The mortgage industry has experienced significant shifts within various facets of lending process as a result of the implementation of artificial intelligence (AI). A notable 42% of lenders have incorporated AI and machine learning (ML) primarily to optimize operational efficiency, while 41% believe that these technologies can enhance the overall borrower experience.
According to a recent report by Accenture , the global AI in lending market is expected to reach $10.4 billion by 2027, growing at a remarkable CAGR of 23.5%. The use of AI in lending not only accelerates the loan approval process but also minimizes errors and mitigates bias. It improves risk evaluation by finding patterns that human analysts might miss, making borrowing easier for people and businesses with efficient, technology-driven processes.
In 2023, a significant shift occurred in the mortgage lending industry, with a notable 73% increase in lenders prioritizing operational efficiency through the adoption of AI-led automation. The surge was primarily driven by the realization that streamlining processes is essential for achieving success.
The primary goal is to transform routine, data-heavy tasks into error-free, swift, and autonomous operations. By achieving this, mortgage fulfilment teams can redirect their focus towards high-value decision-making tasks, fostering a more strategic and impactful approach to their work.
A significant portion, 42% , of lenders leverage AI and machine learning (ML) to enhance operational efficiency, while 41% see the potential for improving borrower experiences through this technology. Notably, over half of mortgage customers express a preference for financial institutions equipped with Machine Learning capabilities, favouring them over those relying solely on human executives.
Loan origination, the process of starting a loan, involves a lot of data, and artificial intelligence (AI) plays a crucial role in simplifying complex tasks. First, AI-driven analytics helps in assessing borrower risk by efficiently handling a multitude of variables that make up a borrower’s risk profile. Unlike human analysts who may only scratch the surface, AI can scale and dynamically update risk models with new data.
Second, in a fluctuating economy, AI comes in handy for matching potential borrowers with the most suitable loan officers. This ensures that lenders maximize their chances with every lead. Lastly, AI assists in making informed decisions about the loan origination ecosystem. By predicting loan volumes and demand patterns, companies can strategically plan their network needs.
The underwriting phase in the mortgage process is crucial but often time-consuming and prone to high denial rates, especially for self-employed borrowers. Typically, underwriters go through multiple touches and complex computations involving income, credit history, and appraisal data. This can lead to delays and a denial rate of up to 50%.
By using Document AI APIs, lenders can streamline the process by directly extracting income and credit history for self-employed borrowers. Furthermore, Generative AI chat applications enable underwriting teams to quickly navigate through investor guidelines by asking human-like questions, receiving recommended actions, and detailed explanations. This not only enhances the accuracy of income analysis but also significantly reduces the underwriting time from days to mere hours.
Mortgage closings often involve cumbersome paperwork and time-consuming processes. Gen AI facilitates the transition to e-closings by automating document verification, ensuring compliance, and expediting the finalization of mortgage agreements. This not only enhances the overall customer experience but also reduces the likelihood of errors.
Assessing mortgage applicants’ creditworthiness traditionally relies on credit bureau information, but human assessment may be influenced by bias, leading to erroneous decisions. Generative AI can revolutionize this process by enabling automated evaluation of mortgage applications based on credit data and risk behaviour insights, potentially transforming lending practices.
Generative AI can also be trained to detect fraudulent practices in loan applications, such as forged signatures or fake property documents. Manual inspection of documents by employees is time-consuming and error-prone, but AI systems can rapidly evaluate document authenticity at scale, matching them against recognized standards.
The initial interaction between applicants and lenders is often time-consuming and stressful. Tasks such as understanding applicant requirements, selecting suitable loan programs, and personalizing them based on credit assessment are complex. Generative AI can automate these processes, including loan application and customer onboarding, providing virtual assistance to customers from the comfort of their homes or anywhere. Lenders can develop a generative AI-powered virtual assistant to gather customer requirements and offer customized loan programs along with the necessary documents.
Generative AI is an important resource during the loan servicing phase. It monitors the financial status of borrowers, tracks payments, and delivers customized financial guidance. By analysing transaction data and market trends, AI can provide borrowers with valuable insights, such as opportunities for refinancing or options for consolidating debt, enabling them to make well-informed decisions. Additionally, AI streamlines routine loan servicing tasks, such as creating payment schedules and conducting checks for regulatory compliance. This not only reduces operational expenses but also ensures that borrowers receive precise and timely information about their loans.
While the integration of Generative AI brings about numerous use cases and substantial benefits, but it is essential to acknowledge and address potential risks and challenges associated with its deployment.
Generative AI adoption in the mortgage industry raises concerns about data security, potential inaccuracies, and lending bias. Taking lending bias as an example, the effectiveness of generative AI models depends on the quality of the data they are trained on. If biased or inaccurate data is used, the AI models can amplify these issues, resulting in discriminatory outcomes.
Ensuring data security is crucial, and measures such as encryption, access controls, and secure storage facilities are essential to protect sensitive information. Regular security audits of generative AI models help identify vulnerabilities and maintain robust data security practices.
As AI systems become more advanced, determining clear ownership of the complex codes that drive these technologies can be risky. Ambiguity in defining code ownership may lead to disputes, legal complexities, and ethical considerations.
To make the most of generative AI, you need to update and improve your systems. Without modernizing them, you won’t fully benefit from the potential of Gen AI.
Faster Product Delivery: By streamlining backend development, Compage facilitates quicker product delivery. This efficiency is essential for staying competitive in fast-paced markets.
Cloud-Native Adoption: Compage supports the adoption of cloud-native technologies. This means that the system becomes more flexible, scalable, and able to take advantage of cloud-based resources.
Policy Enforcement for Security: The platform enables the enforcement of policies that enhance the security of the software supply chain. This is crucial in safeguarding against vulnerabilities and ensuring a secure development and deployment process.
Secure Code Delivery: Compage emphasizes delivering secure code, supports the use of secure programming languages. This contributes to the overall robustness of the system.
Use compage – a language agnostic auto-code generation framework to leverage the use of Gen AI. Compage serves as the initial building block, creating a strong and secure foundation for your code. Once Compage accomplishes this, Gen AI can be introduced to expand the capabilities of your code, catering to diverse use cases and enhancing its functionality.
Compage automates backend development, paving the way for effortless integration of Gen AI to facilitate predictive analytics in risk assessment. This means that Compage sets up the foundational infrastructure, allowing Gen AI to be seamlessly incorporated into the system to analyze data and predict potential risks
Compage addresses ownership issues through its licensing feature and minimizes maintenance overheads with auto self-healing and self-maintenance capabilities. This is crucial as it ensures streamlined operations and reduced operational costs, mortgage businesses to focus on serving their clients effectively and efficiently.
Want to explore how Compage can elevate your mortgage lending business? Reach out to schedule a demo today. You can also visit our platform on GitHub and connect with us on LinkedIn for the newest updates and insights.
Shreya Trivedi is a Frontend Developer and Technical Content Writer.
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