Data submission quality directly impacts the effectiveness of the Orders API (POST /orders and PATCH /orders/{orderId}). Although the API accepts minimal inputs, the underlying AI and machine learning models require contextual data for increased performance. Comprehensive order details improve identity establishment, anomaly detection, and automated approval rates.
High-fidelity data enhances specific downstream capabilities:
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Risk Analysis: Populates policy engines and reporting tools to visualize risk and business performance.
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Dispute Management: Enables automated evidence collection, stronger dispute representation, and merchant account health monitoring.
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Customer Analytics: Facilitates precise cohort segmentation to distinguish between high-value customers and one-time buyers.
Submitting a complete data set maximizes the platform's ability to analyze, protect, and report on transactions.
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Identity Resolution: Linking disparate data points—including emails, payment tokens, phone numbers, and addresses—establishes a cohesive identity history. This enables the system to recognize returning customers regardless of changes in device or payment method.
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Model Accuracy: AI and machine learning models rely on detailed pattern recognition. Contextual data regarding the item, fulfillment, and payer allows the system to predict outcomes with high fidelity. Missing data reduces model confidence and decision precision.
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Policy Enforcement: Business policies require specific input data to function. Policy sets targeting attributes such as transaction value, shipping region, or product type execute only when the corresponding data is present in the API request.
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Platform Scalability: A comprehensive initial integration supports both current and future services. Mapping all available fields upfront eliminates the need for re-integration when adopting additional platform modules.
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Lifecycle Visibility: Full data capture tracks the transaction lifecycle from inception to completion, encompassing post-authorization events, chargebacks, and dispute resolution.
Note
Exclude properties without values from the payload. Omitting empty fields prevents training the model on inaccurate data.
The data you send in an API request directly affects whether the request succeeds and how accurate the resulting decision or response will be. Some fields are strictly required for the API to function, while others significantly improve decision quality. Clear prioritization helps teams meet schema requirements while also getting the most value of the platform.
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Required: These fields are critical. Without them, the system cannot effectively identify the transaction, link it to device data, or process a basic risk inquiry.
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High-Value: Critical inputs that drive model performance, data resolution, and processing quality. While not technically required, these fields significantly influence decision accuracy and automation rates.
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Business specific: Supplemental data points that support specific business logic, granular reporting, or specialized use cases. These fields provide additional context without heavily weighing on primary assessments.
These fields are required to send in the Orders API request to ensure that Kount 360 can effectively identify the transaction, link it to device data, and process a basic risk inquiry.
Pre-Authorization versus Post-Authorization
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Pre-Authorization: If you send the order before processing payment, ensure you update the order later using the Order Update API (
PATCH /orders/{orderId}) to provide thetransactions.authorizationStatusdetails and any new information in Kount 360. You can call thePATCHmultiple times as you realize new applicable information. -
Post-Authorization: If you send the order after processing payment, include the
transactions.authorizationStatusin the initial request.
These fields are vital to your success in optimizing the solution. We strongly recommend sending these for every order.