Poor data quality costs organizations an average of over $15 million in losses each year. In mortgage lending, using bad data can result in inappropriate underwriting leading to the loss of good customers, ineffective risk pricing, or the taking on of less desirable accounts. In any of these circumstances, revenue can easily suffer.
Even when your processes are automated, your success as a profitable mortgage lender will only be as good as the data you feed into those systems. (You are what you eat, and robots are the data that they are fed.) If you take the steps to ensure that your data is clean and accurate, you’ll be able to improve your mortgage outcomes and your bottom line.
Why Data Quality Matters
For most businesses, data is a critical resource, and the mortgage industry is no exception. Data is important for targeting your message to the right people, staying in touch with your customers, and making effective underwriting decisions.
When it comes to underwriting, faulty data will lead to unsubstantiated decisions. For example, a fraudulent tax return or altered pay stub could lead underwriters to approve a mortgage that might actually be too risky. In another example, if you plan to bundle your mortgage loans to sell to a larger institution, data that is incomplete, inaccurate, or not in a standard format can risk or devalue the sale.
Poor quality data also requires underwriters to spend more time chasing the correct information, and potentially on unqualified prospects. With the current labor shortage, as well as the fluctuations of the mortgage department in general, the efficiency of your staff is crucial.
High-quality data is necessary to save time and avoid unnecessary costs. That means data that is:
- Complete. The records include all necessary information.
- Updated. The data is not old or outdated.
- Unique. The records are not duplicated and occur only once in a set of data.
- Formatted consistently. The data appears in a standard format in each record.
- Accurate. The data is correct.
- Timely. The data is available when it is needed.
How to Ensure Data Quality
Many organizations’ use data that is not high quality, and many of the errors occur when the data is first created. The most reliable way to ensure that data is high quality is to develop processes that foster the creation of accurate, complete data at the point of ingestion or creation. Additionally, organizations that have a process for tracking the origin of data will find it easier to correct mistakes.
Think about all the data to which your organization has access. Most likely, you only use a small portion of it on a regular basis. Focus on ensuring the quality of the data that you use regularly rather than overhauling all your data.
Let’s say a mortgage company is experiencing challenges with verifying applicant documents. In this example, the company should focus its efforts on cleaning up data extracted from the documents. Here are additional actions the company can take:
- Educate staffers about how inaccurate or incomplete data will affect customers and the bottom line.
- Encourage staffers to take ownership of the data quality.
- Implement a system to ensure a consistent verification process for every applicant.
- Use relational databases that can associate extracted data with the original unstructured source; for example, a link in the database to an image file of the document.
How Automation Can Help
High-quality data is important for automation to work well. At the same time, automation can help ensure data quality. The right automation software will extract accurate data from borrower documents, while also verifying those documents for accuracy. With modern intelligent document processing (IDP), your organization can avoid human error from manual data entry and confidently process documents more efficiently, and accurately than ever before.
Software with AI capabilities can identify suspicious patterns or altered documents and transactions, making it an ideal solution for cleaning up data. In addition to analyzing a significant amount of data rapidly and accurately, AI can also help underwriters make informed decisions about credit scoring and risk assessment.
Automation and AI can also ensure that unique or new forms of data are accurate and standardized. For example, many lenders are becoming more interested in making loans to college students or others who may have a limited credit history. In that case, AI can measure other data that can predict creditworthiness, such as mobile payment history.
High-quality data is a prerequisite for reliable automation. But the best automation tools can also help ensure the quality of your data. It’s a win-win.
