A New Era in Payments Looms as Lithic Challenges Traditional Models
Lithic, a leading payments technology firm, has announced a major shift in the way online transactions are processed, upending the status quo that has dominated the industry for a quarter-century. For 25 years, card-not-present payments have been built on the premise of verifying the buyer’s humanity, authorization, and control over the transaction. This approach has relied on a patchwork of fraud systems, merchant controls, and authentication tools to minimize losses due to non-human behavior or unauthorized transactions.
The problem, however, is that these systems are no longer equipped to handle the rise of sophisticated AI-driven shopping behaviors.
According to Lithic, the days of treating non-human behavior as the enemy are over. Instead, the company advocates for a more proactive approach that anticipates and adapts to the evolving nature of online shopping. This involves moving beyond traditional authentication methods, which are often based on static data and manual verification, to a more dynamic and AI-driven environment that focuses on the nuances of human behavior.
At the heart of Lithic’s proposal is a novel approach to risk assessment, which emphasizes the analysis of human behavior and decision-making patterns in real-time.
This shift in perspective is driven by the increasing use of AI-powered shopping assistants, which blur the lines between human and machine interactions. As these technologies become more prevalent, the traditional risk assessment models will become increasingly obsolete, leaving merchants and payment processors scrambling to catch up.
What this means
For online shoppers, Lithic’s proposal represents a more seamless and intuitive checkout experience, one that is better attuned to their individual needs and preferences. For merchants and payment processors, it signals a move away from a defensive, reactive approach to risk management, and towards a more proactive, AI-driven strategy that anticipates and mitigates potential risks.
