Last Updated on January 29, 2024 by
Generative AI can significantly improve chargeback prevention strategies in various ways. The use of data to detect patterns indicating fraud is critical to success. Moreover, fraudsters constantly innovate using automation. To keep up, ecommerce companies must use the same tools. In the past, it was enough to take reams of historical data. Today, that is still useful but not enough to quickly separate signal from noise.
Below are five ways using generative AI as a tool in a chargeback prevention strategy can decrease chargeback rates:
Improved Chargeback Fraud Detection
Generative AI can be used to create advanced predictive models that analyze transaction patterns and customer behavior. By generating synthetic transaction data, it can help in training these models to identify potentially fraudulent activities more accurately. This results in early detection of transactions that deviate from the normal pattern of legitimate orders in some statistically meaningful way. As a result, they can be rejected for flagged for human review.
Automated Chargeback Dispute Resolution
Generative AI can assist in successful automated chargeback dispute resolution. It analyzes customer complaints for problems that can quickly be addressed (such as incorrect shipping information, policy abuse, etc.) and triggers an appropriate response. This can either be through automated responses to specific issues/inquiries, or the triggering of additional in-house workflows. By addressing customer concerns quickly and efficiently, the instances of chargebacks can be significantly reduced.
Enhanced Customer Profiling
In addition to identifying suspicious orders, generative AI can build detailed customer profiles based on purchasing habits and history in order to identify customers who are more likely to file chargebacks. This has the double benefit of allowing businesses to quickly implement additional friction where the risk-profile warrants it, while reducing friction for other customers. This not only reduces chargebacks, but comes with the added positive benefit of an improved customer experience.
Dynamic Risk Assessment Tools
Generative AI can develop dynamic risk assessment tools that adapt over time. As it encounters new types of fraudster attack vectors or feedback on how to approach dispute scenarios successfully, the AI can learn and adjust its parameters. This can free human fraud analysts from the need to constantly make minor tactical changes to algorithms and approve/decline decisions. Instead, they can focus on longer-term strategic questions.
Chargeback Analytics and Continuous Improvement
Business policy abuse is an emerging fraud trend. Fraudsters will take advantage of businesses’ incentives to customers by finding loopholes that allow them to return items, claim warranty violations, and more. Generative AI can analyze trends in the intersection between a company’s policies and chargebacks. This can generate new insights into vulnerabilities, and with the help of cross-departmental human stakeholders suggest improvements to business policies, product offerings, or customer service practices to reduce the incidence of chargebacks resulting from loopholes.
Generative AI offers businesses a new tool for improving chargeback prevention strategies. By enhancing fraud detection, providing dynamic risk assessment, and analyzing internal policies, it can reduce chargebacks and improve the customer experience.
