As financial transactions become increasingly digital, the danger of fraud, money laundering, and payment failure continues to increase. Be it financial institutions or e-commerce retailers like Amazon and Walmart, corporations are under intense regulatory scrutiny to detect suspicious transactions, prevent fraudulent activity, and remain compliant with strict financial regulations. Traditional fraud detection techniques, although helpful in some situations, often fall short to handle high false positives, manual intervention requirements, and evolving fraud methods.
This is where Artificial Intelligence is transforming financial security. AI-driven solutions are not only enhancing Anti-Money Laundering compliance but also improving fraud detection, payment optimization, and risk management for businesses. Large language models, machine learning models, real-time monitoring are being employed by firms to automate the compliance process, reduce human error, and make better decisions.
AI in Financial Security: Enhancing Compliance and Fraud Prevention
Financial institutions and businesses that handle monetary transactions such as Amazon, are required by federal and state laws to detect and prevent money laundering. Anti-Money Laundering regulations enforce strict compliance through Suspicious Activity Monitoring to identify irregular transaction patterns, Know Your Customer regulations to verify customer identities and assess risk, and Denied Party Screening to ensure individuals and entities are not listed on government-sanctioned watchlists. Currently most institutions use traditional rule-based algorithms to implement AML regulations. However, rule-based approach can generate a large number of incorrectly flagged cases, requiring extensive manual intervention. Therefore, leveraging AI is paramount as it helps to scale operations, lower operational costs while improving the accuracy of flagging suspicious transactions.
Bhavnish Walia, Amazon’s lead of AI risk management vertical, uses AI to increase financial security. With Amazon processing approximately 8.22 million transactions per day in the U.S. alone, AI-driven compliance solutions have proven to be a game-changer in managing financial risk. He emphasizes the importance of Large Language Models in automating fraud detection and compliance workflows. His team uses LLMs to complete questionnaires, add annotations on suspect transactions, and offer recommendations to human investigators about approving or rejecting them. He also leverages real-time AI tools to generate automated summaries for approvals and declines—tasks that previously required manual review.
Amazon has invested heavily in AI, committing a planned $100 billion by 2025, confirming its emphasis on AI-driven security, compliance, and risk management solutions.
Henry Xu, previously Product Data Scientist at the German neobank N26, explained another use case of LLM’s for banks, “We took hundreds of thousands of data point as input, such as your address, zip code, account information and transaction behaviors and predict a probability that your account is fraudulent. If the probability was over a certain threshold, which was dynamic, then the algorithm flag the account as suspicious”.
For more on this topic, check out: Risk-Based Authentication: The Future Of Secure Digital Access
AI for Fraud Prevention and Payment Optimization for Subscription Services
Walmart’s subscription services have two critical financial challenges: fraud and payment failure. Identity theft, account takeover theft, money laundering, and return item fraud are causing significant economic losses along with eroding customer trust. Subscription fraud—where fraudsters use fake identities or stolen credentials to access services— remains on the rise. TransUnion’s report states that account takeover fraud increased by 81% from 2019 to 2022. At the same time, declined payments due to lack of funds, card expiration, or incorrect payment means are disrupting the user conversion process for significant revenue loss. Over 11% of online payments fail, and these failed transactions could cost subscription businesses more than $129 billion by 2025 according to a report by Pymnts.
To address the above challenges, AI-powered solutions are revolutionizing payment optimization and fraud protection in the subscription economy.
Banani Mohapatra, the head of Walmart’s Subscription Product’s Analytics Fraud and Payment vertical, employs multimodal fusion techniques to combine insights gleaned from LLMs with transaction logs and metadata to identify suspicious behavior patterns in real-time. In addition to preventing fraud, she sees AI-powered payment recovery systems as key use cases in reducing involuntary churn. These systems leverage LLM outputs to trigger personalized reminders for upcoming payment renewals, execute transactions in real-time to the best payment processors, and schedule retry attempts dynamically based on past payment history.
Looking Ahead
As online transactions expand in scope across industries, the financial risks of fraud, compliance errors, and payment failure continue to grow. This dynamic landscape demands intelligence that is smarter, faster, and responsive—where AI is emerging as a critical enabler. From Amazon’s and N26’s use of LLMs in risk decision-making and compliance automation to Walmart’s use of real-time fraud detection and payment recovery platforms, leading organizations are looking to AI to drive resiliency, reduce losses, and establish customer confidence. The future of financial security isn’t just a matter of detecting fraud, but preventing and foreseeing it—proactively, efficiently, and at scale.
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