Rippleshot is excited to announce Rules Assist™, an AI-driven decision rules analytics solution to empower community banks and credit unions in the fight against emerging fraud trends.
E-commerce fraud now accounts for roughly 75% of all card fraud, causing financial institutions to race to keep up with fraudsters. In response, top Fortune 500 financial institutions have embedded artificial intelligence and machine learning into their core business models. The four biggest banks in the U.S. budgeted a collective $38.4 billion for innovation and technology in 2019 alone.
Faced with more limited resources, community banks and credit unions often lack the technological edge to keep pace with innovations that greatly impact customer experience. Rippleshot Rules Assist was developed to address technology gaps smaller financial institutions face in their back office to efficiently protect their customers. Financial institutions will be able to cost effectively leverage AI and Machine Learning within their existing infrastructure without adding IT resources or staff.
“Traditional decision rules solutions are either too labor-intensive, too expensive, too difficult to implement, not effective enough, or all of the above,” said Rippleshot CEO Canh Tran. “Rules Assist automates the fraud trend analysis and delivers comprehensive daily fraud intelligence to help fraud managers write faster, more powerful rules with lower false positives.”
Developed and validated with Rippleshot’s banking and credit union customers and industry practitioners, Rules Assist provides fraud analytics that allow fraud managers to proactively stop millions of fraudulent transactions that escape detection by traditional methods.
“The analytic power of AI and machine learning to identify and detect patterns of fraud and potentially fraudulent behavior is much more powerful than human staff could ever accomplish using databases and spreadsheets,” Jeff Aipperspach, a Payment Processing Industry VP, said about the ability of Rippleshot’s technology to disrupt the industry.
