A significant number of data extraction and classification solutions are based on technologies such as Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, Optical Character Recognition (OCR), Deep Learning, and Natural Language Processing (NLP). These solutions naturally integrate a decision-making process to identify the most relevant data necessary for accomplishing the overall Extract/Translate/Load objectives.
The architecture of Automated Data Extraction and Classification is characterized by its agility and flexibility, effectively catering to the needs of business and operational activities.
The Automated Data Extraction Framework
- Document Upload: In this initial stage, documents relevant to the mortgage application are uploaded into the system. This may encompass substantial PDFs, extensive collections of paper files, scanned images, and more. The importation process is automated, facilitated by Optical Character Recognition (OCR) technology and computer vision techniques.
- Classification: Machine Learning involves providing the system with a dataset, allowing it to analyze and uncover underlying structures. The system evaluates the submitted documents and utilizes the learned model, employing machine learning technologies and automated classifiers to ascertain the beginning and end of each document.
- Separation: Utilizing the aforementioned techniques, the identified documents are organized into distinct categories. This categorization enables the efficient retrieval of documents upon user request. The application of the SML model significantly enhances the speed of document processing.
- Extraction: Following the data parsing rules associated with classification, relevant data can be extracted. For example, these rules can distinguish between various mortgage types, sales terms, consistency checks, and repayment details. The extracted information can then be directed to the appropriate destination.
The success of these processes depends on the accuracy of the data when a user query is facilitated.
Importance of Automated Data Extraction and Classification
- Rapid Time to Offer: The automation of data extraction and classification plays a crucial role in minimizing human errors, thus expediting the processing and finalization of mortgage applications.
Improved Accessibility: Processed data is generally stored in cloud or hybrid-cloud systems, allowing for on-demand access by all lending staff within the organization. This is particularly beneficial for employees working from remote locations.
Enhanced Scalability: The scalability of automated data extraction is achievable without significant capital investment, which helps in lowering operational expenses while boosting the profitability of the business.
Better Performance Metrics: Automated data extraction ensures high levels of accuracy and extremely low error rates when compared to manual processes, effectively reducing lengthy procedures and delays in funding.
