Trusted data is critical for decision-making and automation, especially today with the rapid evolution of AI. People, processes, and technologies all need data to work efficiently and make informed decisions.
However, the challenge for mortgage lenders is that data arrives in many different forms and changes throughout the origination process. With hundreds of pages of documents containing both structured and unstructured data — and the need to cross-validate all that data — it’s easy to see how data can go from being an asset to a roadblock when consistency, quality, and governance are lacking.
Data modernization goes beyond improving quality and accuracy — it unlocks new capabilities. With a modern data platform, organizations can use advanced analytics and automation to make smarter decisions.
That is why implementing a data modernization program is essential. Organizations require a unified source of accurate information, effective data governance and quality, and cutting-edge tools that enable advanced analytics, artificial intelligence, generative AI (GenAI), and large-scale business transformation.
With the rapid advancement of GenAI, and while business is slower for most lenders, now is the perfect time to start their journey. Data modernization is a big undertaking, especially if you have multiple systems and sources of data (and even bigger if the company has had acquisitions). This is why it’s so important to start early and be strategic with timing to get ahead.
The lenders that start their modernization journey now will be prepared to start taking advantage of new technologies and efficiencies when the market inevitably shifts.
Why Data Modernization? Why Now?
New technologies like GenAI are rapidly improving, which can reduce costs and increase accuracy, however they require good data to bring those benefits.
The more you can use data, the greater the impact you will have. Most data right now is spread between different systems, often with disparities between them. Every disparity creates a gap that can lead to errors or misinformed decisions, especially when using AI. If lenders cannot trust their data or can only use a limited amount of it, it lowers AI’s potential impact.
Data modernization closes those gaps. By taking the time to evaluate structured and unstructured data across different systems and documents, lenders can identify discrepancies and therefore reduce buyback or rescission exposure. Data modernization also corrects errors and unifies data points to create a single source of truth that lenders can trust.
Thinking of how many documents and data points lenders have, this process, understandably, takes time. However, it will pay off when volume increases, as it lays the foundation necessary for the technologies that will make lenders efficient and competitive.
Best Practices For Successful Data Modernization
The first step to data modernization is understanding the data you have. Not every piece of data or data field carries the same weight to be modernized. Start by identifying decision-critical data that directly influences outcomes, such as to assess risk, validate eligibility, or resolve exceptions — that’s where you’ll find the biggest payoff. Establish data quality rules, data definitions, and data governance to ensure that the data is unified and going forward, all new data will adhere to these standards.
Next, determine what processes or decisions can be automated, including subsets of the mortgage origination or servicing process. It’s not realistic to try and fix everything all at once — and not every process will be a candidate for automation. Some processes benefit from automation due to their repeatability and consistency, while others still require human judgment. Determining where technology should assist — and where people should remain central — is a critical part of any modernization effort.
Implement data modernization in phases or modules instead of one big project. There is too much data to go all in at once and some data is simply not worth the cost to modernize. Nothing gets staff overwhelmed or burnt out more quickly than a seemingly insurmountable project. Successful data modernization efforts are phased, aligning improvements to specific business outcomes. By focusing modernization efforts on decision-critical data first, organizations can deliver meaningful impact without attempting to standardize every data field across the enterprise. Another approach can be to begin by modernizing data that supports high-volume or high-risk processes before expanding to additional use cases over time.
Working in phases also helps build momentum and create small wins. Once staff see that the data modernization project was achievable and successful, it can create more buy-in for the next one. Give people time to build trust and get familiar with one change before making another.
A successful data modernization effort requires a strong internal executive champion as well as functional champions. They can set a vision, prioritize investments, and align stakeholders across the organization. Without clear ownership and advocacy at the leadership level, data modernization risks becoming a technical initiative rather than a strategic transformation that delivers lasting business value.
Where Should Lenders Start Their Journey?
The best place to start modernizing is to evaluate where the organization will be making data-driven decisions and invest there. There is really no right or wrong place to start — it comes down to what makes the most sense for the organization. For example, there are lots of tools out there to help underwriters with manual, time-consuming “stare and compare” work. OCR, for example, has transformed how structured data is presented and validated and new AI tools can handle unstructured data, like photos or appraisers’ notes. GenAI also has many different applications that can save lenders time across many different areas.
Focusing on a few simple questions will help keep the data modernization effort concentrated: What do we need? What do borrowers need? Where are the pain points or bottlenecks? What is the best tool for the job?
Putting Data Modernization To Work
Data modernization is not the final destination — it is the first step in a broader journey. To truly harness the power of all this data, lenders need to maximize their use of automation and technology.
And when implementing any new tech, don’t underestimate change management. People need to know that they can trust a new technology and the data it’s using. If they are always going back to check the automation or maintain the same existing process, it doesn’t actually save time.
Make sure everyone knows who’s accountable if the technology is wrong. People may think they are on the line if the tech makes a bad decision, so most people won’t risk it and won’t end up using it. Again, this means the technology — and all the data modernization work — won’t be bringing any benefit to the organization.
Education is key to helping build trust with any tool. Demo it so people can see how it works. Building familiarity will help build trust. It’s also helpful to give staff options for tools they can use once data modernization is in place. Help them understand where to emphasize humans over tech and vice versa. Don’t expect people to figure things out on their own — help them envision what their job looks like after new technology and systems are in place.
Data modernization is no small task. It requires a lot of time and effort from organizations that want to do it right, and now is the perfect time for lenders to get started. Having a strong foundation laid out in advance can pay off massively in the future, and provide the necessary foundation to take advantage of automation and new technologies.
