Chargebacks911 augments its automated dispute response platform

US-based post transaction fraud platform Chargebacks911 has enriched its data to facilitate dispute responses.

The use of data from a consortium of businesses across a number of verticals enables the technology to be ‘trained’. It can dynamically ‘identify data points related to post-transaction fraud, including first-party fraud.’ This is then enriched ‘to exponentially improve underlying dispute intelligence and insights for merchants.’

Company officials stated that they that chargebacks are out of control and this can be devastating, especially in today’s desperate economic climate. However, by further fuelling the power of AI in their digitisation of the disputes management process, the heavy lift of data aggregation is optimised, providing insight and flexibility that easily accommodates the changing landscape of chargeback rules and payments.

One of the primary reasons for the growth in first party fraud is due to unavailable feedback from the merchant. As a result of the quantity of chargeback data that online merchants receive, and the time it takes for a human operator to investigate each chargeback and determine whether it is legitimate or not, the vast majority of cases expire, inadvertently contributing to this negative trend and its unintended consequences, as per the press release.

The industry needs to adopt more intelligent automation to meet this challenge. In response, Chargebacks911 is improving the data collection process, transforming the concept of end-to-end automation by digitising complex and unstructured formats to maximise data insights and leverage rule-based data driven automation.

The company’s AI-enabled automated dispute response platform allows merchants of all sizes across dozens of verticals to fill the knowledge gap and thwart growth in first-party fraud. The Chargeback911 platform acts as a multidimensional orchestration layer. It powers rules-based logic, machine learning, and AI-driven calculations that work in unison to dynamically analyse and classify data input, and compile a response.