In today’s fast-paced lending environment, financial institutions face ever-escalating pressure to be more efficient, productive, and responsive to customers through every phase of the lending process. This begins with loan origination; a stage typically laden with challenges and complexities. Yet without proper lien analysis during the due diligence process, lenders face significant issues evaluating the potential risks associated with borrowers and their collateral assets.
One of the greatest obstacles to efficient onboarding is the struggle to gain fast, accurate insights on prospective borrowers. This effort is further magnified for lenders who continue to rely on time-consuming, error-prone manual processes. To optimize onboarding procedures, lenders are increasingly turning to advanced technology such as automation, artificial intelligence (AI), and machine learning. Incorporating these types of solutions helps lenders make rapid, confident loan decisions with expert-augmented intelligence.
By leveraging AI and machine learning, lenders gain the tools needed to execute powerful searches, trace all documents associated with original liens, and quickly and effectively analyze results. Automated solutions are often instrumental in relieving significant due diligence burdens, such as:
- determining lien priority position against other lenders;
- consolidating multiple borrower searches for a single loan;
- quickly identifying UCC amendments, encumbered assets, and lien status;
- obtaining standardized, actionable search results; and
- harnessing essential data across scattered and often inconsistent asset descriptions.
To complete a proficient due diligence process, lenders must be able to properly vet all assets and individuals associated with a single loan ― a task made difficult by fragmented data sources and a lack of real-time data. Additionally, when multiple parties are associated with a transaction, it can be extremely cumbersome to access all core documents and pare down necessary and relevant information. For example, a single loan with multiple co-borrowers might include hundreds of pages of Uniform Commercial Code (UCC) filing documents, spread out among varying sources and encompassing numerous amendments.
AI-driven due diligence tools can enable lenders to create an easy-to-read snapshot of UCC search results. With the ability to identify and apply specified criteria, machine learning can help by generating an aggregated report that highlights the lien condition of all borrowers. In addition, automated reports can deliver a valuable synopsis of collateral risks, such as the number of liens held by borrowers, any liens that could conflict with a lender’s interest, and the position and identification of encumbered collateral assets.
Reports generated by machine learning provide data in a standardized format that allows lenders to drill down into a variety of detailed sections. For instance, lenders can access basic filing information such as the UCC-1 filing number, filing date, expiration date, status, and filing state. Reporting options also include detailed change histories that cover debtors, secured parties, and collateral ― information that is often difficult to trace due to being scattered in multiple locations or having been through multiple UCC amendments.
By examining aggregated, organized lien results that assess collateral asset risk and lien position, lenders are able to optimize their due diligence process and make timely, informed decisions. Deploying these types of automated tools — either through an API or add-on to a search and file platform — not only helps streamline processes and alleviate the time and effort required for onboarding, but can result in cost savings, regulatory confidence, and more accurate insights.
Learn more about how you can optimize decisioning within your own organization by downloading our infographic, “Vantage reporting: The iLien Borrower Analytics edge.”
