How pursuing GenAI can transform mortgage lending

Opportunities to boost efficiency across key operational components span the entire mortgage lifecycle. These uses are extensive and scalable, evolving with technological advancements and incorporating more sophisticated applications over time.

For lenders starting their GenAI journeys, the following three use cases offer an ideal entry point.

• Personalized loan offerings: Traditional lending institutions primarily offer standardized loan products with minimal customization, which may not cater to the specific needs of all customers. The lack of a personalized approach that aligns the loan offerings with the customer’s financial condition also increases the chance of default. Standardized loan products come with high operational costs due to the manual process of scrutinizing individual loan applications and underwriting them.

GenAI can customize loans, leveraging customer data to design products tailored to individual needs, which in turn enhances customer satisfaction and retention. It can increase loan origination rates by analyzing existing customer metadata. Based on a customer’s personal and financial situation, GenAI can offer data-based insights that would allow the lender to adjust the loan terms, potentially decreasing the chances of default. Through greater automation of the entire loan sanctioning process, operational costs can be reduced and efficiency improved.

Difference in customer loan offerings

Source: EY Consumer Lending Team

• Knowledge center: When receiving a customer query, agents in traditional organizations often have to access multiple databases to find the answer. Agents are further challenged by the possibility that the information in the database may be outdated. Difficult interfaces also can create bottlenecks by requiring the use of different tools to find information. Once the answer is found, organizations run the risk of agents interpreting or conveying the same information differently.

GenAI can help connect and combine knowledge across different databases, creating a knowledge center for agents to access. It can also parse and summarize new laws, rules and regulations applicable to the organization. Having dashboards and interfaces that are easier to navigate simplifies the information-gathering process and saves time. With all agents working from the same information, they can provide a consistent customer experience.

Answering customer queries for information

Source: EY Consumer Lending Team

• Customer complaints: When a customer calls with a complaint, the traditional organization has agents who manually draft complaint summaries, a time-consuming task that can lead to inaccuracies and errors. Without clear parameters, the manual categorization of complaints can be difficult. While logging the complaint, the agent receiving the call is also expected to treat the caller with empathy, providing a human touch to the customer experience.

An application that leverages a Large Language Model (LLM) can help transcribe and summarize the complaint into call notes. By using predefined categories, GenAI can classify complaints, increasing organization and making it easier to identify trends. Freed up from manual tasks, agents can focus on extending empathy and apologies, which improves customer satisfaction.

Handling customer complaints

Source: EY Consumer Lending Team

Conclusion

With advancing technology and evolving consumer expectations, a transformative opportunity awaits forward-thinking mortgage lenders. By exploring and investing in GenAI technologies, lenders stand to gain a first-mover advantage and play a pivotal role in shaping the future of the consumer lending space.