The growing role of AI in financial security and fraud prevention

As financial transactions become more and more digital, the risk of fraud, money laundering, and payment failures continues to increase. Whether it’s a financial institution like Amazon or Walmart or an e-commerce retailer, businesses are undergoing intense regulatory scrutiny to detect suspicious transactions, prevent fraud and continue to comply with strict financial regulations. Traditional fraud detection techniques can be useful in some circumstances, but are often lacking in handling high false positives, manual intervention requirements, and evolving fraud methods.

This is where artificial intelligence is changing financial security. AI-driven solutions not only enhance anti-money laundering compliance, but also improve fraud detection, payment optimization, and business risk management. Large-scale language models, machine learning models, and real-time monitoring are being adopted by companies to automate compliance processes, reduce human error, and make better decisions.

Financial Security AI: Strengthening Compliance and Fraud Prevention

Financial institutions and businesses that process financial transactions such as Amazon are mandatory by federal and state laws to detect and prevent money laundering. Anti-Money Laundering Regulations implement strict compliance through suspicious activity monitoring, identifying irregular transaction patterns, grasping customer regulations and verifying customer identity, understanding customer regulations to assess risk, and denying parties screening to ensure that individuals and entities are not listed on government authorized watchlists. Today, most institutions implement AML regulations using traditional rules-based algorithms. However, rules-based approaches can generate a large number of false flagged cases and require extensive manual intervention. Therefore, leveraging AI is of paramount importance as it helps to increase the accuracy of flagging suspicious transactions while increasing operations and reducing operational costs.

Bhavnish Walia, AMAMON’s AI risk management lead, uses AI to enhance financial security. With Amazon processing around 8.22 million transactions per day in the US alone, AI-driven compliance solutions have proven to be a game changer for managing financial risk. He highlights the importance of large-scale language models in automating fraud detection and compliance workflows. His team uses LLMS to complete surveys, add commentary to suspicious transactions, and provide recommendations to investigators about their approval or rejection. He also leverages real-time AI tools to generate automated summaries for approval and reduction.

Amazon has invested heavily in AI, committed to $100 billion planned $100 billion by 2025, with a focus on AI-driven security, compliance and risk management solutions.

Previously, Henry XU, a product data scientist at Neobank N26 in Germany, described another use case for the bank’s LLM.

For more information on this topic, see Risk-Based Authentication: The Future of Secure Digital Access

AI for fraud prevention and payment optimization for subscription services

Walmart’s subscription services present two important financial challenges: fraud and payment failure. Identity theft, account acquisition theft, money laundering, and return items fraud have erode customer trust and are causing significant economic losses. The number of subscription scams used by scammers to access the services using fake identities or stolen credentials is on the rise. According to a Transunion report, account acquisition fraud rose 81% between 2019 and 2022, while payments declined as the user conversion process was interrupted due to a large revenue loss due to lack of funds, expiration of cards, or incorrect payment instruments. According to a PYMNTS report, more than 11% of online payments have failed, potentially costing more than $129 billion by 2025.

To address the above challenges, AI-powered solutions are revolutionizing payment optimization and fraud protection in the subscription economy.

Banani Mohapatra, head of analysis fraud and payment vertical for Walmart subscription products, employs multimodal fusion technology to combine insights collected from LLMS with transaction logs and metadata to identify suspicious behavior patterns in real time. In addition to preventing fraud, she views AI-powered payment recovery systems as an important use case to reduce involuntary termination. These systems leverage LLM output to trigger personalized reminders for future payment updates, execute transactions in real time to the best payment processor, and schedule retry dynamically based on past payment history.

Looking ahead

As online transactions grow across the industry, the financial risks of fraud, compliance errors, and payment failures continue to increase. This dynamic landscape requires smarter, faster, and more responsive intelligence. This is where AI is emerging as a key enabler. From the use of LLM in risk decision-making and compliance automation on Amazon and N26, Walmart uses a real-time fraud detection and payment recovery platform, leading organizations are increasing resilience to AI, reducing losses and establishing customer trust. The future of financial security is not only detecting fraud, but also preventing it, predicting it, and predicting it.

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