Inside Consolidated Analytics’ hybrid AI model for the future of mortgage operations

Artificial intelligence is rapidly transforming mortgage operations, driving new levels of efficiency, compliance, and innovation, but the key lies in pairing technology with human expertise. In this conversation, Arvin Wijay, CEO of Consolidated Analytics, and Neil Sahota, the Chief Artificial Intelligence Officer, discuss how their organizations are integrating AI across the loan life cycle to create sustainable automation and smarter decision-making. Wijay shares how developing in-house AI tools has helped Consolidated Analytics streamline operations and maintain stability through market cycles, while Sahota explains why human oversight remains essential to ensure fairness, accuracy, and adaptability in a highly regulated industry. Together, they outline how lenders can approach AI adoption as a collaborative process, one that enhances human intelligence rather than replacing it.

HousingWire: How are CA and loanDNA integrating AI into mortgage operations today?

Arvin Wijay: We’ve been fortunate to stay right-sized and ensure the company remains profitable. As AI and automation gained momentum, we recognized the need to invest in technology and process improvement. The mortgage industry has a long history of hiring and firing through every cycle without adopting sustainable automation, and we wanted to break that pattern. We began cautiously with RPA and a few vendor partnerships, but quickly realized many solutions didn’t understand the mortgage business well enough to deliver real value.

That’s when we decided to invest in our own platform. Leveraging loanDNA, we developed AI-driven tools like document intelligence, income calculators, and a pricing engine, creating an end-to-end approach that supports the life of the loan. These tools have improved efficiency so we can handle the same volume with the same team, eliminating the need for reactive hiring cycles. This investment has not only strengthened our own operations but also positioned us to serve other clients through loanDNA’s growing product suite.

HW: Why is human oversight so critical when using AI in such a highly regulated industry?

Neil Sahota: Well, there are two key reasons this balance is critical. Mortgage lending is a highly regulated industry, and while AI brings speed and efficiency, people ensure that decisions remain fair, ethical, and compliant. AI will only do what it’s taught, and because data often carries bias, we can’t remove the human from the loop without risking those biases being amplified.

The second reason is that AI performs best on routine, structured tasks but struggles with exceptions or novel situations. It’s like a high-energy intern — great at what it’s trained to do, but when something unusual happens, it still needs human guidance. That human oversight ensures complex or first-of-a-kind cases are handled correctly and that AI learns responsibly over time.

HW: How do you ensure AI models stay accurate and compliant as market conditions and regulations change — and how does the AI + human partnership help lenders navigate future challenges?

NS: Regulations and market dynamics are constantly evolving, so AI can’t be a “set it and forget it” tool. It’s not static software; it learns and adapts continuously. But to ensure that learning aligns with reality, human expertise must guide it. Just like a high-energy intern needs a mentor, AI needs human oversight to interpret shifting economic, social, or political conditions and keep decisions fair, accurate, and compliant.

This partnership is what creates resilience. AI can process massive amounts of data and detect subtle patterns that signal change far faster than people can. But it’s human judgment that determines how to apply those insights in context and decide what actions to take as regulations tighten or markets shift. Together, they create a responsive, compliant system that helps lenders stay agile and informed in any environment.

HW: What advice would you give lenders exploring AI for their operations?

AW: It’s interesting, I believe AI should be seen as a collaborator, not a replacement. As Neil often reminds me, people should not fear AJ. It helps; it does not take jobs; failure to adapt does. Artificial intelligence plus human intelligence creates hybrid intelligence. AI should be trained like an intern with guidance. It streamlines repetitive or information-heavy tasks, allowing humans to focus on higher-value work while maintaining responsibility and accuracy.

NS: I agree. From my experience, organizations often follow what I call the “AI Activation Code.” They first explore AI capabilities, then prioritize use cases. Start small with low-risk tasks that can deliver results in 30 to 60 days. Treat AI as a trainee that gradually takes on more complex work. Over time, it evolves from an intern role to a skilled collaborator, supporting compliance, scalability, and decision-making.

HW: How do you see the industry adopting and adjusting from both an individual perspective and from an overall industry perspective?

AW: Industry costs to originate loans are rising, cited at $13,000, even though efficiency claims suggest otherwise. Historically, originators measure cost in basis points, but loan sizes have doubled while costs remain high. The issue is not a failure to automate but a lack of holistic automation across the life of a loan, from origination to servicing, foreclosure, and REO.

AI can transform lending, but it requires rethinking processes into specialized components such as income, credit, and collateral analysis. This approach increases throughput, reduces time to close, and uncovers opportunities that traditional underwriting might miss, like optimizing debt structures while remaining compliant. Iterative transformation, with minimum viable products in 60 to 90 days, is key to adoption.

NS: Building on that, the industry is moving from a segmented view of lending to a holistic life cycle perspective. AI reduces manual, standardized work while improving accuracy and compliance. Similar to drug discovery, where AI expanded beyond auditing into modeling and simulations, mortgage institutions are now exploring AI across the entire loan process from application to underwriting, diligence, and servicing to drive efficiency and innovation.

Adoption is also shifting. Just a year ago, many lenders hesitated due to liability concerns. Now, the perception is that AI strengthens human judgment rather than replacing it. Institutions recognize AI as a partner that integrates across operations and secondary markets, redefining how the business functions end-to-end while enhancing decision-making and reducing risk.

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