As lenders look to modernize their quality control operations, many are exploring the potential of artificial intelligence to streamline processes, improve loan quality and reduce risk. In this executive conversation, HousingWire spoke with Trevor Gauthier, CEO of ACES Quality Management, about how QC has evolved over time, how AI is reshaping expectations and what lenders and servicers should be doing now to prepare for what’s ahead.
HousingWire: Mortgage quality control has transformed significantly over the last two decades. From your perspective, what have been the biggest milestones in this journey?
TG: The first milestone was moving away from spreadsheets and homegrown systems. In the early 2000s, many lenders were still relying on highly manual processes to manage audits. These approaches weren’t scalable and left too much room for human error and inconsistency. The introduction of configurable platforms gave lenders a better way to enforce sampling rules, ensure consistency in audits and improve reporting.
The next turning point came with the rise of web-based QC systems. These solutions gave lenders the flexibility to work securely across locations and teams, which became especially important as remote work increased. Over time, more automation was added, like dynamic checklists and rules-based logic, which helped streamline the auditing process and reduce repetitive tasks.
We also saw a shift in how organizations approached QC from a staffing and ownership perspective. As regulatory expectations grew, so did the need for more standardized practices and clearer documentation. That drove adoption of built-in questionnaires, certification programs and shared best practices that elevated QC from a compliance obligation to a strategic function.
Now we’re entering the next phase. With the introduction of artificial intelligence, QC technology is moving beyond automation into intelligence, giving audit teams the ability to work faster, uncover insights more efficiently and support the business with better data.
HW: There’s been growing interest in how artificial intelligence (AI) can transform mortgage operations. How do you see AI shaping the future of quality control?
TG: AI is fundamentally changing the way QC teams interact with their systems and their data. While past innovations focused on automation and standardization, AI introduces an entirely new layer of responsiveness and intelligence. It allows teams to move faster, gain deeper insights and remove unnecessary friction from daily workflows.
That’s why we developed ACES Intelligence, which officially launched this month. We designed it to bring the power of generative AI directly into the QC workflow, starting with the tasks that auditors spend the most time on, things like writing exception comments, building loan criteria and generating executive summaries. With ACES Intelligence, users can complete those tasks using natural language, which removes the technical barrier and improves efficiency.
We’ve also introduced features that help teams surface trends and monitor risk across audits. For example, auditors can create summaries of specific exception types, analyze comment history across loans and detect sensitive borrower information before reports are finalized. All of this is recorded for compliance and available instantly.
The launch of ACES Intelligence marks a turning point. As the first AI tool purpose-built for QC, it sets a new standard for how audit teams engage with data and drive operational value.
HW: In light of the fears around AI replacing jobs, how do you view the role of human auditors in this next phase?
TG: AI is a tool, not a replacement. The technology can draft content, find patterns or reduce steps, but it doesn’t understand the nuance of loan quality the way an experienced auditor does. Human judgment is still critical in evaluating findings, determining root causes and engaging business units in remediation.
Where AI can make a difference is by removing the repetitive, time-consuming tasks that slow auditors down. Formatting exception narratives, sorting data or building criteria from scratch are all areas where automation makes life easier. That gives auditors more time to think critically and communicate their findings more effectively.
We’ve also seen that AI helps level the playing field for less experienced auditors. When you have a tool that can guide comment structure or flag inconsistencies, it’s easier for new team members to align with internal standards. That consistency improves the audit trail and strengthens overall performance.
These capabilities aren’t in competition. When paired together, they create a more agile and effective QC function that’s ready for what’s next.
HW: What are some of the most immediate benefits lenders and servicers can expect when incorporating AI into their QC workflows?
TG: The first and most immediate benefit is time savings. We’ve seen ACES Intelligence users reduce exception writing and reporting time considerably. Instead of copying and pasting, users can generate well-structured summaries and narrative content instantly, then review and refine as needed.
There’s also a noticeable improvement in audit consistency. When AI supports comment writing and summary generation, the language becomes more standardized. That means fewer discrepancies in how findings are documented, which reduces rework and improves how audits hold up under external review.
On the reporting side, users can now create portfolio-level summaries that highlight defect trends, root causes and material findings across different loan types or business units. That kind of insight used to take hours to compile. Now it’s available in near real time.
PII detection is another area where automation adds value. It’s no longer a manual review task. The system flags potential issues in the exception dialog, and auditors can choose to redact, reject or explain the data. All those actions are captured and stored for compliance purposes.
AI speeds up the process, but it’s true value lies in how it improves quality, promotes consistency and helps QC teams deliver better results across the organization.
HW: Looking ahead, what should QC leaders and their teams be thinking about as they navigate this period of technological change and prepare for the future?
TG: QC leaders should start by evaluating where their teams are spending time today. If you’re dedicating hours to exception comment writing or manually building criteria, that’s a sign AI could make an immediate impact.
The next step is thinking about how to introduce this technology in a way that supports your process rather than overhauling it. When we built ACES Intelligence, one of our goals was to make it feel familiar — something auditors could adopt without needing to change how they work. That’s key to gaining trust and driving adoption.
It’s also important to ask the right questions of your technology partners. How transparent is the AI? How is data handled? Can you audit the system’s decisions? These considerations will matter more as adoption increases and expectations rise.
Those who start now will be better positioned to navigate regulatory change, scale their efforts and lead the way in defining what modern QC looks like. This is just the beginning. Our journey into advanced technology will be iterative, and we’re fortunate to have a customer base actively collaborating with us to ensure everything we bring to market delivers immediate, practical value.
