Securing Your Lending App: Device Fingerprinting, App Behavioral Data, and Face Recognition
Securing your lending app for great credit underwriting is essential in today's digital age. With the proliferation of fraud and identity theft, it is important to take every precaution to ensure the safety and security of your customers' information. One key way to do this is through the use of device fingerprinting.
Device Fingerprinting and Profiling: Collecting Unique Technical Information
Device fingerprinting involves collecting unique technical information about a device, such as the make and model, operating system, and hardware specifications. This information can be used to identify and track a specific device, making it more difficult for fraudulent actors to impersonate a legitimate user.
Detecting Potential Abuse with App Behavioral Data
In addition to device fingerprinting, collecting app behavioral data can also be helpful in detecting potential abuse. By timestamping the beginning and end of each screen and step in the lending process, you can monitor for unusual patterns of behavior that may indicate the use of bots or other automated tools.
Collecting IP Addresses and Tracking Network Changes
Other important security measures to consider include collecting IP addresses and tracking changes in the network. This can help you identify suspicious activity and prevent unauthorized access to your lending app.
You can use third-party resources such as AbuseIPDB to enrich your understanding of the type of connection and whether an IP has been identified as a malicious IP used by bots. This can provide valuable insights and help you more effectively detect and prevent fraudulent activity on your lending app.
Detecting Potential Abuse with Mobile Device Information
Collecting information from the gyroscope and battery levels of a mobile device can also be useful in detecting potential abuse. For example, if a device is stationary and continuously being charged, it may be part of a "device farm" used to fraudulently generate loan applications.
Monitoring Signal Strength and Network Information
Finally, monitoring signal strength and network information can provide additional details that can help you spot potentially fraudulent activity. For example, if you notice a concentrated number of devices on the same Wi-Fi network, this could be a red flag that warrants further investigation.
Enhancing Detection of Potentially Fraudulent Activity with MAC Address Scanning
To further enhance your ability to detect potentially fraudulent activity, you can also consider scanning for MAC addresses and profiling the manufacturer based on the MAC address. This can help you identify suspicious patterns of activity and protect your lending app from bot farms that use synthetic or stolen identities to apply for loans that will never be repaid.
Enhancing Security with Face Recognition and Liveliness Detection
One additional security measure that can be useful in credit underwriting is the use of face recognition technology. By comparing the face of the applicant with a photo on their identification document, you can verify their identity and ensure that the loan application is being made by a legitimate user.
To further enhance the security of this process, it is important to implement "liveliness detection" during the face recognition process. This involves verifying that the applicant is physically present and not presenting a spoofed video or photo.
To do this, you can validate the depth and fluidity of movements to ensure that the face being presented is that of a live person. Additionally, you can check the metadata of the photo that arrives at your API endpoint and cross-reference it with the information collected from profiling the device. This can help you detect any discrepancies or inconsistencies that may indicate the use of a spoofed image.
Conclusion
It is important to remember that the ultimate goal of these security measures is to protect your lending app and your customers from fraudulent activity. By implementing device fingerprinting, app behavioral data collection, and face recognition with liveliness detection, you can create a secure lending app that accurately and reliably performs credit underwriting. Additionally, by tracking IP addresses, network changes, and signal strength, you can stay vigilant against potential threats and keep your app safe from abuse.
