predicting an expected loss for a set of claim transactions received for processing at a server, the computer implemented method comprising:
(a) predicting, at a first prediction model, a claim frequency of the set of claim transactions over a given time period, the first prediction model trained using historical frequency data for an average number of claims and segment types, each segment type having corresponding subtypes further defining a type of claim;
(b) predicting, at a second prediction model, claim severity of the set of claim transactions during the given time period, the second prediction model trained using historical severity data including average loss severity values based on the segment types and the corresponding subtypes;
(c) identifying the expected loss for the set of claim transactions over the given time period by applying a product of outputs of the first prediction model and the second prediction model; and,
(d) applying the first and the second prediction model, once trained, for predicting an expected loss for subsequent claim transactions associated with any one of the subtypes.

### Claim 19 (depends on 18)

The computer implemented method of claim 18, further comprising: training the first and the second prediction model separately for each segment type having associated data sources for each of the historical frequency data, and the historical severity data.

### Claim 20 (depends on 18)

The computer implemented method of claim 18, wherein: the first and the second prediction model each utilize a machine learning model.

### Claim 21 (depends on 20)

The computer implemented method of claim 20, wherein the machine learning model is a single gradient boosted tree model.

### Claim 22 (depends on 20)

The computer implemented method of claim 20, wherein: the first prediction model applies a regression technique for characterizing distribution of the historical frequency data.

### Claim 23 (depends on 22)

The computer implemented method of claim 22, wherein the regression technique is Poisson regression.

### Claim 24 (depends on 20)

The computer implemented method of claim 20, wherein: the second prediction model applies a regression technique for characterizing distribution of the historical severity data.

### Claim 25 (depends on 24)

The computer implemented method of claim 24, wherein the regression technique is Gamma regression.

### Claim 26 (depends on 20)

The computer implemented method of claim 20, further comprising: collecting location and peril information relating to each of the set of claim transactions wherein the machine learning model is configured to receive claims having different segments, associated with different locations and different perils.

### Claim 27 (depends on 26)

The computer implemented method of claim 26, further comprising: prior to predicting at the first prediction model, aggregating claim transactions relating to each segment type for subsequent input to each prediction model.

### Claim 28 (depends on 20)

The computer implemented method of claim 20, wherein: the first prediction model, and the second prediction model once trained are configured to receive a features dataset for each claim in the set of claim transactions, the features dataset comprising at least one of: client data, product data, user data, location data, claim related data, geographic behavior data per region, user experience data, policy data, and account data.

### Claim 29 (depends on 28)

The computer implemented method of claim 28, wherein the product data comprises vehicle data or residential property data, and the user data comprises driver data or owner data.

### Claim 30 (depends on 28)

The computer implemented method of claim 28, wherein the policy data comprises at least one of: types of coverage, types of endorsements, aggregated policy features and discounts.

### Claim 31 (depends on 20)

The computer implemented method of claim 20, further comprising: aggregating sum of all claims for a particular account to generate a single claim in the set of claim transactions, the aggregating occurs between a time period when a policy change occurs on the particular account.

### Claim 32 (depends on 31)

The computer implemented method of claim 31, wherein the policy change comprises at least one of: policy modification, renewal, and cancellation.

### Claim 33 (depends on 20)

The computer implemented method of claim 20, the first predication model and the second prediction model are trained using different types of data that is dynamically updated and maintained.

### Claim 34

A computer program product comprising a non-transitory storage device storing instructions that when executed by at least one processor of a computing device predict an expected loss for a set of claim transactions received for processing at a server, and configure the computing device to:
(a) predict, at a first prediction model, a claim frequency of the set of claim transactions over a given time period, the first prediction model trained using historical frequency data and segment types, each segment type having corresponding subtypes further defining a type of claim;
(b) predict, at a second prediction model, claim severity of the set of claim transactions during the given time period, the second prediction model trained using historical severity data including average loss severity values based on the segment types and the corresponding subtypes;
(c) identify the expected loss for the set of claim transactions over the given time period by applying a product of outputs of the first prediction model and the second prediction model; and,
(d) wherein the first and the second prediction model, once trained for each of the types of segments and thereby trained for different peril types are applying the first and the second prediction model, once trained, to predict a expected loss for subsequent claim transactions associated with any one of the subtypes.