The client, a mortgage lender, required a solution to improve the efficiency and accuracy of the loan quality control process. With a large volume of loan documents to review each month, the manual process of indexing and identifying missing documents was time-consuming and error-prone.
The manual process of loan document indexing and missing document identification was becoming increasingly challenging for the mortgage lender due to the large volume of loan documents to review each month. The process was also prone to errors, which could lead to delays in loan approvals and dissatisfied customers.
To address these challenges, DigiClave proposed the implementation of an automation pipeline for mortgage loan document indexing. The pipeline involved the following steps: document ingestion, OCR, classification models, document identification and tagging, rules for missing documents, and officer alerts. By using machine learning algorithms, the system was able to identify missing documents with high accuracy and speed.
The implementation of the automation pipeline provided several benefits to the client. The system achieved an accuracy rate of 95%, reducing errors in the loan review process. The automation also improved the efficiency of the process by 10 times, reducing the time it takes to complete the review of loan files. This resulted in a significant reduction in the training hours required for analysts to perform the manual process. Overall, the automation pipeline provided a scalable solution for mortgage loan quality control, allowing the client to handle large volumes of loan documents efficiently and accurately.