How Data Science Helps Fight Synthetic Identity Fraud

Various technological advancements have made synthetic identity theft easier than ever. At the same time, massive breaches are exposing sensitive personally identifiable information (PII) at an unnerving rate. Can data science techniques protect individuals’ identities?

What Is Synthetic Identity Fraud?

Synthetic identity fraud is a type of identity theft that occurs when a fraudster uses some combination of real and fake credentials to steal someone’s identity and commit financial fraud. For example, they may use a genuine Social Security number and birth date but make up a name and an address.

How is synthetic identity fraud different than identity fraud? The main difference is the use of counterfeit information. The bad actor creates fake names, contact information, or biometric data instead of stealing every piece of PII they can. This makes it easier to pretend to be someone else, allowing them to go unnoticed for longer.

Synthetic fraud happens more often than most people think. Social Security numbers sell for about $3 on average on the dark web. When you add other private data, the price jumps to about $8.

Synthetic identity theft is so common because intercepting and exfiltrating data is relatively easy. Various technological advancements have lowered the entry barriers to fraud even further. For instance, because of artificial intelligence (AI), a bad actor only needs one minute of audio and one photo to create a deepfake.

What Happens to Victims of Synthetic Identity Fraud?

Fraudsters can create dozens of synthetic identities if they have enough data. They can take out loans or open credit cards in someone else’s name, making it essential for people who notice potentially fraudulent activity to freeze their credit with the three major credit bureaus. Victims often only realize what’s happening when they get denied benefits, receive mail from debt collectors, or get arrested on a bogus warrant.

Most people don’t think they’ll become victims of identity theft. However, synthetic identity theft is easier because it requires less genuine information. Since there was a massive data breach in 2024 that compromised 2.9 billion records — including Social Security numbers, names, and contact details — individuals must be vigilant.

How Can Data Science Combat Synthetic Identity Fraud?

Almost everything a person does generates an enormous amount of data. When considered as a whole, that information is as unique as a fingerprint. Even if they’re not taking out loans or making purchases, their behaviors reveal a lot about who they are. When they go online, everything from their typing speed to their mouse movements leaves a trace.

Companies can use data science techniques to turn this abundance of information into actionable insights. For example, they could use a statistical model to determine whether someone’s buying behavior indicates identity fraud.

Since synthetic identity fraud relies on fake details, data science is vital. For reference, traditional fraud models were unsuccessful at catching 85%-95% of likely synthetic identities, according to the United States Federal Reserve. Whether businesses use pattern matching, machine learning (ML), or forecasting, their approach will outperform conventional rule-based systems.

Data Science Techniques to Use Against Fraudsters

There are several data science techniques for preventing and detecting synthetic identity fraud.

Evaluation

To tell the difference between fake and real identities, companies must establish a baseline. Pattern recognition helps them build personalized profiles, enabling them to monitor online behaviors for outliers and abnormalities.

With ML-driven predictive modeling, data science professionals can evaluate how likely individuals are to become victims of synthetic identity fraud. This allows them to prioritize intervention and incident response.

Prevention

Behavioral analysis uses AI and behavior analytics. When used in fraud prevention, it helps uncover fraudsters. For example, a system could flag a user holding their phone at an unusual angle and swiping in a robotic pattern. These signs could mean a bot is in charge and the device is sitting on a rack among dozens of others.

A machine learning decision tree can help data science professionals prevent synthetic identity theft. One study found this type of model could achieve 99.7% precision, 92.25% accuracy, and 81.49% recall on average.

A neural network operates comparably but on a higher level. It recognizes hidden relationships in datasets by mimicking the way the human brain works. Factors like swiping speed, time spent online, credit card activity, and eye tracking can reveal if the person behind the screen is a legitimate account holder or a fraudster.

Detection

A time series analysis can help companies detect anomalies. Whether they track individuals’ credit scores, loan activity, or purchasing patterns, the background of historical data will make outliers stand out.

Another useful detection tool is data visualization. Firms can use it to enhance fraud detection by making structures and relationships more visible. Information is often more digestible in a graph or chart than in a spreadsheet — especially when big data is involved.

Eliminating Synthetic Identity Fraud With Data Science

Information like Social Security numbers is easily leaked, yet access controls and authentication measures are lacking. As long as this data remains a cornerstone of credit scores and finances, synthetic identity theft will remain a problem. Fortunately, data science techniques can help professionals assess risk, detect fraudsters, and prevent fraud.