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

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

Fraud and identity theft target lending apps. Protecting customer data and keeping credit underwriting reliable matters. Device fingerprinting is a practical control that raises the bar.

Device Fingerprinting and Profiling: Collecting Unique Technical Information

Device fingerprinting means collecting distinct technical details about a device, including make and model, operating system, and hardware specifications. You can use this profile to identify and track a device across sessions, which makes impersonation and multi-account abuse harder.

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Detecting Potential Abuse with App Behavioral Data

Collect behavioral signals from the app flow. Record when each screen or step starts and ends. Then flag unusual patterns, such as exact repeated timings or nonhuman navigation, that suggest bots or other automated tools.

Collecting IP Addresses and Tracking Network Changes

Capture IP addresses and monitor network changes. This helps you spot suspicious access and block unauthorized use of the lending app.

Use third-party sources such as AbuseIPDB to add context about the connection and whether an IP is known for bot activity. These signals improve detection and speed up investigation.

Detecting Potential Abuse with Mobile Device Information

Gyroscope data and battery level can expose automation at scale. 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 to add context. A cluster of devices on the same Wi-Fi network can indicate coordinated activity and should trigger a review.

Improving Detection of Potentially Fraudulent Activity with MAC Address Scanning

Scan for MAC addresses and infer the manufacturer from the prefix. Patterns in these identifiers can reveal farms or scripted setups that use 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 to the portrait on their identification document to verify identity and confirm the applicant is the person applying.

Add liveness detection to prevent spoofing. Confirm that a real person is present, not a replayed video or a static photo.

Validate depth and natural movement during capture. Also check the photo metadata received by your API endpoint and 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 to stay alert to threats and reduce abuse.

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