Bypass the buzzwords: 5 ways AI actually prevents fraud

AI fraud detection helps verify documents, monitor transactions, and catch coordinated attacks

Fraud losses hit $12.5 billion in 2024, up 25% from the previous year, according to the Federal Trade Commission. Scams that once required specialized skills and expensive tools can now be launched by anyone with an internet connection and basic technical knowledge. This is largely due to advances in artificial intelligence (AI) technology, which allows machines to mimic human decision-making and communication patterns, learn from data, and adapt over time.

Financial institutions and fintechs understandably want to push back against fraudsters and are seeking new ways to combat AI-driven fraud with AI-driven solutions. But applied without strategy, even the most exciting new technology also promises to create new vulnerabilities. For example, AI models trained on limited data can perpetuate blind spots that fraudsters exploit, while AI tools that aren't properly integrated with existing fraud controls may cause gaps in coverage. To prevent financial losses and improve operational efficiency, AI fraud detection systems must be tied to real use cases, scalable patterns, and multi-layered defense strategies. 

This post covers five ways AI fraud detection strengthens risk management controls, from automating verification workflows to detecting coordinated fraud attacks in real-time. We demonstrate how these capabilities work together to prevent fraudulent activities by creating adaptive defenses that evolve alongside emerging threats. We explore anonymized examples of financial institutions and fintechs that are effectively utilizing AI in the fight against fraud.

1. Automating document verification

Today's fraud prevention teams are busy, and document forgeries can slip past even the most careful manual reviews. Identity theft remains one of the most persistent threats in financial services, with over 1.1 million reports submitted to the FTC in 2024 alone. While identity thieves steal and misuse real people's information, fraudsters are also increasingly turning to synthetic identities as a more scalable way to commit fraud. 

Experian research found that only 25% of financial service companies feel confident in addressing synthetic identity threats, and just 23% feel prepared to combat AI-generated and deepfake fraud. To remedy this, financial organizations are turning to AI-powered document verification (DocV), a type of identity verification protocol that makes and analyzes document requests at key touchpoints:

• Account opening — When customers create new accounts, AI-driven DocV instantly validates their government IDs, proof of address, and other required documentation as part of the organization’s KYC (Know Your Customer) process. 
• Loan applications — During lending workflows, DocV automatically verifies income statements, tax returns, and employment records to prevent application fraud. This replaces manual reviews that could take days and helps reduce friction without compromising security.
• At the point of transaction — When customers initiate significant transfers or withdrawals, many organizations require additional document verification as a security measure.
• Business banking — For commercial accounts, DocV helps validate business licenses, incorporation documents, and beneficial ownership information. These checks are both central to fraud prevention and necessary for complying with anti-money laundering (AML) regulations, which require organizations to verify business entities to prevent financial crime.

AI document verification accelerates legitimate transactions while identifying subtle inconsistencies that human reviewers might overlook. Machine learning systems can simultaneously analyze government IDs, cross-reference watch lists, and validate sanctions data. 

Example of how AI works in document verification

A small bank flagged a suspicious account application using government IDs that looked legitimate under traditional scrutiny. However, the data told a different story: according to the bank’s risk engine, the applicant's email address had been created within the past 24 hours. 

New email addresses are part of a broader pattern signaling synthetic identity fraud. To ensure the validity of the application, the bank’s risk engine automatically triggered enhanced verification requirements, including AI-powered liveness detection. The system then asked the applicant to perform simple movements — such as turning their head, blinking, and responding to prompts — while their camera captured multiple angles of their face. 

The liveness request revealed the deception: the supposedly legitimate ID photos were actually generated by AI. Because the fraudster could only provide static, synthetic photos that didn't match their actual appearance on camera, they were compelled to abandon their application when faced with an additional, more sophisticated layer of protection.

Best practices for using AI for document verification

Consider two of the best practices for maximizing the effectiveness of AI-powered document verification:

• Layer multiple verification methods — While a fraudster might bypass a single check, combining AI-powered document analysis with biometric verification and data source cross-referencing creates a more robust defense.
• Implement dynamic risk scoring — Reduce friction for trusted users while focusing manual reviews and step-up verification on high-risk cases, improving both fraud prevention and customer experience.
   

