Real-Time Chargeback Scoring Engine for Network Transactions
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Solution Overview
Problem
The existing chargeback process is costly and inefficient, as it often results in unnecessary processing operations and fees due to the lack of real-time or near real-time assessment of chargeback probabilities during transactions.
Innovation Solution
A system and method that utilize a scoring engine to provide chargeback probability scoring in real time or near real time, based on specific data from consumers, merchants, and transactions, allowing issuers and acquirers to decline transactions likely to result in chargebacks, thereby avoiding processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If real-time chargeback probability scoring is implemented, then processing costs and fees are reduced by avoiding unnecessary chargebacks, but system complexity increases due to the need for scoring engines and real-time data processing
Solution Approach 1:
The system performs chargeback probability assessment in real-time at the point of transaction authorization, before the transaction is fully processed. The scoring engine evaluates multiple data points (consumer history, merchant risk, transaction patterns) and generates a chargeback score that informs the authorization decision, preventing costly chargebacks before they occur.
Solution Approach 2:
A scoring engine is introduced as an intermediary component between the transaction request and the authorization decision. This engine aggregates data from multiple sources (issuer, acquirer, payment network), applies scoring algorithms, and provides risk assessments that guide authorization outcomes, thereby reducing chargebacks without requiring direct complex interactions between all system participants.
2Productivity
If real-time chargeback scoring is implemented, then operational efficiency is enhanced by preventing chargebacks before they occur, but data processing requirements increase due to the need for real-time analysis
Solution Approach 1:
The scoring engine does not require complete analysis of all possible data points for every transaction. Instead, it uses a predefined set of key indicators (consumer chargeback history, merchant risk factors, transaction amount thresholds, device fingerprinting) that provide sufficient predictive power. This selective approach achieves real-time scoring without processing excessive data, balancing operational efficiency with reasonable computational requirements.
3Loss of energy
If chargeback probability scoring is used to decline transactions, then fees and processing operations are reduced, but transaction approval accuracy may be affected due to potential false positives
Solution Approach 1:
The system dynamically adjusts scoring parameters and thresholds based on accumulated data and observed chargeback patterns. The scoring model is continuously refined using machine learning techniques that adapt to changing fraud patterns and consumer behaviors. This allows the system to maintain high accuracy in identifying at-risk transactions while minimizing false positives that would incorrectly decline legitimate transactions.
Solution Approach 2:
The system implements feedback loops where actual chargeback outcomes are fed back into the scoring model to continuously improve its predictive accuracy. Transactions that were scored as high-risk but did not result in chargebacks, and legitimate transactions that were incorrectly flagged, are used to retrain and adjust the scoring algorithm. This feedback mechanism ensures the system learns from experience and improves transaction approval accuracy over time while maintaining fee reduction benefits.
Data Source
AI summary
Systems and methods are provided for use in imposing chargeback probability scores on network transactions. One exemplary method includes obtaining at least one transaction detail of a network transaction between a consumer and a merchant. A computing device determines a chargeback probability score for the network transaction based, at least in part, on the at least one transaction detail. Chargeback data is transmitted to an entity associated with the network transaction, where the chargeback data includes at least one of (a) the chargeback probability score and (b) an indicator that the chargeback probability score fails to satisfy one or more thresholds, thereby permitting the entity to hold and/or decline the network transaction when the chargeback probability score fails to satisfy one or more thresholds.