AI, Large Language Model
How Generative AI Elevates the Borrower Experience in Mortgage Servicing
Borrower Experience and the Mortgage Industry
Mortgage loans constitute a significant portion of consumer debt in the US, comprising over 70% of total consumer debt, with approximately 84 million mortgages amounting to over $12 trillion in debt. Due to their financial impact and associated risks, mortgage processes are intricate, involving extensive documentation and manual processing.
Central to the mortgage industry is the mortgage servicing process, responsible for managing loans throughout their duration. This includes payment collection, addressing borrower inquiries, handling financial hardships, loan modifications, escrow management, loan transfers, and many other types of interactions.
With numerous points of engagement between borrowers and servicers, the relationship often spans the 30-year average loan term, although many borrowers change their loan much sooner than this period, either through a home move or refinancing. Either way, the relationship between mortgage servicing providers and borrowers extends over a sizable period of time.
Ensuring exceptional borrower experiences amidst evolving economic and regulatory landscapes is imperative for mortgage servicers. By leveraging AI, mortgage servicing providers can enhance borrower self-service capabilities, streamline processes, and reduce operational costs, enabling servicers to meet borrower needs across diverse interactions and complex processes efficiently.
Moreover, LLM solutions can help mortgage servicing providers ensure compliance with regulatory requirements and effectively manage risk.
How Generative AI elevates the Borrower Experience in Mortgage Servicing
Generative AI and Large Language Models (LLMs) are now shaping the future for mortgage servicing providers. The emergence of advanced LLMs is revolutionizing the field of conversational AI, offering unprecedented opportunities for enhancing the performance, versatility, and user experience of AI assistants or chatbots.
These AI models are also augmenting the capabilities of traditional Natural Language Processing (NLP) technology beyond pure conversational use cases, opening the path to new and transformative business applications for the technology.
Here are 3 transformative ways in which Generative AI can be leveraged by mortgage companies to further improve borrower experience.
1. Generating Trusted Responses using RAG Techniques
One of the fear factors for businesses in using Generative AI is that it generates text-based responses based on the vast amount of data that it has been trained on rather than staying within certain boundaries of moderated content. While it can often generate good quality and detailed responses to queries and is suitable for many use cases, it is not always completely accurate and reliable. Responses can potentially include outdated, inaccurate, or even offensive information. Naturally, for mortgage servicing companies who need to adhere to strict regulatory guidelines and consistently provide accurate information to their clients, this is a huge concern.
However, a specific technique, referred to as Retrieval Augmented Generation, or RAG, can tackle this problem. Retrieval Augmented Generation (RAG) is a technique in the field of natural language processing and artificial intelligence that enhances the capabilities of GenAI technology by integrating the large language model with external information retrieval systems. That allows the LLM to generate better responses because it has more context provided by the information that is sent to it.
By enhancing the accuracy and trustworthiness of Generative AI through indexing targeted facts and information from external sources that can be chosen for their reliability, RAG techniques mitigate the risks associated with pure generative AI responses, such as those we may be familiar with using ChatGPT.
For mortgage companies, this means that RAG techniques can be used for indexing content from an industry-trusted source. In the USA, for example, this could be indexing Fannie Mae’s Servicing Guide so that consistent and accurate responses are given to borrowers on queries related to mortgages. The technique can also be employed to index other types of content that will ensure greater accuracy in responding to the borrower’s request, for example a webpage or website that is known to have the latest and most accurate information.
By indexing pre-determined and trusted sources in responding to a customer query, a mortgage provider can be sure that their Generative AI chatbot remains within the intended boundaries and doesn’t answer incorrectly if it lacks the knowledge.
2. Improved Intent Classification
Correctly identifying and understanding user intent is crucial for an AI assistant to provide accurate responses. Intent Classification is used to identify and associate user phrases with specific user intent. When traditional chatbots fail to classify user intent, it is referred to as missed intent, where the NLP can’t match a phrase to a defined intent, and will generally give a predefined fallback response but can’t understand the context of the conversation to assume the closest matching intent.
Generative AI, however, outperforms traditional NLP models in understanding user intent and managing conversations in more dynamic and contextually-aware ways. Thanks to its extensive training data and sophisticated algorithms, Generative AI assistants can accurately infer user intent from a variety of expressions, including those that are vague or ambiguous. This capability ensures that the chatbot can address the user’s needs more effectively, leading to higher satisfaction rates and a more intuitive interaction experience.
3. Better Context Understanding
Traditional NLP-based chatbots are designed and trained on very specific, predefined intents and associated training phrases to meet very specific goals for their use case, making them less fluid in adapting to conversational nuances and changes in context. They may falter when faced with multi-part questions or follow-up queries that require understanding the context of an ongoing conversation.
Generative AI, however, excels at understanding and retaining contextual information across interactions with borrowers, providing comprehensive responses and seamlessly continuing the conversation. By leveraging their vast training data and sophisticated algorithms, LLMs enable more coherent and relevant conversations, even as borrowers switch between engagement points such as loan transfers, loan modifications, or payment inquiries.
LLMs can understand the flow of dialogue, recognize patterns in conversation, and infer the intent behind user queries more accurately. As a result, LLMs can provide more relevant and accurate responses, leading to more natural, personalized, and engaging interactions with users.
4. Emotional Intelligence and Empathy
Generative AI can also help elevate the borrower experience in mortgage servicing interactions through improved empathy and emotional intelligence. By analyzing the sentiment and emotional undertones of user inputs, it can tailor its responses to be more empathetic, supportive, or enthusiastic, as appropriate.
Take the example of a borrower going through financial hardships due to losing a job, suffering a medical condition, or for some other reason. Handling financial hardship, delinquent payments, and forbearance are common interactions for mortgage servicers. Over the course of a loan there are other crises such as death of a spouse or a divorce that require more empathetic engagement. There are also instances where a borrower may be disgruntled and have a complaint. When using an AI assistant, the ability to understand and adjust according to the tone and sentiment of the conversation, can be key to making clients feel more understood and valued. Better emotional intelligence and understanding of the borrower’s issue can also lead to better and more empathetic resolution paths.
Conclusion
The integration of Generative AI and Large Language Models (LLMs) offers a promising avenue for mortgage companies to enhance the borrower experience while maintaining security and compliance standards. Leveraging techniques such as Retrieval Augmented Generation (RAG), mortgage companies can generate trusted responses by indexing content from reliable sources, ensuring accuracy and reliability in borrower interactions.
The adoption of Generative AI enables better intent classification and context retention, allowing AI assistants to accurately infer user intent and provide contextually-aware responses. This results in higher accuracy and efficiency in handling a wide range of borrower interactions, from payment-related queries to loan modifications and forbearance requests.
Moreover, Generative AI enhances emotional intelligence and empathy in borrower interactions, allowing AI assistants to tailor responses to the emotional undertones of user inputs. This capability is particularly crucial in sensitive situations such as financial hardships or crises, where empathetic engagement can significantly impact the borrower’s experience.
By harnessing the capabilities of Generative AI and LLMs, mortgage companies can create more personalized, empathetic, and secure borrower experiences, leading to higher satisfaction rates and stronger customer relationships over the duration of the loan.
For more information about how a mortgage servicing provider used Conversational AI to enhance borrower experience watch this video from Cenlar FSB