Are your traditional ATO tactics putting you at risk? Get expert insights into how the latest technology is helping businesses prevent reputational damage, operational disruptions, and revenue loss.
Securing against ATO with advanced authentication and artificial intelligence (AI)
In today’s digital landscape, the complexity of authentication processes can often lead to fatigue, referred to as “always tired online” due to “too many factors to authentication.” With the rollout of PCI DSS 4.0, many organizations anticipated a decline in ATO incidents due to stronger security requirements. However, some businesses have encountered challenges in meeting the new compliance standards.1 As a result, those that remain non-compliant may face a heightened risk of account takeovers compared to their compliant peers.
The persistent threat of account takeovers
ATO is an important issue that can lead to reputational damage, operational disruptions, and revenue loss. Factors such as data breaches, phishing, credential stuffing, and poor password hygiene continue to drive the frequency of incidents. These incidents can quickly erode customer trust and drain revenue.
Do you know the difference between what is fraud and a scam? Check out our complete list of scams, frauds, and financial crimes in the ABCs of fraud glossary.
Traditional detection methods can’t keep up with advanced tactics
Traditional methods for detecting ATO often fall short, especially as fraudsters employ sophisticated tactics and identity-masking techniques. AI-powered ATO models offer significant improvement by detecting behavioral anomalies rather than waiting for the consequences of a breach.
Identifying these nuanced anomalies
Effective ATO detection relies on spotting subtle signals. These may include changes in login patterns, device usage, contact details, or spending behavior. Leveraging real-time analysis of these signals, utilizing intelligence from an extensive, anonymized data consortium can flag and prevent ATOs before they cause damage.
The importance of digital identity in modern commerce
Effective ATO detection relies on spotting subtle signals. These may include changes in login patterns, device usage, contact details, or spending behavior. Leveraging real-time analysis of these signals, utilizing intelligence from an extensive, anonymized data consortium can flag and prevent ATOs before they cause damage.
Integrate ATO detection into your payments system easily
ACI uses machine learning, adaptive behavior monitoring, and precision scoring in a cloud-native platform to identify high-risk activities early without generating false positives. These models scale seamlessly across enterprise-level transaction environments, adapting to shifting patterns while maintaining vigilance. Book a demo today to see it in action.
A certified fraud and analytics professional with over 20 years of experience in payment acceptance optimization, global fraud prevention, digital identity verification and authentication, statistical data analysis, machine learning, and artificial intelligence enterprise real-time financial decision technology solutions. Erika leads a global team of fraud professionals across six different countries overseeing customer analysis and implementing state-of-the-art intelligent solutions to reduce operational costs and generate incremental revenue. Leveraging incremental learning, pattern detection and data mining techniques, Erika’s efforts have delivered best-in-class performance for leading merchants across the globe. Erika’s contributions have been recognized with the ‘Forty under 40’ and Women in Payments Educator in 2019. Her passion for her work drives her pursuit of excellence in the industry.
Vlad brings nearly three decades of experience in strategic consulting and innovation, acquisition of business partnerships, development of opportunities, design and implementation of solutions in the financial and payments industry to his role at ACI, leading business development within the LATAM region.
CustomersPartners
RTGS / Wires and cross-border 
