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Abstract
Automated chargeback management is an emerging solution that leverages machine learning (ML) to enhance the efficiency and effectiveness of chargeback dispute processes in the financial and e-commerce sectors. Chargebacks, which occur when a consumer disputes a transaction, can result in significant losses and operational inefficiencies for merchants. Traditional chargeback management involves manual review of disputes, which is often time-consuming and prone to errors. By integrating machine learning techniques, organizations can significantly improve their win rates in chargeback disputes, reduce manual effort, and streamline decision-making processes. Machine learning models are capable of analyzing large volumes of transactional data to identify patterns and predict the likelihood of chargeback success or failure. These models can classify chargebacks by their likelihood of being overturned, enabling merchants to focus on high-risk cases and prioritize efforts on those most likely to succeed. Additionally, machine learning algorithms can help identify fraudulent chargebacks, reduce false claims, and automate responses, further enhancing operational efficiency. By automating routine tasks, chargeback management solutions powered by machine learning not only improve win rates but also reduce operational costs and enhance customer experience. Furthermore, such systems provide valuable insights into consumer behavior, enabling merchants to better understand and address underlying issues that contribute to chargebacks. As e-commerce and online payments continue to grow, automated chargeback management will play a crucial role in mitigating risks and ensuring sustainable financial operations for merchants.
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