GenAI is a potent weapon to fight fraud in financial services

Fraud is a pervasive and costly issue for the financial services industry, affecting both institutions and their customers. In 2025, global fraud losses are expected to top $485 billion. Financial services companies spent $43.8 billion fighting fraud in 2024. The financial impact extends beyond direct monetary losses, as institutions also face increased operating costs, regulatory scrutiny, and reputational damage.

And yet, fraudsters keep evolving, especially in digital channels, which now account for half of all fraud losses in the U.S. and Canada.

The playbook for fraud keeps changing. For example, synthetic identity fraud—where criminals patch together real and fake data to fabricate identities—surged by 31% in 2024. The message is clear: traditional fraud detection methods aren’t enough. It’s time for financial institutions to rethink their defenses, and GenAI may be the edge you need.

Financial services firms and fraudsters are locked in a pitched battle

Financial institutions have leaned on rules-based systems and machine learning (ML) models to flag suspicious activity. These tools pick up on anomalies, like small “test” transactions that often signal a bigger fraud attempt. Multi-factor authentication (MFA) and real-time monitoring add extra layers of protection.

But fraudsters are getting more sophisticated in their methods, and AI is a weapon in their arsenal. Hyper-realistic phishing emails, deepfake voice and video scams, and AI-powered identity theft are making fraud harder to detect and easier to execute. A finance worker in Hong Kong recently wired $25 million after a deepfake video impersonated his CFO. Instant payment systems like FedNow are also being exploited, giving criminals a way to move stolen funds before banks can react.

Financial institutions need to evolve faster than fraudsters. GenAI offers a way to fight back.

GenAI is a powerful weapon to fight fraud

GenAI is still evolving, but it’s already proving to be a useful tool for many reasons. For example, unlike static systems that react to known threats, GenAI can proactively anticipate new fraud tactics by simulating potential scenarios based on learned patterns. But that’s not all.

GenAI excels with anomaly detection

GenAI models analyze vast amounts of transactional data to establish baseline behavioral patterns for customers and flag deviations that may indicate fraudulent activity. Fraudsters trying to mimic a customer's spending habits? GenAI picks up on the subtle inconsistencies traditional systems miss. This proactive approach is particularly effective in identifying new types of fraud that have not yet been encountered.

Synthetic data protects customer privacy

Training AI on real customer data is risky, but GenAI offers a workaround. It can generate synthetic datasets that mirror real-world fraud scenarios without exposing sensitive information. This allows institutions to refine your fraud detection models while staying compliant with privacy regulations. American Express has already tapped into this approach to strengthen its fraud prevention capabilities.

GenAI sharpens accuracy

GenAI also excels in reducing false positives—a common issue in traditional detection systems where legitimate transactions are mistakenly flagged as fraudulent. By better distinguishing between genuine and suspicious activities, GenAI improves customer experience while reducing operational inefficiencies. PayPal has used advanced AI algorithms to nearly halve its loss rate from fraud while increasing its payment volume.

Real-time monitoring gets smarter

GenAI enhances transaction monitoring by assigning dynamic risk scores in real time. This means high-risk transactions get flagged instantly while legitimate payments go through without unnecessary friction. Visa’s Account Attack Intelligence (VAAI) tool is already showing how AI-driven risk assessment can prevent fraud before it happens.

GenAI automates fraud investigations

Fighting fraud can be overwhelming for your security team, but GenAI lightens the load. It can automate data analysis, generate fraud reports, and identify patterns that even seasoned investigators might miss. Swedbank uses generative adversarial networks (GANs) to map hidden relationships between legal and fraudulent transactions.

GenAI helps pressure-test fraud defenses

GenAI also helps you to simulate potential fraud scenarios and stress-test your detection systems against emerging threats. Think of generative AI as an in-house hacker. By simulating new fraud tactics and testing detection systems against them, you can identify vulnerabilities in your systems and train detection models to recognize new tactics before bad actors can hurt you.

GenAI anticipates fraud before it happens

Predictive analytics powered by GenAI enables youto stay ahead of emerging threats. GenAI analyzes historical data alongside real-time patterns to pinpoint where fraud is most likely to occur next—giving you a chance to intervene before the damage is done.

GenAI is not a perfect solution, but you can manage its risks

GenAI isn’t a magic bullet. It comes with its own set of risks, and you need to tread carefully. For example, GenAI sometimes generates misleading or inaccurate conclusions. In a high-stakes industry like financial services, these errors could lead to wrongful fraud accusations or missed red flags.

And if a model is trained on biased data, it could unfairly target people or reinforce systemic disparities in fraud detection. You need to actively mitigate bias by using diverse and representative datasets.

In addition, GenAI needs massive amounts of data to function effectively, but mishandling that data could create more problems than it solves. It’s essential that you implement strict encryption and access controls to protect customer information.

To navigate these challenges, we recommend that you follow some best practices.

• Conduct audits. AI models should be regularly audited for accuracy and fairness. Explainable AI (XAI) frameworks can help ensure transparency in decision-making, which builds trust with customers and regulators. 

• Mitigate bias. AI training should incorporate diverse datasets, and you should continuously monitor models to catch bias before it causes harm.

• Secure your data. Encryption and strict access protocols should be non-negotiable when handling customer data.

It’s also essential to keep learning. As noted, fraudsters keep developing new tools, and GenAI is constantly evolving. Adopt a learning mindset to stay on top of emerging trends.

A frontier AI data foundry can help you fight fraud with GenAI

A frontier AI data foundry platform can help you apply GenAI to combat fraud by providing tailored, scalable, and secure solutions. A frontier AI data foundry platform integrates GenAI into your operations while remaining compliant with regulations, among other benefits. Such a platform provides infrastructure to stress-test fraud defenses, simulate emerging threats, and refine detection models continuously.

A frontier AI data foundry platform also helps ensure transparency, fairness, and adaptability in fraud prevention strategies by combining advanced tools like large language models (LLMs) with explainable AI frameworks. As fraud tactics evolve rapidly, the expertise of a frontier AI data foundry platform enables you to stay ahead of bad actors, reduce operational inefficiencies, and build customer trust through proactive and innovative fraud mitigation measures.

Learn more about Centific’s frontier AI data foundry platform.