Executive Summary
What is the AI spectrum? Artificial Intelligence (AI) is an umbrella term encompassing many different technologies and approaches to creating systems that work and react like humans. Mortgage servicers have started on the AI journey through automation and analytics, but the quality and ease of access to generative AI (e.g., large language models—LLM) models presents a great opportunity for organizations to accelerate their application of AI across mortgage servicing. Application of AI can be grouped into Analytics (e.g., ML-based propensity models), and Agentic AI (e.g., conversational agents or chatbots, workflow automation using intelligent agents).
What are the drivers pushing Servicers to invest in AI? Mortgage servicers are increasingly exploring artificial intelligence (AI) to reduce operational costs and enhance borrower experience across the servicing lifecycle, enabling faster document processing, personalized customer interactions, and data-driven decision-making. However, alongside these opportunities come critical responsible and compliance considerations. Bias, transparency, consumer privacy, and regulatory compliance risks must be proactively managed to ensure AI is used responsibly. This document outlines guiding principles and best practices for integrating AI responsibly across the servicing lifecycle—from loan boarding to default and refinancing.
Core Principles for Responsible AI in Mortgage Servicing
What are the key considerations for responsibly using AI? To responsibly deploy AI in mortgage servicing, organizations should ground their efforts in a few fundamental principles:
- Fairness & Non-Discrimination: AI-influenced decisions (whether in loss mitigation, collections, or refinance offers) must comply with ECOA and FHA, ensuring equitable treatment across all demographics. AI models should be carefully trained and tested to prevent perpetuating historical biases in data. Servicers should build a “fair servicing framework” into all processes, using data-driven insights to identify disparities and take meaningful action to mitigate bias. Regular fair lending testing is crucial to conduct disparate impact analyses on loss mitigation outcomes to ensure workout options are offered consistently across demographic groups. A subtle but critical risk in training servicing AI systems is ‘vibe coding’—the use of subjective or intuitive judgments by human annotators when labeling borrower interactions. If left unchecked, vibe coding can introduce hidden bias into models used for sentiment analysis, or collections prioritization. Servicers should implement structured labeling standards, bias-aware annotator training, and continuous calibration reviews to ensure data used for AI training reflects objective borrower behavior rather than human perception.
- Accountability & Governance: Institutions, not algorithms, remain responsible for AI outcomes. Generative AI models, especially large language models (LLMs), can occasionally produce hallucinations—outputs that sound plausible but are factually incorrect or inconsistent with source data. In mortgage servicing, this risk can manifest in AI chatbots giving borrowers inaccurate information or internal assistants misquoting servicing guidelines. Strong governance frameworks with grounding responses in verified data (e.g., using retrieval-augmented generation (RAG), human oversight, vendor due diligence, and model risk management must be in place to manage accuracy risk. Governance should enforce “responsible AI” practices for model evaluation and guardrails—where AI outcomes are continuously monitored, audited, and improved over time—as a core part of corporate risk management and culture. Accountability also means having human oversight (“human-in-the-loop”) for high-stakes decisions. Employees should be empowered to review, override, or explain AI-driven decisions, and borrowers should have channels to appeal or seek human assistance.
- Transparency & Explainability: Servicers must explain AI-driven decisions in plain language and disclose AI use to borrowers, especially when decisions impact credit or relief outcomes. This builds trust and supports regulatory compliance.
- Consumer Privacy & Data Security: Servicers must ensure AI systems (and data pipelines feeding them) comply with privacy laws like GLBA for financial data and CCPA/CPRA for consumer personal information. Data used in AI should be minimized to what is necessary, and robust cybersecurity controls should guard against breaches or unauthorized use. If AI models use customer data for secondary purposes (like predictive analytics for marketing), privacy notices must allow for that, or customers should have consented.
- Compliance & Responsible Innovation: AI should enhance—not bypass—regulatory compliance. Testing, piloting, and continuous monitoring are key to ensuring AI aligns with UDAP/UDAAP, RESPA, and state laws.
- Employee Communication & Transparency: Responsible AI governance extends beyond compliance—it also requires transparent communication with employees whose roles intersect with AI systems. Servicers should clearly explain how AI tools are being used in their workflows, what decisions they influence, and where human judgment remains essential. Transparent dialogue helps mitigate fear of job displacement, clarifies accountability, and reinforces the principle that AI assists rather than replaces human expertise.
Editor’s Note: Stay tuned to MP Daily for parts 2 and 3 of this white paper. For more information about the Mortgage Servicing Executive Alliance (MSEA), click here.
