Fraud Detection and Customer Service
One of the most common applications of artificial intelligence (AI) in e-commerce is fraud detection. AI can analyze vast data sets and identify patterns or anomalies that might indicate fraudulent activity. This ability proves especially useful in card-not-present environments, like online transactions.
Although AI excels at processing large amounts of information, it requires careful tuning to ensure accurate analysis and presentation of findings. Overly autonomous AI decision-making can lead to unintended consequences, either approving fraudulent transactions or incorrectly flagging legitimate ones as suspicious.
“Sometimes, a transaction is clearly fraudulent,” Don Apgar noted, “but in more ambiguous cases, if you’re not cautious enough, you might approve a fraudulent transaction, or conversely, wrongly block a genuine purchase due to overly strict policies.”
Compliance and Anti-Money Laundering (AML)
In the realm of compliance, AI can be instrumental in helping payment processors verify merchant accounts and adhere to regulatory standards. By scouring the internet for relevant data, AI tools can provide comprehensive information that aids in due diligence processes.
Compliance issues like anti-money laundering (AML) can be particularly challenging because they involve analyzing large volumes of data to detect patterns indicative of illegal activities. While banks and processors are facing increased scrutiny, the role of AI in ensuring compliance is likely to persist regardless of regulatory changes.
Transaction Routing
Another area where AI can significantly enhance operational efficiency is transaction routing. As payment methods become more diverse, organizations need robust systems for selecting the most efficient payment path. Rules-based systems are common but limited by their inability to handle new and unseen variables.
“Machine learning models rely on transactions with similar attributes,” Apgar explained, “but when an entirely new scenario arises, how does AI make a decision? This is where broader data analysis capabilities come into play, enabling more nuanced transaction routing.”
Customer-Facing Applications of AI
Beyond operational efficiency, AI can also offer benefits in customer service. For instance, chatbots and virtual assistants can swiftly direct customers to relevant information or FAQ sections. While these tools handle a significant portion of inquiries, it’s crucial to maintain human oversight for more complex issues.
Some financial institutions have taken the next step by implementing agentic AI agents that can conduct transactions autonomously, albeit with restrictions. These agents could potentially simplify the buying process by allowing customers to give broad instructions—such as finding a 25th anniversary gift—and having the agent handle all subsequent steps.
However, many consumers are wary of full automation due to concerns about potential errors or misuse of personal data. Therefore, it’s essential for businesses to maintain a buffer through which critical actions can be monitored and corrected if necessary.
Conclusion
AI offers substantial efficiency gains across various aspects of the payments ecosystem. As AI continues to evolve, its applications will expand beyond back-end operations into more customer-facing roles. Nevertheless, careful management is required to mitigate risks and ensure that AI complements rather than disrupts existing processes.