What Is Fraud Analytics?

Updated: June 18, 2026 by Theresa Nguyen 5 min read November 6, 2023

At A Glance

Fraud analytics is the use of data, artificial intelligence and machine learning to detect, predict and prevent fraudulent activity in real time.

As the sophistication of fraudulent schemes increases, so must the sophistication of your fraud detection analytics. This is especially important in an uncertain economic environment that breeds opportunities for fraud. It’s no longer enough to rely on old techniques that worked in the past. Instead, you need to be plugged into machine learning, artificial intelligence (AI) and real-time monitoring to stay ahead of criminal attempts. Your customers have come to expect cutting-edge security, and fraud analytics is the best way to meet — and surpass — those expectations. Leveraging these analytics can help your business better understand fraud techniques, uncover hidden insights and make more strategic decisions.

What is fraud analytics?

Fraud analytics refers to the idea of preventing fraud through sophisticated data analysis that utilizes tools like machine learning, data mining and predictive AI.These services can analyze patterns and monitor for anomalies that signal fraud attempts. While at first glance this may sound like a lot of work, it’s necessary in today’s technologically savvy culture.

Fraud attempts are becoming more sophisticated, and your fraud detection services must do the same to keep up.

Why is fraud analytics so important?

According to the Experian® 2025 US Identity and Fraud Report, fraud is a growing issue that businesses cannot ignore, especially in an environment where economic uncertainty provides a breeding ground for fraudsters. According to the FTC, consumers reported fraud losses of $15.9 billion in 2025 — up from $12.5 billion in 2024.

Understandably, 57 percent of consumers are still concerned about online security. Their worries range from authorized push payment scams (such as phishing emails) to online privacy, identity theft and stolen credit cards.

Unfortunately, while 85 percent of surveyed businesses feel confident that their fraud controls align with consumer expectations, 57 percent of consumers are still concerned about doing things online. There’s a lot of unearned confidence out there that can leave businesses vulnerable to attack, especially with nearly 70 percent of businesses admitting an increase in fraud loss in recent years.

The types of fraud that businesses most frequently encounter include:

Authorized push payment fraud: Phishing emails and other schemes that persuade consumers to deposit funds into fraudulent accounts.
Transactional payment fraud: When fraudulent actors steal credit card or bank account information, for example, to make unauthorized payments.
Account takeover: When a fraudster gains access to an account that doesn’t belong to them and changes login details to make unauthorized transactions.
First-party fraud: When an account holder uses their own account to commit fraud, like misrepresenting their income to get a lower loan rate.
Identity theft: Any time a person’s private information is used to steal their identity.
Synthetic identity theft: When someone combines real and fake personal data to create an identity that’s used to commit fraud.

How can fraud analytics be used to help your business?

More than 85% of consumers expect businesses to respond to their security and fraud concerns. A good portion of them are even ready to share their personal data with trusted sources to help make that happen. This means that investing in risk and fraud analytics is not only vital for keeping your business and customer data secure, but it will score points with your consumers as well.

So how can your business utilize fraud analytics? Machine learning is a great place to start. Rather than relying on outdated rules-based analytic models, machine learning can vastly increase your speed in identifying fraud attempts. This means that when a new fraudulent trend emerges, your machine learning software can pinpoint it fast and flag your security team. Machine learning also lets you automatically analyze large data sets across your entire customer portfolio, improving customer experiences and your response time.

In general, the best way for your business to use fraud analytics is by utilizing a multi-layered approach, such as the robust fraud management solutions offered by Experian. Instead of a one-size-fits-all solution, Experian lets you customize a framework of physical and digital data security that matches your business needs. This framework includes a cloud-based platform, machine learning for streamlined data analytics, biometrics and other robust identity-authentication tools, real-time alerts and end-to-end integration.

How Experian can help

Experian’s platform of fraud prevention solutions and advanced data analytics allows you to be at the forefront of fraud detection. The platform includes options to help:

Reduce fraud

Detect fraud earlier in the customer journey using behavioral and identity insights.

Streamline operations

Reduce false positives while maintaining strong fraud controls.

Prevent AI-driven attacks

Identify sophisticated threats such as bots, account takeover and first-party fraud.

Your business’s fraud analytics system needs to increase in sophistication faster than fraudsters are fine-tuning their own approaches. Experian’s robust analytics solutions utilize extensive consumer and commercial data that can be customized to your business’s unique security needs.