Artificial Intelligence (AI) is the new frontier for the mortgage industry. As it advances, it offers more ways to streamline the mortgage process and improve the customer experience. Despite concerns, mortgage industry professionals will always be needed as, for the foreseeable future, property ownership will be one of the major players of the US economy. Instead of replacing them, AI will work to support mortgage professionals, helping them to manage immense amounts of data, make decisions, and become more accessible and equitable for all stakeholders.
The Role of AI in the Mortgage Industry
AI, or Artificial Intelligence, is the ability of machines to do things that would normally require human intelligence. AI is quickly becoming ubiquitous across many industries and mortgage is no exception. Its capabilities have and continue to significantly impact the way mortgages are processed and serviced.
Benefits of Integrating AI in Mortgage Processes
The use of AI in the mortgage industry has many benefits for both lenders and borrowers:
1. Speed. AI automation can help streamline the approval process, and extract information from documents more quickly than manual data entry.
2. Accuracy. Although AI is not perfect, it can reduce the incidence of human error in data entry and the calculations used to determine loan eligibility—making loan decisions more accurate.
3. Reduced Overhead. Automation reduces the need for salaried employees and other duties usually done by humans. AI can also help with losses due to fraud.
4. Sustainability. AI could be considered more environmentally friendly due to the reduction in paper use.
Overall, AI works to modernize the mortgage process, which helps provide a more satisfactory experience for clients.
Disadvantages of Using AI in the Mortgage Process
As with most new developments, there can be downsides to reliance on AI for mortgage processing. Let’s look at the key considerations:
1. Privacy. Data privacy is often a key concern in any type of technology-based program. Since the mortgage industry deals with financial information, this issue may cause leaders to be even more averse to using AI.
2. Compliance. Mortgage industry regulations mean that any automation companies use must comply with the industry’s laws and compliance standards. Shaping a system this way can become complex.
3. Bias. Some machine learning models can rely on historical data that was inherently biased to draw conclusions and make decisions. This can run up against anti-discrimination laws.
4. Customer Experience Concerns. Borrowers may not be comfortable with an algorithm handling their information and making such a weighty decision about their future.
A successful implementation of AI will require a high level of planning and monitoring to address pitfalls, as well as the ability to make ongoing adjustments that make automated systems more reliable.
AI and Customer Experience in Mortgages
The experience that a client has while applying for a mortgage can arguably make or break a mortgage company’s business. AI can be used to enhance the mortgage journey for customers in several ways.
1. Personalized Mortgage Recommendations. In the mortgage industry, there are many programs for borrowers to procure funding. Automation is able to analyze a client’s data, including their financial behavior, and help align them with mortgage products that fit their personal situation.
2. Virtual Assistants. Virtual assistants offer customer support and can answer questions that customers may have during or after hours.
3. Natural Language Processing (Chatbots). Chatbots use natural language procession (NLP) and have conversation capabilities. They can answer questions, offer application guidance, and even spell out terms and conditions.
4. Accessibility. AI can make the mortgage application process more exclusive by being able to adjust to borrowers who need additional assistance.
5. Up-to-Date Information. AI can use real-time information to give borrowers updates on their approval status and other information.
AI With Mortgage Loan Servicing & Underwriting
Underwriting and loan servicing are two crucial parts of the mortgage loan process. Underwriting takes place before final approval and loan servicing takes place for the life of the loan. Artificial intelligence can play important roles in both functions.
Underwriting
The role that AI plays in assessing borrower eligibility makes it a desirable option for the underwriting process. Credit scoring, income, and debt-to-income ratio are just a few areas where AI can make analysis more efficient and accurate for lenders. Other ways that AI moves the needle in underwriting include:
1. Risk Management. AI can detect unusual patterns and other transaction data that could be a sign of fraud. Lenders who have this information can be more astute in their processing of certain applications and mitigate the risks associated with fraud.
