Lenders tend to spend long hours filling out an application and gathering documents in their mortgage process. There’s a lack of transparency about the status of the process and uncertainty about, what outstanding documents could be requested later.
Most of the lenders still rely on traditional paper-based manual process. But, with the world evolving rapidly and massive digitization in the mortgage industry, most of the mortgages are missing out the opportunity to capitalize on this change and adapt accordingly.
Lenders are struggling to ingest and capture relevant data due to high complexity and lack of tools to automate the processes.
Here’s when cutting edge technologies like computer vision and trained proprietary machine learning models for mortgage specific documents come into the picture.
In this blog, we will discuss how computer vision, can be used in innovative and differentiated ways to meet industry needs and usage of machine learning models specific to the mortgage industry.
Computer vision is an interdisciplinary scientific field that deals with how computers can gain a high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
Computer vision uses intelligent data capture that involves a combination of multiple technologies, by creating a content flow and integrating it into the existing business workflow and internal systems.
Intelligent data capture provides document storage capabilities in a secured manner and helps in data ingestion/capturing from various sources, data extraction from multiple languages, and document classification using natural language processing and text analytics.
This intelligent infrastructure connects contents from all channels - using image repository, data capture, information management and AI technologies.
Intelligent data capture technology enables automation of multiple tasks that require human intervention, hence saving time.
Scanning devices and mobile capture technologies have extended the scope: Data can now be captured, streamlined, and managed by advance document handling capabilities from customer’s home or workplace to other remote locations. It has improved the speed of operations and decision-making capabilities of mortgages.
DigilyticsTM Oculyse is a platform module for processing, managing and generating insights from e-documents; it’s an AI extension to Electronic Document Management Systems (EDMs) – that enhances the business process by adding intelligence and improves process efficiency by providing access to AI-enabled analytics and automation capabilities.
An integrated intelligent data capture tool that can handle different levels of integration, types of data and highly complex processes.
Steps involved for any type of mortgage document, as illustrated below:
How DigilyticsTM RevEL for financial services dramatically reshapes the mortgage lending process, leveraging Documents such as mortgage illustration, application declaration, payslips, bank statements and affordability assessment form are a rich source of valuable information, that can be leveraged to gain meaningful insights.
AI for document processing is a powerful tool for streamlining workflows, minimising delays, and reducing errors caused by manual document classification.
RevEL for financial services leverages DigilyticsTM Oculyse and recommendation engine modules to automatically classify documents based on structural features (layout-based document classification), textual features (content-based document classification), or both.
It enables users to automatically classify various mortgage specific documents such as payslips, bank statements, legal documents, valuation documents, affordability assessments, correspondences, and others.
It ensures information is available for intelligent decision-making, eliminating risk and cost associated in manual document management by improving the time to offer and time to fund significantly.
Machine learning algorithms provide high levels of accuracy and reliability by handling messy inputs. Different types of algorithms are used to classify documents.
TF-IDF, which stands for term frequency-inverse document frequency, a scoring measure is widely used in information retrieval (IR) or summarization.
TF-IDF reflects the relevance of the term in each document. The logic behind this instant is, if a word occurs multiple times in a document, the model should boost its relevance than other words.
Features of built and trained proprietary machine learning models for mortgage specific documents
Automating all aspects of the mortgage lifecycle is still a challenge. Most lenders will be hopping on the train and integrating AI and machine learning into their systems at some point.
We’ve built DigilyticsTM RevEL with the latest AI technology which will help you save time and money by revolutionizing your origination process.“NOT I – NOT ANYONE else, can travel that road for you, You must travel it for yourself.” – Walt Whitman
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