How Behavioral Biometrics Enhances Fraud Prevention

Today’s fraud prevention processes are far smoother than they used to be. Automated alert systems and authentication measures are now standard, but these relatively simple, rules-based solutions are still imperfect. The growing field of behavioral biometrics offers a more reliable alternative.

Higher Detection Accuracy

Behavior biometrics’ accuracy is its primary advantage over conventional fraud detection. Researchers have developed systems capable of identifying fraud 95.5% of the time by tracking keystrokes alone. Such precision is difficult with a rules-based system, especially when it comes to unknown attack patterns.

Many older fraud prevention systems rely on known threat indicators. While such data is useful, it’s unreliable as the sole identifier of suspicious activity, as it cannot account for previously unseen methods. Behavioral analysis, by contrast, can notice anything falling outside the norm, whether security teams have recorded it before or not.

A broader scope becomes increasingly important as cybercrime continues to evolve. Accessible AI tools are making cyberattacks more common and convincing, so adaptation is necessary.

Fewer False Positives

Behavioral analysis is also more precise than conventional alternatives. Specifically, it produces fewer false positives.

Rules-based fraud prevention struggles to account for the nuances of real-world IT usage. Remote access to company documents may be unusual for one user but not for another, and different departments require varying levels of customer data. Without accounting for these subtleties, fraud alerts may flag innocuous behavior.

Some companies have reduced false positive rates by 66% after implementing behavioral biometrics. These improvements, in turn, reduce alert fatigue for IT teams and ensure they can spend additional time addressing actual problems.

How To Implement Behavioral Biometrics

Developers must approach behavioral biometrics with care to ensure they use the technology effectively. Here are some steps to follow for a more reliable fraud prevention system.

1. Determine Relevant Metrics

First, security teams should decide what their biometrics solution will track. Keystrokes are the most common metric, but AI models can also consider mouse movement, access patterns, location data, and sentence style.

Looking at a larger range of factors will make models versatile and lower false positives. However, analyzing a higher amount of user data introduces additional privacy concerns. Considering how quickly data privacy regulations are growing — five states enacted such laws in 2023 alone — it may be best to collect only what’s necessary for a reliable model.

2. Establish Acceptable Behavior Baselines

Next, the behavioral analytics engine must establish acceptable parameters for each user. This step is relatively straightforward — simply create a profile for how each person uses company devices or software.

The more data teams collect on their users, the more reliable these baselines will be. Once again, though, developers must consider privacy concerns and regulations. Each profile should also offer some wiggle room, as people’s real-world usage can change slightly between instances.

3. Design a Risk Scoring System

Behavioral biometrics solutions also need a way to triage fraud alerts. Not every instance of suspicious activity is equally concerning, and security teams cannot address all concerns immediately. Consequently, they need a risk-scoring system.

One method is to prioritize high-risk users. While 39% of nonprofit fraud comes from employees, executives — who account for fewer cases — tend to steal higher amounts. A behavioral analytics model should weigh suspicious activity from high-ranking accounts more heavily than that from entry-level workers. 

4. Inform and Involve Users

Before teams train and deploy their biometrics model, they need to get user consent. These security solutions require a lot of data and continuous monitoring, so affected parties must be aware and have the choice to opt-out at their own risk.

Similarly, teams should take the opportunity to inform users of security best practices to make these systems more reliable. A staggering 79% of account takeovers still begin with phishing. Emphasizing the need for phishing prevention can reduce the workload on the biometrics solution, ensuring a smoother rollout.

5. Adjust Over Time

Like all AI technologies, behavioral biometrics also requires ongoing monitoring and adjustment. In many cases, devs must regularly retrain the model to maintain high accuracy as behavioral patterns change over time.

The solution will need heavier tweaking in its initial stages to minimize false positives. As teams provide this oversight, though, it should become additionally reliable over time. However, improvement is only possible with ongoing benchmarking, so constant vigilance is necessary.

Behavioral Biometrics Is the Next Frontier in Fraud Prevention

Fraud prevention will become increasingly pressing and challenging as new technologies enable more sophisticated cybercrime. Behavioral biometrics may become a crucial defense amid that trend.

While behavioral analytics models are far from perfect, they offer significant advantages over rules-based alternatives. Learning to build and deploy these solutions today will prepare security teams for tomorrow’s cybercrime landscape.