2. Performing ongoing transaction monitoring

Machine learning excels at detecting subtle changes in transaction patterns that might escape human notice. Rather than relying on rigid rules, machine learning models can determine what "normal" looks like for each customer and flag meaningful deviations — like a dormant account suddenly showing credit card fraud indicators like high-velocity purchases or signs of payment fraud.

The automation advantage extends beyond pattern recognition. ML models monitor activity 24/7, automatically escalating suspicious behavior and flagging potentially fraudulent transactions while allowing legitimate ones to proceed. This selective friction approach maintains security without disrupting genuine customer activity.

When potential fraud is flagged, fraud teams need to assess multiple data points (including related transactions, device data, and behavioral patterns) to determine whether escalation is needed. Machine learning helps streamline this process by reducing false positives and automatically prioritizing high-risk alerts, allowing teams to focus their expertise where it will have the most impact.

Example of AI-driven transaction monitoring in action

A credit union's ML models flagged unusual spending patterns across several customer accounts. While individual transactions were under typical fraud thresholds, the AI detected that these accounts were making identical purchases at the same online electronics retailers within minutes of each other — a sharp deviation from their normal spending patterns. The system automatically declined subsequent transactions from these accounts and alerted the fraud team. Investigation revealed that the accounts had been compromised in a coordinated account takeover attack targeting high-value electronics that could be quickly resold.

Best practices for ongoing monitoring

To maximize the benefits of AI-powered transaction monitoring, financial institutions should adhere to several key best practices that enhance accuracy, minimize false positives, and strengthen overall fraud risk management.

• Establish behavioral baselines — Train models on anonymized data to learn typical activity patterns across accounts, enabling better detection of true anomalies.
• Layer multiple signals — Combine transaction monitoring with device intelligence and behavioral analytics to get a more complete view of account activity.
• Continuously calibrate — Fine-tune model thresholds and validate alerts against outcomes to minimize false positives and avoid alert fatigue.

Want to better understand the threats AI is built to detect? Learn about the different types of fraud

3. Stopping coordinated fraud attacks with actionable AI

Seventy-one percent of financial organizations identified professional crime rings as their primary fraud threat, according to Alloy’s 2025 State of Fraud Benchmark Report. These coordinated groups can submit hundreds of applications in minutes, testing different combinations of stolen or synthetic identities until they find ones that work.

Actionable AI tools, such as Alloy’s Fraud Attack Radar (FAR), can help detect these coordinated attacks by analyzing application patterns in real-time. Rather than evaluating applications individually, FAR’s AI model looks across an institution's entire application queue to spot suspicious patterns — like multiple applications sharing IP addresses or similar email formats.

When FAR detects a potential fraud attack, it doesn't just alert the fraud team. It enables immediate action through "safe mode" workflows with heightened security measures that can be activated instantly. This allows institutions to maintain operations while addressing the threat, rather than shutting down application channels entirely.

Example of how AI stops fraud attacks in real-time

A credit union recently discovered multiple suspicious account applications originating from a Walmart parking lot. While each application looked normal in isolation, FAR detected that dozens of applications were coming from the same IP address. The system automatically flagged these applications as connected and enabled step-up verification requirements. As a result, the credit union quickly adapted, deploying an IP-based block that shut down the fraud ring’s activity before any fake accounts could be created.

Best practices for stopping attacks using AI fraud detection

Here are key practices for effective real-time attack detection, based on real feedback and experience:

• Have safe mode workflows ready — Prepare stricter verification policies that can be activated immediately when attacks are detected. This allows you to keep your funnel open for low-risk applicants while addressing threats.
• Add additional step-up verification to safety protocols — Adding friction, such as document verification during suspected attacks, often causes fraudsters to abandon their attempts.
• Monitor velocity patterns — Watch for unusual spikes in application volume or patterns like shared IP addresses, email formats, or device characteristics.
• Automate adaptive policy responses — Configure your workflows to automatically increase friction or trigger manual review based on changing risk levels without disrupting low-risk accounts.

See how Alloy’s Fraud Attack Radar helps stop coordinated fraud attacks

4. Connecting onboarding and ongoing data for holistic risk detection

While some AI tools look for patterns across multiple applications, others analyze individual customer behavior throughout their relationship with your organization. Together, these different types of AI models form the groundwork for holistic machine learning fraud protection.

