ML Features

Ravelin ML Features allow in-house data scientists and risk teams to benefit from Ravelin’s data insights and infrastructure, by providing Machine Learning features as an API response. These features can then be used as an input to your internal risk models.

Once enabled by Ravelin, features can be requested by adding a features=true parameter to an existing endpoint request. Currently we support returning features on the Connect and Checkout endpoints.

Response Modules

Features are returned in themed blocks called modules. Depending on your needs, you can choose in advance which modules should be returned, and Ravelin will configure the service to only return these modules.

Some modules may require additional integration, such as Ravelin’s Mobile SDKs and JavaScript library for device intelligence.

Speak to our integrations team to further discuss this and decide which modules best suit your needs.

ML Features Response

An example response to a checkout request with all modules returned is shown below:

POST https://api.ravelin.com/v2/checkout?&features=true HTTP/1.1
Authorization: token ...
Content-Type: application/json

{
    "customerId": "61283761287361",
    ...
}

HTTP/1.1 200 OK
Content-Type: application/json
{
  "status": 200,
  "timestamp": 1668428911,
  "data": {
   "customerId": "abc135857058",
   "features": {
        "customer": {
          "emailLength": 15,
          "paymentMethodsRegisteredLastMonth": 5,
          "minutesSinceRegistration": 14643,
             ...
        },
        "order": {
          "minutesFromRegisterToOrder": 2427,
          "cancelledOrderCountLastMonth": 2,
          "successfulOrderCountLastMonth": 1,
            ...
        },
        "payment": {
          "TransactionsByShippingAddressLastWeek": 3,
          "paymentMethodCountryMatches": 2,
          "paymentMethodRegisteredVelocityCount24h": 3,
            ...
        },
        "consortium": {
          "addressFraudScore": 0.211,
          "emailFraudScore": 0.0371,
          "asnFraudScore": 0.373,
          "emailDomainFraudScore": 0.278,
          "binFraudScore": 0.01,
          "transactionDeclineCodeFraudScore": 0.01,
          "ipAddressFraudScore": 0.01,
          "cardIssuerCountryFraudScore": 0.01,
          "paymentMethodTypeFraudScore": 0.01,
            ...
        },
        "graph": {
          "deviceDegreeMean": 1.472,
          "deviceDegreeMax": 3,
          "edgeGeneralMeanAge": 1800,
          "edgeGeneralGrowthRate": 4,
          "edgeGeneralCount": 5,
          "edgeLocalMeanAge": 720,
          "edgeLocalGrowthRate": 3,
            ...
        },
        "locationEnrichment": {
           "orderStartAddressLat": -117.662,
           "orderStartAddressLong": -117.662,
           "orderStartAddressNormalised": "1 main street london uk",
           "orderEndAddressLat": -117.662,
           "orderEndAddressLong": -117.662,
           "orderEndAddressGeohash": "w21zd2mkt",
           "orderEndAddressNormalised": "1 main street london uk", 
        },
       "deviceIntelligence": {
           "deviceType": "phone",
           "deviceModel": "iPhone6,2",
           "deviceOS": "ios",
           "firstSeen": "2022-11-21T03:39:30Z",
           "isRootedOrJailbroken": false,
           ...
       },
       "tags": [
            {
               "tagName": "VIP",
               "depth": 7
            },
            {
               "tagName": "return-abuse",
               "depth": 3
            }
            ...
          ]
       }
    }
  }
}

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