Document classification lies at the heart of modern mortgage document processing software. The volume and complexity of mortgage documents make manual document sorting, a time-consuming, labor-intensive, and error-prone process. Given the large size of datasets in mortgage processing, organizations are turning to automation to classify and process documents with far greater accuracy and efficiency.
In this detailed exploration, we’ll dissect the principles of document classification as it pertains to mortgage processing software. We will uncover the mechanisms behind automated document classification and discuss how technology is reshaping document management in the mortgage industry.
What is Document Classification?
Document classification, also referred to as document categorization, is the systematic organization of documents into predefined categories. In the context of mortgage processing, this means identifying and categorizing documents such as loan applications, property appraisals, and legal contracts.
You're a mortgage professional looking to optimize your document management system. You're not only looking for speed and accuracy but also the ability to comply with ever-changing regulatory requirements. This article is tailored to address your needs and provide guidance in navigating the complexities of document classification in the mortgage landscape.
Manual Classification vs. Automated Classification
Before discussing automated document classification intricacies, it's important to grasp the differences from manual document classification.
What is Manual Document Classification?
Manual document classification involves humans reviewing and categorizing documents subjectively, a process common in mortgage processing. However, it is time-consuming, error-prone, and risky financially and legally. While many companies use manual classification, smaller ones handle it internally, while larger ones outsource due to the high volume. Despite its inefficiency, it remains prevalent. This method faces two critical challenges:
- Time Consumption: Processing a large number of documents is time-intensive.
- Subjectivity: Human biases can lead to subjective and inaccurate document classification.
What is Automated Classification?
Automated document classification utilizes machine learning algorithms to categorize documents based on their content, reducing processing time significantly and ensuring consistent and precise classification. This automated approach is a quicker and more accurate alternative to manual classification. Within an Intelligent Document Processing (IDP) system, documents are swiftly identified, classified, sorted, split, assembled, and processed according to their document type. This process allows you to:
- Scan documents without the need for pre-sorting or separator pages.
- Automatically direct documents to the appropriate E-folders based on their content.
- Categorize both single-page and multi-page documents automatically.
- Identify any documents with missing or incorrect pages.
- Automate the verification of batch document scanning for accuracy.
The Methodology Behind Automated Classification
Automated document classification in mortgage processing workflow is not a "one-size-fits-all" solution. The process is dynamic, iterating through multiple levels of document analysis to arrive at the most accurate categorization. Let’s break down the components of this methodology:
Level 1 - Identifying the File Format
The document classification journey starts with identifying the file format. This initial step discerns whether the document is a scanned image, a PDF, or another digital format. Understanding the document's structure is crucial for subsequent processing, as different file types require distinct handling methods.
Level 2 - Identifying the Document Structure
Structured documents- Structured documents in the mortgage industry, such as pre-designed loan forms, and mortgage applications and come with fixed templates, layouts, key-value pairs, and tables that simplify classification.
Semi-structured documents - Semi-structured documents, like property inspection reports, possess some standard elements, a fixed set of key-value pairs and tables but they vary in terms of layouts and templates. but can vary in format.
Unstructured documents - These documents have no structure at all. There are no key-value pairs, formatting, or tables. Unstructured documents, such as emails and correspondences, offer no format consistency, making classification a greater challenge.
Level 3 - Identifying the Document Type
The final level involves applying classification models. Techniques such as OCR (Optical Character Recognition) and NLP (Natural Language Processing) come into play to analyze document content and assign the appropriate category. This step requires a robust pre-processing strategy and a high-quality tagged dataset to train the classification models effectively.
Automated Document Classification Techniques
Automated document classification employs a range of techniques to interpret and categorize documents accurately. Let's examine the primary methods:
Visual Approach (Compute Vision)
The visual approach utilizes computer vision algorithms to analyze a document's layout, structure, and unique features without reading the text. This method efficiently handles structured and semi-structured documents by relying on visual patterns for categorization during scanning. Rather than reading text, documents are classified based on their distinct structures and styles. For instance, an invoice and a tax form can be differentiated solely by their layout.
