Manually updating spreadsheets. Dealing with paper jams in the printer. Do you remember what office life was like in 2005? If you’re feeling nostalgic, you can still find many of the same practices in the reporting offices of lending investors today.
But why haven’t these offices evolved with changing technology?
It’s partly cultural, but it also reflects how the industry has developed over time. Many of our systems were never designed with data transparency or real-time automation in mind; some service platforms even predate the invention of Excel.
When I started in the mortgage industry in the 1990s, data management remained largely manual – we still used paper ledgers and simple spreadsheets. Excel began to gain popularity as companies switched from DOS-based to Windows-based systems. At the time it was revolutionary and gave us far more options and flexibility than ever before.
That revolution changed the way people worked. Over time, almost every department has learned how to improve their processes, set up automation, and quickly solve problems with Excel. These practices became a natural way of working and eventually became part of the culture of investor reporting.
Fast forward fifteen to twenty years and many of the same people who relied on these methods are now industry leaders who have passed that knowledge and mindset on to the next generation, keeping spreadsheets embedded in our operational DNA.
The comfort of visibility and the cost of over-trust
There is also an element of trust in the game. People feel more comfortable with what they can see and verify, even if it is inefficient. After all, a spreadsheet gives users complete insight into their data.
But that transparency can be deceptive. We’ve all seen how a small formula error that affects just a fraction of a percent can become a costly problem when applied to thousands of rows of data. Some spreadsheets have become so complex that even though you can technically trace every formula, they are beyond our ability to really understand them.
The challenge is to help organizations realize that automation does not mean losing control. It means transferring control, shifting from manual processes with limited data integrity and capabilities to systems that ensure accuracy, visibility and simplicity. True automation doesn’t obscure the data; it clarifies it, allowing teams to focus on what really matters.
While Excel still offers some degree of scalability, it is only a sketch of what is possible with modern, managed systems.
Why legacy workflows persist
What keeps these outdated workflows alive? In my experience, the biggest culprits are data fragmentation, limited system interoperability, and distributed systems of record.
Data management takes place across multiple platforms – core services, accounting, cash management, investor data and client-specific reporting templates – but these systems don’t communicate cleanly, or sometimes at all. Even when technology providers promise automation, their solutions can often sit on top of inconsistent data. As a result, teams still need to manually fine-tune results.
There is also an understandable resistance to risks. In investor reporting, even a small error can have regulatory or reputational consequences. Many organizations prefer security over efficiency, at least until they realize that modern automation can deliver both.
And then there are costs. Excel is affordable and most managers are unaware of the hidden costs of poor quality investor reporting. Many managers still view reporting to investors as a purely external obligation: a compliance achievement, rather than a strategic asset. But that mentality overlooks a big opportunity.
By harnessing the power of all the data collected through investor reporting, managers can uncover valuable insights to improve upstream and downstream operations, from loss mitigation and exclusions to cash management and loan underwriting. The ROI becomes apparent when organizations view investor reporting as an internal catalyst for improvement, not just an external requirement.
Using AI to replace manual processes
Servicers can leverage AI-enhanced expert systems that process and analyze hundreds of service platform reports, investor records, and other related data sources in parallel. These systems use extensive reconciliation, triangulation and data validation to detect even the smallest anomalies, effectively flagging issues that often go unnoticed or require significant manual adjustments when identified by traditional processes.
Unlike tools that only automate superficial tasks, these new expert systems, enhanced by AI, use advanced rules-based decision making to automatically resolve nearly 80% of exceptions and operations. Reporting analysts can therefore shift their focus to more complex or high-quality research, knowing that the data remains clean, reliable and aligned across systems. AI-driven insights suggest potential avenues of investigation based on the patterns they detect. These guidelines not only increase efficiency, but also lead to more consistent and accurate conclusions.
Quality results require quality data
High-quality automation starts with high-quality data. Too often, organizations invest in new systems without addressing the underlying data problems. And that’s where malfunctions arise.
Automated platforms should augment human oversight, not replace it. A well-designed system helps identify and resolve inconsistencies by triangulating information from multiple data sources. While that involves some initial effort, it creates a critical feedback loop: operations teams can identify and correct data issues or improve processes at the source, increasing efficiency and accuracy over time.
As the underlying data becomes more reliable, automation delivers even better results. Cleaner data means clearer feedback, and clearer feedback further improves data quality, creating a virtuous cycle that continues to build over time.
This ripple effect goes far beyond investor reporting. Servicers can use such feedback to drive operational excellence in the broader organization. As data integrity improves, the entire maintenance operation benefits.
How automated systems can gain trust
Ultimately, automation succeeds or fails on one factor: trust.
Once the data is consistent and validated, organizations can introduce automation with more confidence. Although a system can technically work with bad data, if the results are not reliable, confidence in the results will quickly erode.
Trust is earned through transparency, traceability and performance. Teams need a clear understanding of the data used, every processing step taken, and the reasoning behind each result. Over time, consistent efficiency gains, auditability, and alignment with user judgment strengthen trust, making automation not only accepted, but trusted.
Modernization is not about giving up the tools that got us here; it’s about evolving the way we use them. Spreadsheets gave this sector its first taste of digital empowerment. But the same trust and visibility that made Excel revolutionary have also made it difficult to abandon.
As we enter a new era of intelligent automation, the mindset shift is already underway. Automation is not about taking control from people. It’s about giving them better tools. Tools that ensure accuracy, improve overview and give them the freedom to focus on higher value work.
When we can see automation as an extension of our expertise rather than a threat to it, real progress begins. By eliminating spreadsheet-based exception management and reducing reliance on end-user computing tools, expert systems become foundational solutions, strengthening current workflows and positioning organizations for more advanced AI adoption in the future.
Jeff Choi is COO at PMSI.
This column does not necessarily reflect the opinion of HousingWire’s editorial staff and its owners. To contact the editor responsible for this piece: [email protected].
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