What AI is Teaching Us About Mortgage Data and Documents

The integration of Artificial Intelligence (AI) into document management systems is revealing profound insights about the nature of data and documents, particularly in the mortgage industry.

As we navigate this technological revolution, we're learning valuable lessons about data consistency, system adaptability, and the evolving landscape of financial documentation. As every lender knows, these are critical considerations in an increasingly complex compliance environment.

Over the past two decades, the mortgage industry has witnessed a gradual yet significant shift in document processing technologies. From simple barcode systems and separator pages to sophisticated content classification using machine learning, the evolution has been steadily maturing.

However, this progression has also exposed critical gaps in our understanding of data management and its utilization.

Where to Store the Additional Data We’re Finding

One of the most striking revelations is the disconnect between data extraction capabilities and data storage infrastructure.

While AI can extract hundreds of data points from a document, many line of business applications are equipped to store and act on only a fraction of this information. This mismatch highlights the need for a more holistic approach to system design, where data extraction, storage, and utilization are working in tandem.

It’s not that the data points are new, necessarily, but rather that borrowers now come to the table with a lot more information about their financial lives.

For instance, our new AI tools have been uncovering unexpected trends in financial documentation, most recently in the prevalence of 1099-NEC forms (Non-Employee Compensation). With more Americans taking on additional work outside of their primary job to make ends meet, this rise in documentation quickly surpassed initial expectations.

This growing ‘gig’ economy trend underscores the importance of flexible, adaptable systems that can quickly incorporate new document types and data fields as economic patterns shift.

Some Data is New, Some Just Inconsistent

Perhaps most intriguingly, AI is shedding light on the inconsistencies in how information is presented across different documents, even when originating from the same source.

A prime example is the variation in employer names between W-2 forms and pay stubs from the same company. These discrepancies, which might seem minor to human processors, can significantly impact automated systems relying on exact matches.

This challenge extends beyond corporate entities to individual names as well. The various forms a person's name can take – from formal (Robert) to familiar (Bobby) – presents a unique hurdle for AI systems. It's a reminder that human communication is inherently flexible and context-dependent, a nuance that AI must learn to navigate.

These inconsistencies point to a crucial area where AI, particularly machine learning and fuzzy logic, can help software developers make significant strides. By building relationships between words and learning to recognize equivalent terms (like Facebook and Meta), AI systems can become increasingly adept at bridging these gaps in data consistency.

Moreover, AI's struggle with these inconsistencies is teaching us about the importance of standardization in document creation. It's prompting discussions about how organizations can improve their internal processes to ensure consistency across all documentation, which would not only benefit AI processing but also enhance overall data management.

Working to Hit a Moving Target

Of course, in an industry like ours, where new document requirements are frequent, we must ensure continuous learning and adaptation in AI systems.

As new forms of income documentation emerge and societal norms around employment evolve, our AI systems must be capable of quick adjustment. This need for flexibility is pushing the boundaries of traditional machine learning, encouraging the development of more dynamic, adaptable AI models.

The application of AI in document management is not just about improving efficiency; it's providing valuable insights into the complex nature of data and documents in the modern world. It's highlighting the need for more comprehensive data management strategies, exposing inconsistencies in documentation practices, and pushing for greater standardization.

It’s exciting work and we’re proud to say that Mortgage Cadence is working on the cutting-edge. If you’d like to find out more about how the tools we’re creating are solving problems and dealing with the new revelations our tools are providing, reach out to us today.

By Mark Swift, Software Product Manager at Mortgage Cadence 

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