Fenris API Documentation

Fenris is an API-driven service for the modern insurance company. The Fenris suite of APIs provides the data and scores that inform insurance coverage decisions, evaluate risk, and improve the experience of applying for, rating, and servicing insurance accounts. The APIs save research time and minimize fillable form content errors by returning applicant data completed form fields for a client (applicant) to confirm, instead of requiring the client to complete forms manually.

This documentation has been used with the OpenAPI 3 specification. Definitions of the response fields are included under each service endpoint. Example requests and responses can be seen in the right-hand panel for each endpoint. The example request JSON can be collapsed and expanded using the minus and plus signs that appear on the right side of the JSON objects. For help getting authentication set up, please see our authentication documentation .

Once you have your authentication set up, please note that access lasts only 24 hours per generated token. You can make a request for any service you have access to with an example request for that service, found in the right-hand panel of the documentation.

Do you need an account to get started? Click here to request credentials.

If you are stuck or have a question not covered in our documentation, please send us an email at hello@fenrisd.com.

The Fenris Developer Data Collection is a set of artificial data records that can be used to test the Fenris services. The service’s responses to records from the artificial data are guaranteed to be constant, so that your integration tests will not break. Since the data in the set is synthetically generated, no personally identifying information (PII) is present, and the data can be shared openly. Best of all, your account is never charged for requests made with the developer data.

You can download our developer data here:

• Personal Developer Data
• Business Developer Data

Postman is a third-party all-in-one API platform that can be helpful for testing and working with APIs. We host a Postman collection that uses the Fenris Developer Data to make example requests. For help getting started with Postman, please see the Postman documentation

The Fenris suite of services is comprised entirely of REST APIs. Representational State Transfer (REST) is a set of guidelines to manage communications on networks, including the Internet. One of the many benefits of using this pattern is that most development teams will be familiar with the protocol. You can read more about RESTful APIs here.

The Components of a RESTful API Client Request

The first component of a RESTful API request is the unique resource identifier. For all Fenris Services, this is the URL (Uniform Resource Locator). This is what allows the request to be routed to the correct service. This is also known as the request endpoint. The URL for each service can be found in the right-hand column of the documentation.

The next component of the request is the Method. The method lets the service know what the request needs to do. The current Fenris services support GET and POST requests, depending on the particular endpoint and desired process. GET allows for the access of resources at a specified URL. POST is used to send data to the service. POST requests are used to prevent information from being exposed in the URL.

The appropriate method for each request is described in the right-hand column of the documentation, to the left of the service endpoint, or in the left-hand navigation menu, next to the highlighted service.

The Request Headers component of a request is the metadata sent from the client to the server. This contains information about authentication and the format of the request, and the response.

The data used to create the request is provided in the body of the request.

Additional parameters can give the service additional details, such as a query of path parameters.

Using the Submission ID in a Request

Every Fenris API request has an optional submission-id parameter. This parameter is echoed directly back in the response. This value may be used to track your internal processes or systems associated with the request. For example, let’s say you have multiple lead scores and want to use Fenris Profile+ to evaluate the contact information and quality of the leads without losing the ability to route the lead back to its original source. While making a request to Fenris Profile+, you can include the submission-id parameter in your request's header to specify the original source, and that same string will be returned in the response's submissionId field.

Sending Test Requests

The submission-id parameter can also be used to mark a request as a test request by using the prefix test in the value of the submission ID.

Test the Property Details service with our Personal Developer Data. The developer data is synthetically generated and contains no PII. Requests made with developer data are not charged to your account.

Test the Property Replacement Cost service with our Personal Developer Data. The developer data is synthetically generated and contains no PII. Requests made with developer data are not charged to your account.

Test the Property Hazards and Perils service with our Personal Developer Data. The developer data is synthetically generated and contains no PII. Requests made with developer data are not charged to your account.

Test the Auto Prefill service with our Personal Developer Data. The developer data are synthetically generated and contains no PII. Requests made with developer data are not charged to your account.

There are two return options for the Auto Prefill endpoint; a base response and an enhanced response. The enhanced response includes everything in the based response along with additional enhanced vehicle information. The response types are toggled with the responseType parameter in the request body.

Setting the responseType to M returns only the base data. This is the default. Setting the responseType to C returns both the base data and the enhanced vehicle information. Enhanced vehicle information must be activated for your account. Please reach out to your account representative with questions.

The enhanced vehicle information is returned in the vehiclesEnhanced field in the response. This field is a list of vehicles found for the request, with each item in the list containing details for the vehicle, including the vehicle's vin number and registration.

Examples of each type of response can be seen in the right-hand panel, under Response samples, by toggling the options under Example. Additional details of the response fields can be seen in the Responses section below under the 200 Response Schema. The vehiclesEnhanced details can be seen by clicking the expansion carrot.

The Fenris Digital Driver Record service provides summary information of potential violations on a driver's record. This includes a summary status of the driver's record, counts of the found minor and major violations, the dates of the latest minor and major violations found, and a calculated rating factor for the driver based on the found incidents. The rating factor is priority to Fenris, generated with industry experts, and gives an estimate of how the driver's rates for different tiers of insurance policies may be impacted. The Fenris Driver Record service provides invaluable insights for streamlining applications and quoting auto insurance policies.

Test the Driver Record service with our Personal Developer Data. The developer data are synthetically generated and contains no PII. Requests made with developer data are not charged to your account.

Test the Drivers License service with our Personal Developer Data. The developer data are synthetically generated and contains no PII. Requests made with developer data are not charged to your account.

Many smaller and medium-sized companies don't have access to data science resources and the breadth of data needed to create effective machine-learning models. The Fenris Commercial Scoring API Suite hosts a set of both industry generic and tailored machine learning models to give our customers a competitive edge when it comes to understanding their leads and prospective customers.

For a model trained with your data, our data scientists tailor our machine learning algorithms to the criteria that you use in your business to define a high-quality lead. Once a model is trained and deployed, when a request to score a lead is sent to the service we match the lead to our proprietary database to create an enriched prospect profile. This improves the predictive power of the model by augmenting your lead data with our own attributes. The enriched data is scored using your tailored model to segment the lead based on predicted propensity to buy your insurance product.

The Fenris Lead Insight Service designed to insure that the leads you are buying meet your parameters of quality before they are purchased. Using anonymous data, the Fenris Lead Insight Service generates a score indicating the probability of a lead will convert. This insight allows you to route the request to the appropriate channel or return the lead to the seller.

Highly customizable, the Fenris Lead Insight service houses both generic line of business models for probability to convert as well as tailored customer models for assessing the quality of a lead.

For a model trained with your data, our data scientists tailor our machine learning algorithms to the criteria that you use in your business to define a high-quality lead. Once the tailored model is trained and deployed, the new lead information can be seamlessly scored in a matter of milliseconds.

Many smaller and medium-sized companies don't have access to data science resources and the breadth of data needed to create effective machine-learning models. The Fenris Scoring API Suite hosts a set of both industry generic and tailored machine learning models to give our customers a competitive edge when it comes to understanding their leads and prospective customers.

The generic propensity to buy models housed in this suite can identify applicants in your sales funnel the leads who are 10x more likely to make a purchase, with the models be tuned by line of business or product line.

For a model trained with your data, our data scientists tailor our machine learning algorithms to the criteria that you use in your business to define a high-quality lead. Once a model is trained and deployed, when a request to score a lead is sent to the service we match the lead to our proprietary database to create an enriched prospect profile. This improves the predictive power of the model by augmenting your lead data with our own attributes. The enriched data is scored using your tailored model to segment the lead based on predicted propensity to buy your insurance product.