Computer vision dissects a document into pixels to understand its structure, style, and layout. These pixels form an image, which is then recognized as objects and classified accordingly. Computer vision has evolved into a significant field in computer science, enabling machines to interpret images in applications ranging from self-driving cars to AI on smartphones. Its potential applications, including facial recognition and pattern recognition, continue to expand.
In computer vision, a feature provides information about the image being processed, aiding in the classification of various elements within documents. By recognizing different information blocks based on document formats, the CV algorithm classifies documents effectively. Modern approaches, such as those in self-driving cars, employ Deep Learning models like CNNs, LSTM, and Transformers to enhance recognition accuracy.
Text Classification Approach
For textual recognition, sophisticated algorithms leverage OCR capabilities to convert scanned text into machine-readable content. Rule-based systems guide the process of identifying and classifying text according to predetermined parameters, achieving increasingly high accuracy levels. Text can be analyzed at different levels:
- Document level: Reads all text in a document.
- Paragraph level: Focuses on text within a paragraph.
- Sentence level: Examines text from a particular sentence.
- Sub-sentence level: Reads specific phrases.
For a deeper understanding let's look at the technique in depth -
Optical Character Recognition
OCR scanners simplify data entry by automatically classifying text, a task that would otherwise be time-consuming. In a basic OCR scanner, light and dark areas are distinguished to identify characters or numbers. By employing computer vision and pattern recognition in an algorithm, the system can recognize text from scanned documents or images.
Feature detection is then used to identify document characteristics like lines, curves, and crosses, storing them as ASCII codes for further manipulation. The OCR program can process various elements such as blocks, tables, images, and formats to extract and classify text efficiently. This technology streamlines data entry and classification processes, saving significant time compared to manual methods.
Rule-based text recognition
Rule-based text recognition involves identifying words in a document using methods like isolation, explicit word segmentation, simultaneous recognition, and more. It can also involve searching for specific terms in a document to determine their context.
In a rule-based system, 'rules' guide the identification of text elements to categorize them based on content. Each rule includes a pattern and a category. For instance, to classify mortgage-related topics, you'd define words for categories like 'Loan Types' and 'Approval Criteria'.
By tallying occurrences of these words in text through a trained algorithm, the system can determine the dominant category and classify the text accordingly. For instance, a phrase like "Mortgage approvals for first-time buyers have increased dramatically" would be classified under 'Approval Criteria'.
Rule-based systems are transparent and can be developed with relative ease, but they require domain expertise and time. Generating rules for intricate systems can be challenging and demands substantial data. Maintenance can be complex due to the continuous addition of rules, which may not always align well with existing ones.
Document Auto-Classification - Benefits and Perks
Implementing powerful document classification tools offers several benefits to mortgage companies. Let’s explore these advantages in detail:
Adaptability to Highly Variable Content
Mortgage documents are highly variable in content and format. Automated document classification tools are designed to adapt to these variations, ensuring that new document types or changes to existing formats can be accommodated without significant reconfiguration.
Employee Time Savings
Automated document classification significantly reduces the time it takes to classify and process documents, liberating employees from mundane, repetitive tasks. This not only improves their job satisfaction but also allows them to focus on higher-value activities, driving efficiency across the organization.
Prevent Data Breaches
Stringent data privacy regulations in the mortgage industry make the accurate classification of sensitive information a top priority. Automated document classification tools minimize the risk of data breaches by precisely identifying and segregating confidential documents, enhancing compliance efforts.
Vaultedge Document Classification in Mortgage Documents
By adopting Vaultedge’s document classification framework, mortgage companies can expect industry-leading accuracy, exceptional scalability, and robust compliance with regulatory standards. Our focus on machine learning capabilities ensures that your document management processes remain agile and future-proof.
In conclusion, the integration of automated document classification into mortgage processing workflow offers practical benefits and a strategic advantage in an industry where precision, speed, and compliance are crucial.
Book a call with a team member of Vaultedge now to explore how our system can enhance your mortgage processing workflow!