nortridge software logo small

Leveraging the right data at the right time a key for lenders

A lending business can thrive or struggle based on its ability to effectively access and utilize data.

Information about borrowers, collateral, rates, demographics, and a wide variety of other items is available electronically. Almost anything a lender could want to know is out there, in electronic form. Nonetheless, the two most important considerations for lenders is how to get the data and how to use it.

There are two primary ways for lenders to collect data and incorporate it into their operational systems. The first is simply to subscribe to the various services, such as valuations, lien reporting, and deal source portals. The information is then manually collected by someone logging onto the data provider’s website and re-entering that data somewhere else. For a majority of data services beyond the basics of credit reporting, this is still the method used by many lenders, especially small and mid-market lenders.

The second approach is automated integration of external data into the operational systems. The benefits of this approach are fairly obvious: All of the key data is automatically in one place, workflow is more efficient, errors are eliminated, and results can be analyzed or correlated based on a complete set of information.

So why doesn’t everybody just do it this way?

The answer is that automatically retrieving data over the web and incorporating it into loan management systems can be tricky for things beyond the basics, unless the loan management system has been specifically designed to connect and use new external data elements. Connecting to a credit bureau and pulling credit is a “basic” that most lenders can probably do with their loan management system. The systems generally understand what a Trade Line is and into which bucket it should go in the operational system. Difficulty typically arises if there’s no “bucket” for newer data elements: Things like “number of times checking account has been overdrawn in last 120 days” or “vehicle crash repair amount” or “average occupancy rate of comparable commercial properties within 10 miles.”

A loan management system must first be flexible enough to simply accept a new data type. Once the data is received, the loan management system must be able to use the new data. If there’s no already-defined bucket for new data, the system must be able to easily create the required fields and define their use. It must then be able to incorporate the new data into operations such as underwriting rules, payment processing, and collections workflow. And in order to evaluate ROI, the new data must be available for use in reporting and analytics to determine how it affects business processes and financial results.

The only way all of the above can be done cost-effectively is if the loan management system can handle all of the aspects of integrating and using external data with configuration, rather than programming. The abilities to readily define a new data type, automatically retrieve it, build workflow rules around it, and analyze results should be critical factors for any lender seeking to stay ahead of the competition.

This website stores cookies on your computer. These cookies are used to collect information about how you interact with our website and allow us to remember you. We use this information in order to improve and customize your browsing experience and for analytics and metrics about our visitors both on this website and other media. To find out more about the cookies we use, see our Privacy Policy.