How AI Connects Payments and Fraud Prevention

The payment industry is seeking new technology and data-driven ways to minimize fraud.

In 2024, The U.S Department of Treasury reported that machine learning and AI recovered over $4 billion in fraud and improper payments, causing the Treasury to partner with federally-funded state administered programs who provide them the necessary data, AI tools, and expertise to increase access to and the usage of Treasury’s payment integrity solutions.

AI technology and machine learning can improve payment experiences and provide innovative ways to reduce fraud, but it can also generate vulnerabilities for unscrupulous people. To protect your business from these complex, ever changing threats, implementing new ways of prevention is worth its weight in gold.

Some unexpected larger transactions to new accounts can be seen as risky and go undetected without the right checks and balances. To combat this ongoing threat in the digital payment landscape requires the use of machine learning and AI.

How Machine Learning and AI in the Payment Industry Reduces Fraud

Despite numerous payment systems using advanced technology to detect suspicious activity with improved accuracy to minimize false positives, machine learning and AI can stay one step ahead of fraudsters. It is the most effective method to prevent any form of fraud.

The best forms of machine learning and AI in the payment industry today include:

• Supervised Learning Models: These models can learn from historical labeled transaction data and use gradient boosting machines to manage imbalanced datasets regularly found in fraud. Neural networks and random forests make complex patterns simple and detect subtle patterns in high-dimensional transaction data.
• Real-Time Processing: Online learning algorithms can update in real-time to detect fraud immediately, while engineering pipelines compute fraud indicators on the fly. Plus, high-volume transaction data is handled seamlessly by stream processing frameworks.
• Anomaly Detection Methods: Isolation forests can randomly choose a feature and splitting value to single out transactions. Normally, fraudulent transactions have fewer splits to isolate, making isolation forests ideal for detecting geographical anomalies, unusual timing patterns, and odd transaction volumes. 

Fraud prevention requires a technological approach with AI and machine learning that addresses vulnerabilities humans may miss that can cause a breach. These prevention technologies in conjunction with greater employee awareness are indispensable for preventing all types of fraud.

‍

The Future of AI and Machine Learning: Payment Fraud Prevention 

AI will play a vital role in fraud prevention throughout 2025 and beyond to protect your business.

The advanced security tools from AI like ensemble methods combining multiple models and graph-based methods essential for detecting complex fraud networks that identify and prevent fraudulent activities could be a huge step forward to enhancing your strategy of prevention. Using these tools that carefully analyze transaction data for suspicious activity or patterns makes it significantly easier for early identification and interceding of potential fraudulent scenarios.

Here are other ways how AI-driven fraud detection systems can assist with your payment fraud prevention in the future:

• AI-powered automation can process large data sets accurately and swiftly, minimizing the need for manual involvement.
• The reduction of false positives by AI adds to an easier customer experience and increases security measures.
• A McKinsey & Company study predicts utilizing AI-driven fraud detection systems can reduce fraud related costs by 30% to 50%.

Open payment platforms powered by AI and machine learning can anticipate future threats and expedite the identity verification process necessary to combat the threat of fraud with innovative capabilities like automation to streamline efficiency.

Your payment ecosystem needs the ability to analyze large data sets to detect peculiar patterns or behaviors revealing cases of fraud, a feature enabled through AI and machine learning technologies.

Enhanced security methods such as predictive analytics and anomaly detection create a new layer of authentication to predict future fraud attempts before transactions occur. Analyzing unstructured data is critical to recognizing new types of fraud, the capability to automatically monitor a social media post will minimize data breach risks throughout transactions.

Finally, fraud is becoming more sophisticated and difficult to identify. It will be essential for you to collaborate with other industries to optimize payment flow and mitigate fraud efficiently. The sharing of various data sets and insights across numerous sectors will allow you to stay one step ahead of everchanging fraud tactics to enhance payment security.

Spreedly Elevates Fraud Prevention and Payment Orchestration with Protect

At Spreedly, Protect is our innovative solution to embed fraud management tools into the payment orchestration process, we use a single API call to orchestrate fraud checks, payment authorization, and 3DS authentication. Now merchants can optimize fraud decisions and transaction routing to increase authorization rates and minimize fraud related losses.

Work with an open payments platform that unlocks global payment performance.‍

Schedule a call with our team today to discover how Spreedly has an open ecosystem to empower merchants with a better customer experience and payment performance.

Download the Payments Orchestration eBook Below