One such model is Alloy's Entity Fraud Model (EFM). EFM creates a holistic risk profile by combining signals from onboarding data (e.g., PII verification, device info), transactional data, account changes, and non-monetary signals. Unlike traditional entity models that focus solely on transactions within specific payment rails, EFM aggregates risk signals across all payment rails and event types. This provides a complete risk view across high-risk touchpoints throughout the customer lifecycle, from account opening through ongoing activity.

This approach is especially powerful for detecting new account fraud, where fraudsters must be caught before they’ve had time to build a substantial transaction history. Alloy’s EFM can detect fraud patterns that might not trigger traditional alerts — like a dormant account suddenly showing unusual activity or subtle changes in behavior that could indicate account takeover (ATO).

Learn how to detect account takeover fraud

Example of how AI models like EFM work to connect onboarding and ongoing customer data

A newly opened account seemed slightly risky, but not enough for the issuing bank to deny the application. After several months of activity, the customer changed their mailing address and initiated several high-value transfers using different payment methods. While each action seemed innocent on its own, EFM connected them with the customer’s onboarding data and recognized this pattern as potentially fraudulent. The system triggered additional verification steps, catching new account fraud that transaction-only monitoring would have missed entirely.

Best practices for connecting onboarding and ongoing customer data

To achieve more comprehensive fraud prevention by connecting onboarding and ongoing data, follow these practices:

• Bridge historical and new behavioral signals — Link initial verification data from onboarding (like identity documents and device info) with ongoing behavioral patterns to create a continuous risk narrative for each customer.
• Enrich profiles with alternative data — Supplement traditional datasets with alternative sources, such as utility payments and employment verification. This creates a more complete picture of customer risk levels at onboarding and beyond.
• Leverage feedback data — Feed confirmed fraud outcomes (like closed accounts or transaction reversals) back into your models to improve their accuracy over time. This "closed loop" approach enables AI systems to learn from real fraud cases and adapt to emerging patterns.

Because fraud tactics are constantly evolving, financial organizations must continuously assess and adapt to varying levels of fraud risk across the customer lifecycle. Behavioral analysis helps surface suspicious activity that might not be detected by traditional rules-based systems, allowing institutions to tailor their defenses to each unique risk profile.

Many financial organizations don’t rely on a single data source to detect fraud, nor should they. That’s where a data orchestration platform like Alloy comes in. Alloy connects with dozens of trusted data providers to bring in hundreds of identity signals, behavioral insights, and machine learning models to make informed decisions. By combining data and risk scores from third-party sources with your own internal insights, Alloy helps power more accurate AI systems that improve anomaly detection at both the account and organizational level.

5. Optimizing fraud strategy and risk policies

Machine learning can help financial organizations improve their fraud prevention strategies over time by analyzing approval rates, denial rates, and manual review patterns. These systems can recommend policy updates that enhance fraud detection while maintaining efficient customer onboarding.

Example of policy optimization AI in action

After experiencing a surge in fraudulent loan applications, a fintech used its policy optimization model to analyze approval and denial trends. The system flagged a subtle correlation: applications with mismatched employer information and unverifiable email domains were far more likely to result in downstream fraud. By adjusting their workflow to trigger enhanced verification for those patterns, the fintech was able to stop a wave of synthetic identity attacks — improving fraud prevention without adding friction to legitimate users.

What previously took days of manual analysis to uncover (and might have slipped past traditional rules-based systems) can now be identified and stopped through real-time fraud detection, dramatically accelerating response time and reducing risk.

Best practices for policy optimization

Effective policy management requires a strategic approach that balances security with user experience:

• Monitor key metrics — Track approval rates, manual review rates, and fraud losses to understand how policy changes impact both security and customer experience.
• Test before deploying — Use historical data to validate policy changes before implementing them in production workflows.
• Stay adaptable — Regularly review and update policies as fraud tactics evolve and new patterns emerge.
• Balance friction and risk — Optimize policies to add friction selectively based on risk level rather than applying blanket rules.

Stop fraud attacks without stopping legitimate customers

As fraudsters' use of AI technology becomes more sophisticated, financial organizations need comprehensive defenses that evolve alongside threats. The most effective approach combines strong fundamentals with advanced AI capabilities, creating multiple layers of protection that streamline AI fraud detection and keep fraudsters from entering the ecosystem.

Learn more about Alloy's fraud prevention solution