E-Commerce Dispute Resolution Prediction

TL;DR

This paper introduces a dataset and machine learning models to predict dispute outcomes in e-commerce, aiming to assist human agents in resolving complex buyer-seller disagreements efficiently.

Contribution

It presents the first large-scale dataset of e-commerce disputes and develops classifiers that accurately predict dispute outcomes, enhancing dispute resolution processes.

Findings

High accuracy in dispute outcome prediction

Identification of behavioral and linguistic patterns

Insights into features influencing dispute resolutions

Abstract

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.