AI, Large Language Model

How LLM Solutions Help Manage Risk and Compliance for Mortgage Companies

By 5 Minute Read

Risk and compliance management using AI

Why Large Language Models (LLMs) are a Good Fit for Risk Management and Compliance.

Large Language Models (LLMs) like OpenAI’s GPT series have revolutionized the way we process and analyze vast amounts of transactional and conversational data. With these advancements, LLM solutions for risk and compliance management can greatly benefit many industries, including the mortgage industry.

At their core, LLMs leverage deep learning techniques to understand and generate human-like text based on the input they receive. This capability is underpinned by their training on extensive datasets encompassing a wide array of human language and textual information.

To ensure the successful implementation of LLMs in enterprise environments, it’s crucial to consider security and compliance safeguards tailored for generative AI solutions. These safeguards not only protect sensitive data but also maintain the integrity and reliability of the AI-powered risk management and compliance processes.

This extensive training enables LLMs to discern patterns, trends, and insights from large datasets much more efficiently than traditional data analysis methods. Moreover, their ability to understand context and nuance in language allows them to parse through complex, unstructured data, which is often how conversational and transactional data is presented. This makes LLMs particularly adept at identifying relationships and insights within data that might not be immediately apparent through conventional analytical approaches.

Implications of using LLMs for Risk and Compliance Management in the Mortgage Servicing Industry

The power of LLMs in analyzing large datasets has significant implications for the mortgage industry. The high volumes of loan transactions and modifications, borrower conversations, regulatory documents, and agent conversations that are characteristic of the industry make a good argument for LLM-enabled solutions. Such solutions can transform search, summarization, data analysis, sentiment analysis, and insights generation in unprecedented ways and can identify patterns and trends, potential risk factors, and anomalies that may impact loan performance or compliance with regulatory requirements for the mortgage servicing industry.

Take search for example – by searching large volumes of conversation history between frontline agents and borrowers, LLMs can be trained to identify phrases that are potential indicators of opportunity (“do you offer home equity loans” or “i am selling my house so I need a payoff quote”), customer satisfaction (“you guys are the best” or “why is it so difficult to make a payment”) and even risk to a loan and/or to a borrower that may be in need of a loan modification (“I lost my job” or “I can’t make my payment again this month”).

By summarizing complex documents and highlighting critical information, LLMs enable mortgage service providers to expedite decision-making and better train their agents. Key information and actionable insights can also be extracted efficiently from lengthy regulatory documents and unstructured data sources using the power of LLMs.

With the capability to infer sentiment and tone from customer interactions, AI assistants can also gauge borrower satisfaction levels, identify potential issues, and proactively address concerns. They can be leveraged to assist customer service agents, by identifying potential opportunities for training or even compliance issues and how to correct them in real time.

5 Use Cases for LLMs in Risk and Compliance Management

1. Historical Data Analysis & Insights

Analyzing the large volumes of interaction and conversational data on borrowers can yield valuable insights into the risk profile (and opportunity profile) of a borrower and their loan. This gives the mortgage servicer the ability to proactively manage risk and adherence to standards.

Using the summarization capability of LLMs, an instant profile of the borrower that takes account of their interaction behavior and any conversations they may have had with customer service can be created. Individual transactions and/or trends that might be problematic or that create opportunities for retaining a customer can also be identified. Likewise with identifying compliance issues and training opportunities. Evidence of adherence to standards and operating procedures in sensitive situations can also come to light. In summary, LLMs can make it easy to search and find the needle in the proverbial haystack.

2. Real-time Risk Identification

LLMs can assist agents in real-time by identifying potential risks and compliance issues during live customer interactions. Using natural language processing capabilities, LLMs can analyze chat conversations between customer service agents and borrowers in real-time, suggesting next steps based on the recommended operating procedures, highlighting potential areas of non-compliance or elevated risk factors as they emerge and enabling agents to take immediate corrective action.

The number of regulations governing the processing of different types of loans can make it difficult and stressful for agents to have to remember every nuance of an FHA loan versus a VA loan, for example. Providing them with the tools to make better decisions in real time can lead to better outcomes, both for the borrower and for the lender.

3. Agent Training Enhancement

Through data analysis and summarization, LLMs can help improve agent training programs by identifying and highlighting areas for improvement across agent groups and call types, providing evidence-backed examples of positive transactions and highlighting individual opportunities for further training.

By analyzing historical data and generating comprehensive summaries, LLMs enable mortgage servicing companies to provide new agents with targeted training on regulatory requirements, risk mitigation strategies, and best practices for ensuring compliance.Summaries of potential risk factors and compliance issues gleaned from past conversational history can be generated and used in training agents.

With high staff turnover in the contact centers, the ability of LLMs to provide better training and support to their agents is key to more cost-efficiencies and agent satisfaction.

4. Compliance Checks

LLMs can conduct comprehensive compliance checks across various regulatory aspects, including mortgage regulations, payment regulations, and other relevant mandates.

By leveraging their search capabilities, LLMs can scan vast amounts of data to identify past instances of non-compliance and monitor ongoing interactions in real-time to detect potential compliance issues, helping mortgage servicing companies maintain adherence to regulatory standards.

This is very helpful in cases of regulatory oversight and auditing which would otherwise be an onerous manual and time consuming task.

5. Document Processing and Insights

In the mortgage servicing environment, managing and analyzing vast collections of documents, such as loan applications, legal paperwork, and customer correspondence, is both critical and challenging.

Large Language Models (LLMs) present a significant opportunity to revolutionize this aspect of the industry. Through their advanced natural language processing capabilities, LLMs can automate the extraction, categorization, and summarization of key information from these documents. This not only drastically reduces the time and effort required to process documents manually but also minimizes human error, ensuring more accurate and consistent outcomes. By understanding the context and content of documents, LLMs can identify relevant information, such as applicant financial information, terms of the loan, and compliance with regulatory requirements, streamlining the review and approval processes.

This enables mortgage servicing companies to quickly extract key information and gain valuable insights to support decision-making processes and ensure regulatory compliance across document-intensive workflows.

Conclusion 

In summary, by leveraging LLMs’ advanced natural language processing capabilities and data analysis tools, mortgage service providers can enhance risk management and compliance efforts across many facets of their operations from loan modification, document processing, foreclosure, and agent training processes.

By extracting insights from vast datasets, identifying trends and anomalies, and inferring sentiment from customer interactions, LLMs empower mortgage service providers to make informed decisions, mitigate risks, and enhance borrower satisfaction and regulatory compliance in more automated ways.

If you are interested in more insights into how powerful LLM solutions can be across different business areas of mortgage servicing, check out these blogposts or contact us for more information.

 

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