Chargeback Refund Profiling for Fraudulent Transaction Screening

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Solution Overview

Problem

Existing fraud chargeback protection services provide proactive refunds for fraudulent transactions that may not prevent subsequent chargebacks, leading to inefficiencies and market narrowing, as only about 50% of reported fraudulent transactions result in chargebacks, and 10-20% of proactive refunds do not prevent them.

Innovation Solution

A system and method for optimizing refunds by determining probabilities of chargebacks using a chargeback analysis profile, generating proactive refunds based on these probabilities, and creating a fraud analysis profile when existing profiles are unavailable, to minimize unnecessary refunds.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If automatic refund is provided for all reported fraudulent transactions, then the market for fraud chargeback protection service is narrowed, but the effectiveness of proactive refunds is reduced

Engineering Contradiction:

Improveeffectiveness of proactive refundsVSAvoidmarket for fraud chargeback protection service

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system changes the parameter of refund provision from universal (all reported fraudulent transactions) to selective (only transactions with predicted chargeback probability above threshold). By adjusting this parameter dynamically based on predicted chargeback probability, the system optimizes both effectiveness and market adaptability.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system uses feedback from historical chargeback data to train predictive models that estimate chargeback probability for new transactions. This feedback loop enables the system to learn from past performance and adjust its refund decisions to maximize effectiveness while maintaining service versatility.

Inventive Principle:

Principle #23Feedback

2Reliability

If proactive refund is provided for reported fraudulent transactions, then possible chargebacks are prevented, but 10-20% of refunds do not prevent following chargebacks

Engineering Contradiction:

Improvechargeback prevention rateVSAvoidineffective refunds

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of transaction characteristics and historical data to predict chargeback probability before issuing refunds. This preliminary action filters out transactions unlikely to result in chargebacks, ensuring refunds are only issued when they are likely to be effective, thereby reducing wasted effort on ineffective refunds.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the threshold for initiating proactive refunds based on predicted chargeback probability. By changing this parameter threshold, the system optimizes the balance between preventing chargebacks and avoiding ineffective refunds, maximizing the ratio of effective to total refunds.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If chargeback analysis profile is created for every new transaction, then accurate prediction is achieved, but system complexity increases

Engineering Contradiction:

Improvechargeback prediction accuracyVSAvoidprofile database management

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary checks to determine whether a chargeback analysis profile already exists for a transaction before creating a new one. This preliminary action avoids redundant profile creation and reduces system complexity while maintaining accurate predictions by utilizing existing profiles where available.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The chargeback analysis profile serves multiple functions: it stores transaction characteristics, historical chargeback data, and predictive model inputs. By making the profile multi-functional, the system reduces the need for separate data structures and simplifies overall system architecture while maintaining high prediction accuracy.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250384446A1Systems and methods for optimizing electronic refund transactions for detected fraudulent transactions

Publication Date: 2025.12.18 WORLDPAY LLC

AI summary

A method for optimizing refunds for suspected or detected fraudulent transactions includes receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor extracting identifying information of transactions associated with the chargeback transaction from the chargeback analysis request, searching for a chargeback analysis profile in a profile database, determining whether the chargeback analysis profile exists in the profile database, upon determining that the chargeback analysis profile does not exist in the profile database, obtaining a new fraud analysis profile, determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback, determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback after a proactive electronic refund transaction, and generating a proactive electronic refund transaction based on the first probability and the second probability.