Risk scoring metrics: what fraud teams should measure (beyond “blocks”)

Risk scoring is an important element of security for digital devices. It gives a numerical score to each user's actions to assess the probability of a risk, which allows the fraud teams to prevent attacks without affecting legitimate customers.

What is Risk Scoring in Fraud Detection?

Risk scoring is an easy way to calculate a "credit score" for digital interactions. Every time a person sign-up, logs into the system or makes a purchase the system analyzes many data elements in order to determine the score.

A score of high value usually indicates that the transaction appears suspicious (like an automated system or a stolen identity) however a lower score indicates that the user is reliable. This process is automated and helps teams determine in milliseconds if they should accept a transaction, mark it to be reviewed, or block it completely.

Why Risk Scoring is Critical Today

Fraud isn't just related to stolen credit cards, it also involves advanced bot attack techniques, accounts takeovers and fake identities. Rules that are static (like "block all IP addresses from X country") are no longer sufficient as they make real users miserable and do not catch sophisticated fraudsters.

In the present, fraud teams require real-time risk scoring in order to remain ahead. It allows for "Dynamic Friction"--the capability to let users who are good through in a flash, but add additional security measures (like MFA) only for those who appear to be risky.

Key Risk Scoring Metrics Fraud Teams Should Track

In order to build an accurate risk engine, you have to monitor the right indicators. Here are the key measures which modern-day fraud departments are focusing on:

1. Device Intelligence Signals

The device is usually the most visible aspect of an exchange. You must keep track of:

- Device Fingerprinting: Does this device belong to an established device for this particular user?

- EmulatorDetection: The emulator detects if the person is running a fake mobile on a PC?

- Hardware Spoofing: Do they cover their real level of screen's resolution and battery levels to appear like another person?

2. Behavioral Signals

The way a user interacts with your app is much harder to falsify than an account password.

- Keystroke Dynamics: Does the speed of typing be a human, or script?

- The pattern of navigation: Does the person making their way through the checkout process too quickly to be considered human?

- Touch Interactions on mobile: do the swipe and tap gestures akin to the real hand?

3. Network and Location Signals

The location where the request comes from will provide a crucial context.

- Security and VPN: Does the user hide their real location behind a datacenter IP address?

- The IP Speed: Have these IP addresses tried to set up 50 accounts within the last hour?

- Location Spoofing: Does the GPS information match the IP address's location?

4. Historical Risk Patterns

Past behavior can be a good indicator of the risk that is to come in the future.

- Account Age: Accounts with a younger age generally come with higher risks than older ones.

- Transcript History: Does the purchase much more than the average user's spending?

- International Blacklists: Have the email or phone number been linked with other frauds?

Challenges with Traditional Risk Scoring

Most traditional security systems from the past depend on static techniques. 

The issue? The result is that there are a lot of false positives. A loyal customer to a vacation destination could be denied access and lose revenues and a negative image of the brand. Additionally, these systems typically depend on limited information and do not recognize those subtle "risk signals" that modern attackers employ to evade the basic security features.

A Modern Approach to Risk Scoring

The most efficient fraud teams are advancing towards multi-layered Detection. Instead of relying solely on one, or even two points they blend network, device and behavioral data to create an all-in-one real-time score.

This method is dynamic. It doesn't only examine "what" the data is, it's "how" it behaves over time. This eliminates the need for manual checks and guarantees that the security of your business expands.

How DeepID Smart Signals Improves Risk Scoring

While traditional tools offer raw information, Smart Signals provides high-fidelity intelligence that is directly integrated into your current risk management system.

Better Signal Quality

Smart Signals analyzes thousands of aspects--from deep device telemetry all the way to network health, giving you a an accurate picture of the user. It can detect even the most sophisticated techniques for spoofing that traditional tools do not detect.

Improved Accuracy

With more precise data fed in your risk score system, you can reduce your risk scoring model's "gray area." There are less "maybe" scores and more precise "yes" or "no" responses, which means less manual effort on your team of analysts.

Reduced False Positives

Since Smart Signals understands the difference between a privacy-conscious person and a malicious attack and can help you block your most valuable customers. It lets you remove any friction for trustworthy users and maintain an "zero-trust" posture against threats.

Best Practices for Fraud Teams

If you're looking to improve your risk-score today take these four tips in your mind:

Check Your Data Sources: Make sure you're not just looking at the IP address. Integrate behavioral and device-related signals to get a 360-degree perspective.

Check and repeat: The tactics of fraudsters change every week. Check your scoring thresholds frequently to ensure they remain useful.

Focus on the User Experience: Try to achieve "invisible security." The most effective risk scoring occurs in the background, with no user even knowing.

Consolidate Your Stack: Make use of tools that provide deep integration. The faster your signals get to the risk engines, the more quickly you'll be able stop a threat.

Conclusion

Risk scoring isn't an "set it and forget it" task, but rather an ongoing strategy. By focusing on top-quality metrics such as device intelligence or behavioral indicators the fraud teams can secure their bottom line while delivering an uncomplicated experience for users. Making a more intelligent risk engine now is the most effective way to protect your company from the threats of tomorrow's fraud.