Bulk Dispute Challenge System Using ML for Chargeback ROI
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Solution Overview
Problem
Large retailers face significant losses due to chargebacks, which are difficult to manage efficiently, often resulting in resource wastage when disputing unwinnable cases, and existing methods lack a systematic approach to maximize net gain under disputing capacity and cost constraints.
Innovation Solution
A bulk dispute challenge system utilizing machine learning to predict the probability of success for chargebacks, optimizing the selection of disputes to challenge based on return on investment, and incorporating business constraints to maximize net gain, rather than solely focusing on win rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If all chargebacks are disputed, then potential net gain is maximized, but resource expenditure increases significantly
Solution Approach 1:
The system segments the bulk set of dispute events into individual disputes, evaluating each separately using machine learning models to predict success probability. This allows selective challenging of only those disputes with high predicted success probability, rather than uniformly disputing all chargebacks, thus optimizing the balance between net gain and resource expenditure.
Solution Approach 2:
The system changes the decision parameter from binary (dispute or not) to probabilistic (predicted probability of success). By incorporating probability predictions and ROI calculations, the system dynamically adjusts which disputes to challenge based on expected value, maximizing net gain while controlling resource expenditure.
2Measurement precision
If machine learning models are applied to predict dispute success, then dispute selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between the raw dispute data and the decision-making process. These models act as mediators that process dispute attributes and output predicted success probabilities, improving prediction accuracy while encapsulating complexity within modular model components that can be independently managed and updated.
3Productivity
If bulk dispute events are processed systematically, then processing efficiency is improved, but computational resources are consumed
Solution Approach 1:
The system applies partial action by processing only the necessary subset of dispute events through the full machine learning evaluation pipeline. Disputes with low predicted success probability are filtered out early, avoiding unnecessary computational resource consumption while maintaining high processing efficiency for the relevant subset of disputes.
Data Source
AI summary
In one example, a bulk dispute challenge system may select disputes to challenge from a bulk set of dispute events based on a probability of success and a return on investment. The bulk dispute challenge system may receive a dispute success model that calculates a predicted probability of success for disputes generated by applying a machine learning model to a training data set featuring multiple attributes describing data characteristics of disputed events. The bulk dispute challenge system may apply the dispute success model to a dispute of a dispute set. The bulk dispute challenge system may calculate a predicted probability of success for a dispute challenge. The bulk dispute challenge system may perform a dispute decision for the dispute based in part on the predicted probability of success and a dispute return on investment. The bulk dispute challenge system may execute the dispute challenge to the dispute based on the dispute decision.