Protect Your Lending App with Device Fingerprinting, App Behavioral Data, and Face Recognition

Published on: 2024-08-10 19:03:58

Lending apps face fraud and identity theft. Protecting customer data, and keeping credit underwriting reliable, matters. Device fingerprinting is a practical place to start.

Fraud and identity theft target lending apps. Customer data needs protection, and credit underwriting needs to remain reliable. Device fingerprinting is a practical control that raises the bar.

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Device Fingerprinting and Profiling: Collecting Unique Technical Information

Device fingerprinting means collecting distinct technical details about a device, including its make and model, operating system, and hardware specifications.

Use that profile to identify and track the device across sessions. This makes impersonation and multi-account abuse harder.

Detecting Potential Abuse with App Behavioral Data

Start with the app flow. Record when each screen or step starts and ends.

Then flag unusual patterns, including exact repeated timings or navigation that appears nonhuman. These patterns can suggest bots or other automated tools.

Collecting IP Addresses and Tracking Network Changes

Capture IP addresses. Monitor network changes as well.

This can help you spot suspicious access and block unauthorized use of the lending app. Add context with third-party sources such as AbuseIPDB, including information about the connection and whether an IP address is known for bot activity.

These signals improve detection. They also speed up investigation.

Detecting Potential Abuse with Mobile Device Information

Gyroscope data and battery level can expose automation across many devices. A stationary device that stays plugged in for long periods may be part of a device farm generating fraudulent applications.

Monitoring Signal Strength and Network Information

Monitor signal strength and network details for additional context. A cluster of devices on the same Wi-Fi network can indicate coordinated activity.

That should trigger a review.

Improving Detection of Potentially Fraudulent Activity with MAC Address Scanning

Scan for MAC addresses, then infer the manufacturer from the prefix. Patterns in these identifiers can reveal farms or scripted setups using synthetic or stolen identities to submit loans that will not be repaid.

Improving Security with Face Recognition and Liveness Detection

Face recognition can strengthen credit underwriting. Compare the applicant’s face with the portrait on their identification document to verify the applicant’s identity and confirm that the applicant is the person applying.

Add liveness detection to prevent spoofing. Confirm that a real person is present, rather than a replayed video or static photo.

Validate depth and natural movement during capture. Check the photo metadata received by your API endpoint, then cross-reference it with the device profile.

Inconsistencies can indicate a spoofed or manipulated image.

Conclusion

The goal is to protect the lending app and its customers from fraud. Combine device fingerprinting, app behavioral data, and face recognition with liveness detection to support accurate, reliable underwriting.

Track IP addresses, network changes, and signal strength. Stay alert to threats and reduce abuse.

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