The mortgage industry is highly complex and largely dependent on documents for the information required across different stages in their business value chain. Day-to-day operations for mortgage underwriting, property appraisal, and mortgage insurance underwriting are heavily dependent on the comprehension of different types of documents. The slow pace of document transfer between different business units of an organization slows down the overall approval process, leading to poor customer experience.
The mortgage loan approval process usually takes multiple weeks because a multitude of user-submitted documents are scrutinized at each stage to assess the underlying risk. Organizations need the right information at the right time to increase operational efficiency and better document management.
In the wake of COVID-19, the mortgage industry is reeling under pressure to undergo a digital transformation to provide a better customer experience. Large companies are cutting down capital and operational expenditure to sustain operations. The need for operational efficiency is higher than ever.
This post analyzes the role of machine learning (ML) solutions in document extraction in the mortgage industry to enhance business operations.
We highlight the key aspects of Quantiphi’s document processing solution built on AWS, and unveil how it helped a US-based mortgage insurance company address document management challenges through artificial intelligence (AI) and ML techniques.
Quantiphi is an AWS Partner Network (APN) Advanced Consulting Partner with AWS competencies in Machine Learning, Financial Services, Data & Analytics, and DevOps. Quantiphi also has multiple AWS Service Delivery designations, recognizing its expertise in leveraging specific AWS services.
ML-based document extraction for the mortgage industry
Lenders usually have to manually sieve through large volumes of loan packages containing structured and unstructured information to classify documents and identify key information. The identified information is further used for risk assessment. Most of this key information is usually contained in paragraphs, key-value pairs, and tables.
These lenders usually receive loan packages in bulk containing different types of documents such as W2, tax statements, 1008 forms, and so on. Currently, people have to first classify these documents manually and extract the relevant information. Therefore, mortgage firms are looking for meaningful ways of incorporating cognitive capabilities and solutions into their existing mortgage processing pipeline to automate the identification of key information and facilitate easy risk scoring in order to develop operational excellence and red
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/cognitive-document-processing-for-automated-mortgage-processing/