Gain traceable, transparent, and trusted lien intelligence for borrower onboarding
Today’s lenders are increasingly leveraging Artificial Intelligence (AI) advancements to improve efficiency, reduce risk, and enhance customer experiences. In the borrower onboarding arena, AI and machine learning are helping lenders overcome some of their greatest lien due diligence challenges by alleviating manual review procedures, streamlining procedures, and speeding decisioning. Yet it is important to recognize that all AI solutions are not the same.
When evaluating an AI solution, it is critical to explore a number of factors. How a solution derives content sources, builds intelligence, implements ongoing quality assurances, and provides transparency and traceability are all essential to a lender’s ability to accurately interpret complex lien data.
Before implementing a solution, consider how it addresses the following AI aspects:
Content sources
Breadth and depth of content sources is a hallmark of any successful AI solution. It is important for lenders to assess both quality and accuracy. For example, is the content derived from credible, expert, or peer-reviewed sources? Is the information factually correct, timely, and trusted in the industry or domain?
Intelligence building
Using AI to compile intelligence isn’t just about dumping data into a model — it’s about curating, structuring, and integrating information so that the AI can generate accurate, relevant, and actionable insights. This is accomplished in a number of ways, including sourcing data strategically and using multiple, complementary sources, such as combining structured databases and APIs with unstructured content like reports, articles, and transcripts.
In addition, with the help of machine learning, AI can create systems that can learn patterns from data and improve performance over time without being specifically programmed. Instead of writing step-by-step rules, a machine learning model can be fed with data examples and determine patterns or rules on its own.
The level of a vendor’s expertise can also impact how well a solution’s intelligence is designed, deployed, and maintained, as experienced vendors have the ability to understand which sources are most reliable in a specific industry and can anticipate where data gaps and biases might occur.
Quality assurance (QA)
It is imperative for lenders to be able to trust in the overall effectiveness of an AI solution, from the highest level of testing and data to ongoing QA verifications. The optimal solutions should embed quality into every stage of the process. For instance, domain experts should conduct daily filing reviews that encompass verifying extraction accuracy, metadata tagging, and tree logic to identify possible nuanced or ambiguous cases that might be missed by automation. Additionally, operating quality matrixes at the document, metadata, and customer levels help ensure overall accuracy, confirm that extracted elements align with the source document and are of high quality, and verify that the cumulative quality of deliverables provides a key indicator of system reliability and client confidence.
Transparency and traceability
The right AI solution should enable lenders to confidently and easily trace, audit, and validate extracted insights. This can be accomplished through measures such as displaying UCC1–UCC3 relationships over time, pinpointing the exact location of key data points within the original filing, separating raw facts from AI-generated interpretations, and tracking filing IDs, jurisdictions, processing steps, and model versions.
When developing iLien Borrower Analytics, Wolters Kluwer’s UCC onboarding due diligence solution, Wolters Kluwer took these important considerations into account. Download our brochure, The foundation for optimizing AI in lien due diligence, to learn more about our approach to implement AI best practices in solution development.
