We know that the use of generative machine learning and artificial intelligence has transformed the way mortgage underwriting services work. They have effectively become a tool that many services have at least thought about integrating, Be it fraud detection, aiding verification, or enhanced customer service. However, cybersecurity remains a major risk for all data and verification processes, be it because of the use of artificial intelligence or not. In this article, I will walk you down some of the common cybersecurity risks that mortgage underwriting services face.
What are the cyber attacks that mortgage underwriting can be subject to?
Cyberattacks occur in several ways, and any data and verification information is especially sensitive to it. In the case of mortgaging, this becomes a financial risk, too.
Protecting Sensitive Data
Safeguarding classified user data is one of the most important parts of any digital underwriting or archive, regardless of its services. These include the user’s personal details like payment information, location, etc. Information privacy is one of the most important aspects of data dissemination and must be upheld at all times.
Preventing Cheating and Fraud
Mortgage underwriting processes are not without their share of fraudulent activities, such as identity theft, the theft of payment details, and breaching paywalls. That’s why it is necessary for such spaces to implement firewalls to protect and encrypt the data of clients as well as customers.
Preventing Cyber Attacks
It’s important to hone in and prioritize the prevention of cyber attacks because once they start to occur, situations just get worse. With looming threats like phishing, identity theft, etc., mortgage underwriting services must make sure breaches do not occur in the first place.
What Are some security risks associated with AI mortgage underwriting?
Among the several ethical violations of AI, two remain glaring in this context:
Artificial intelligence is pretty harmful without human supervision. This is because it relies on data scraping, which means its learning even from sensitive information that should otherwise be encrypted.
Security and privacy pose some of the biggest concerns for the use of generative machine learning. This is because the process of machine learning needs constant training.
Wrapping Up
This brings us to a close on our article covering some of the major cybersecurity risks associated with mortgage underwriting. While there is no such thing as a benign breach, some are certainly of greater consequences than others, and when it comes to financial decisions and mortgaging information, it all has high stakes. Mortgage underwriting processes must consider these and build robust security systems that protect their clients and users. These include ensuring user data is encrypted, managing access to ensure the classified material reaches the right owners, and having regular software updates.
