Payment Transaction Decline Prediction System
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Solution Overview
Problem
Digital transactions often fail due to various reasons such as expired payment cards, leading to financial losses for merchants and a degraded user experience.
Innovation Solution
A system that uses a machine learning model to determine the likelihood of a payment card being declined by calculating decline probability scores based on historical transaction data, allowing for real-time decisions on whether to transmit an authorization request.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system transmits authorization requests for all payment cards, then transaction reliability is improved, but financial loss increases due to unnecessary requests on expired cards
Solution Approach 1:
The system performs preliminary actions by training a machine learning model on historical transaction data before processing new transactions. The model learns patterns of card expiration and decline from past data, enabling it to predict future outcomes and avoid unnecessary authorization requests on cards likely to be declined.
Solution Approach 2:
The system serves itself by using its own historical transaction data to train the machine learning model. The model continuously improves by learning from the system's own experience, identifying patterns in card usage, expiration, and authorization outcomes without requiring external intervention.
2Loss of energy
If the system uses machine learning to predict decline probability, then financial loss is reduced, but device complexity increases
Solution Approach 1:
The system replaces the mechanical/manual approach of always transmitting authorization requests with an intelligent machine learning-based prediction system. The ML model automatically analyzes patterns in transaction data and makes predictions about card decline probability, substituting automated intelligence for rigid rule-based or manual processes.
3Ease of operation
If the system processes all transactions without prediction, then user experience is maintained, but transaction failure increases due to expired cards
Solution Approach 1:
The system implements feedback by continuously using historical transaction outcomes to train and improve the machine learning model. The model receives feedback from actual authorization results, card expirations, and decline patterns, adjusting its predictions to become more accurate over time and better prevent transaction failures.
Data Source
AI summary
A method includes receiving a payment request that indicates a card identifier corresponding to a payment instrument to be used for payment. The method further includes determining that the card identifiers fails to satisfy at least one card activity criteria. Additionally, method includes subsequent to determining that no failed authorization attempts were performed for the card identifier within a previous time period, retrieving, from a database, a decline probability score associated with the card identifier. The method also includes based on the decline probability score, determining whether to transmit an authorization request for the card identifier prior to processing the payment request.