2. Risk Assessment. The algorithmic models used in underwriting can predict borrower behavior. Whether they are likely to default or pay early, AI can determine this ahead of time and help underwriters keep these risks in mind during the approval process.
3. Non-Traditional Borrower Analysis. Data models that are incorporated can help determine the creditworthiness of borrowers who don’t have traditional credit or employment histories.
Loan Servicing
AI in loan servicing should be used as a supplement to help enhance the customer experience—human communication should never be totally eradicated. Here are a few ways that artificial intelligence may impact mortgage loan servicing:
1. Customer Service. Chatbots, which offer a higher level of communication capability than virtual assistants, are able to provide immediate responses to questions that often emerge in loan servicing.
2. Loan Modifications. AI can also determine eligibility for borrowers who are in need of forbearance arrangements or loan modifications in a down economy.
3. Predictive Analytics For Customer Service. Predictive analytics can help lenders identify which borrowers may default or be delinquent early on, so that lenders can be proactive about offering them support.
4. Payment Processing. Automation can take the work and risk of errors out of collecting, recording, and reconciling payment.
5. Amortization Calculations. Borrowers can access optimized amortization schedules that show them how their principal and interest costs can be affected by their payment behavior.
Bottom line, AI helps lenders serve borrowers in a faster, more accurate, and personalized manner—all while protecting their portfolios by approving loans based on comprehensive data.
Ensuring Regulatory Compliance With AI
Due the severity of compliance requirements within the mortgage industry, AI should only be utilized as a supplemental tool. Lenders, institutions, and even individual MLOs must take steps to ensure that their businesses remain compliant.
1. Know AI-Specific Regulations. Lenders should know any federal or local regulations that specifically address the use of AI in the financial services industry.
2. Audit AI Systems For Compliance. Artificial intelligence models are programmed by humans; so, they can fall victim to human biases. It’s important to conduct regular assessment of the AI systems to ensure there are no features of the algorithm that encourage discrimination, privacy breaches, and other regulatory problems.
3. Conduct Proper Training. Staff members should be well-trained on any AI systems used in the office. Not only should they have a thorough understanding of how the system works, but they should be reminded that they still must rely on their compliance training, as well.
4. Invest in Cybersecurity. Data privacy and security are important. Make sure that in addition to complying with data privacy regulations, the system is encrypted and has strong cybersecurity protocols.
These are some of the most important steps in proactively addressing compliance. In doing so, lenders not only protect borrowers, but also their businesses.
Overcoming Challenges & Limitations of AI in Mortgages
As with all technology adoption, the use of AI will come with its challenges. The key is to approach the integration of artificial intelligence strategically.
1. Ethics. Communicate with vendors about the importance of the ethical considerations of the system design. Systems should be designed with regulatory and ethical standards in mind and updated regularly to remain compliant with current industry standards.
2. Data Validity. Companies should invest in protocols that ensure that data inputs are clean and accurate. Periodic audits of AI systems should be used to make adjustments and updates as needed.
3. User Error. Those who use systems regularly should have, at minimum, functional knowledge of how the systems should be used. There should also be investment in recruiting experts who can help monitor AI systems in-house without needing to rely on an outside vendor.
4. Borrower Concerns. Borrowers should be aware that AI will be used, at least in part, to process their information. Educating them on how the systems in place will affect their application process, underwriting, and even servicing is an important step in supporting them and the MLO/borrower relationship. Just as important is getting feedback from borrowers as a way to audit those systems.
The Outlook For AI in the Mortgage Industry
The outlook for AI within the mortgage industry could be described as dynamic. As technology evolves, lenders will be able to make quicker and more accurate lending decisions, while borrowers will experience a more uneventful mortgage journey. However, this advancement will also require a heightened commitment to the ethics, legalities, and competency of mortgage professionals.
As much as the practice of providing mortgages is a business, it is also a relationship-centered transaction. AI, at its best, should be a tool to nurture these relationships even more.
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