POST
/
scoring
/
payment
curl --request POST \
  --url https://greipapi.com/scoring/payment \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "data": {},
  "format": "<string>",
  "mode": "<string>",
  "userID": "<string>"
}'
{
  "data": {
    "score": 82,
    "rules": [
      {
        "id": "PF10003",
        "description": "Customer IP Address is probably VPN/Proxy/Bot/Hosting/Cloud."
      },
      {
        "id": "PF10004",
        "description": "Customer Email Address is probably invalid or spam."
      },
      {
        "id": "PF10001",
        "description": "High purchase rate, according to `customer_ip`."
      },
      {
        "id": "PF10002",
        "description": "High purchase rate, according to `customer_id`."
      },
      {
        "id": "PF10013",
        "description": "Customer device might not be a real device (according to `customer_useragent`)."
      },
      {
        "id": "PF10014",
        "description": "Customer device is registered as a high-risk device (according to `customer_useragent`)."
      }
    ],
    "rulesChecked": 21,
    "rulesDetected": 6,
    "custom_rules_applied": {
      "total": 0,
      "rules": []
    }
  },
  "status": "success",
  "executionTime": 5
}

​
Overview

Detecting and preventing fraudulent payments is a critical task for any business that accepts online payments. This AI-based module is designed to help you do that by analysing a range of data points related to each transaction.

Using sophisticated machine learning algorithms, this method can detect and flag potentially fraudulent transactions by analysing user data, user behaviour, user device, and other factors. For example, if a transaction appears to be coming from an unusual location or device, or if the user’s behaviour is inconsistent with their past transactions, this method can flag the transaction for further review or rejection.

One of the key advantages of this AI-based module is its ability to adapt and learn over time. As it analyses more data and detects more fraud, it can improve its accuracy and efficiency, making it a highly effective tool for preventing financial losses due to fraud.

By implementing this method in your payment processing system, you can help protect your business and your customers from fraudulent activity, while providing a seamless and secure payment experience.

“ECOMMERCE LOSSES TO ONLINE PAYMENT FRAUD TO EXCEED $48 BILLION GLOBALLY IN 2023, AS FRAUD INCURSIONS EVOLVE”, according to a recent report by Juniper Research.

​
Integration Workflow

Greip Payment Fraud Module - Workflow

​
Body Parameters

​
data
object
required
​
format
string
default:"JSON"

The format command is used to get a response in a specific format.

Expected values: JSON, XML, CSV, or Newline

For more information please refer to Response Format.

​
mode
string
default:"live"

The mode command is used to in the development stage to simulate the integration process before releasing it to the production environment.

Expected values: live, or test.

For more information please refer to Development Environment.

​
userID
string

The userID command can be used to identify requests sent by specific users to monitor in the Events Page.

Expected values: email address, phone number, user id, name, etc.

For more information please refer to User Identifier.

{
  "data": {
    "score": 82,
    "rules": [
      {
        "id": "PF10003",
        "description": "Customer IP Address is probably VPN/Proxy/Bot/Hosting/Cloud."
      },
      {
        "id": "PF10004",
        "description": "Customer Email Address is probably invalid or spam."
      },
      {
        "id": "PF10001",
        "description": "High purchase rate, according to `customer_ip`."
      },
      {
        "id": "PF10002",
        "description": "High purchase rate, according to `customer_id`."
      },
      {
        "id": "PF10013",
        "description": "Customer device might not be a real device (according to `customer_useragent`)."
      },
      {
        "id": "PF10014",
        "description": "Customer device is registered as a high-risk device (according to `customer_useragent`)."
      }
    ],
    "rulesChecked": 21,
    "rulesDetected": 6,
    "custom_rules_applied": {
      "total": 0,
      "rules": []
    }
  },
  "status": "success",
  "executionTime": 5
}

​
Response properties

​
data
object
required
​
status
string
required

The response status.

Expected values: success, or error.

​
executionTime
integer
required

Time spent in milliseconds to process the data.

​
Possible Rules

IdDescription
PF1001High purchase rate, according to customer_ip.
PF1002High purchase rate, according to customer_id.
PF1003Customer IP Address is probably VPN/Proxy/Bot/Hosting/Cloud.
PF1004Customer Email Address is probably invalid, disposable or spam.
PF1005Customer Phone Number is probably invalid or spam.
PF1006Customer Latitude/Longitude is invalid.
PF1007Customer card number (BIN/IIN) is invalid.
PF1008Customer debit/credit card issued by a brand different from the one exist in payment_type parameter.
PF1009Customer country is a high-fraud country.
PF1010Customer debit/credit card issued in a high-risk country.
PF1011Customer is purchasing multiple times from multiple locations within the past 30 days.
PF1012Customer debit/credit card is being used multiple times from multiple customer accounts (according to customer_id and card_number).
PF1013Customer device might not be a real device (according to customer_useragent).
PF1014Customer device is registered as a high-risk device (according to customer_useragent).
PF1015AI flagged the transaction as potentially fraudulent.
PF1016AI flagged the transaction as potentially fraudulent due to high transaction amount.
PF1017Mismatch between billing address and IP geolocation.
PF1018Customer has multiple fraudulent transactions in the past 30 days.
PF1019Unusual purchase amount compared to customer’s history.
PF1020Transaction initiated from a newly created account.
PF10021Multiple payment cards used by a single account within a short timeframe.
PF10022Customer IP address were found in one of your blacklists.
PF10023Customer email address were found in one of your blacklists.
PF10024Customer phone number were found in one of your blacklists.
PF10025Customer card number were found in one of your blacklists.
PF10026Customer Id were found in one of your blacklists.