How can data extraction and classification help in your digital mortgage process?

Introduction

The Housing Industry in the UK is slacking at closing mortgages soon. According to reports, in 2016, the average time taken for mortgage origination, processing, and closing was a baffling fifty days.

But with the COVID-19 pandemic and the resultant nationwide lockdown, the housing market continued to grow, with more buyers seeking profitable and cheap interest rates with refinancing options or by engaging in a fixed-rate mortgage.

With the spike in applications and restriction on movement, lenders’ operational challenges were exacerbated. Here is where cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP) came into the picture!

Lending organizations were forced to adopt a virtual approach to handle the spike in mortgage origination application volume during the pandemic. And the resultant effect was that the average time taken for the mortgage loan origination process went down to Forty days!

Digitization and automation have shaken up the way the mortgage industry works and is fast becoming a business necessity for lending organizations who want to accelerate their mortgage origination process.

One of the most profound changes that AI has brought in the mortgage origination lifecycle is the digitization of paper records. This method of processing mortgage applications emerged as a solution to deal with the challenges of the growth of image data, volume, variety, and the need for velocity.

In this blog, we cover the technology and methods behind the seamless virtual experience of mortgage origination and the processing of data-rich records.

What is Data Extraction and Classification?

Simply put, Data extraction is the process of recognizing and collecting data from a certain source for a purpose. The mortgage industry is a paper-heavy environment. But over the years, the mortgage file size has steadily and substantially increased. Reports suggest that a typical mortgage application has an average of 500 pages

With a mortgage application requiring a range of Mortgage Documents, Trailing Documents, and Imaged Documents, applicants are required to submit a stack of documents at the time of application and during underwriting for the lending organization to process and run requisite checks, I&E data, and assessments.

All of the above are brimming with data. Traditional lenders conduct data extraction activities manually.

However, with the stark increase in mortgage loan applications alongside the number of records, lending organizations struggle to ingest and capture relevant data manually. Let us explore why this is so.

Roadblocks in Manual Data Extraction from Mortgage Documents

Manual data extraction is filled with challenges. Five major challenges are:

1. High Processing Costs

Manual multi-layer data extraction involves investing in additional resources, time and thus, increases costs for your lending organization.

Another challenge that lenders face is fulfilling customer expectations for a faster turnaround. The big challenge with that is clients expect faster turnaround times from what’s typically common and failing to fulfil these expectations can result in eroded trust and lost leads.

2. Accuracy and Error Rate

Manual extraction and processing of data are prone to embarrassing errors, which can result in transactions being erroneously misdirected.

3. Fragmented Systems

Origination records usually consist of a mix of data is written, digitized, structured, unstructured, imaged records. Manual data extraction rules out the possibility of integration of these systems and increases complexities and inefficiencies.

4. Fail to Scale

Manual data extraction constricts the possibility to scale resources and achieve flexibility in their ongoing and past projects. To meet scaling efforts, lending organizations will have to make heavy investments in the form of personnel, time which drives up costs.

5. Regulatory Compliance

The dynamic nature of regulations and laws makes it an arduous task to change and adapt to the hard and rigid nature of manual data extraction.

To do away with these roadblocks, mortgage origination solutions such as Digilytics Oculyse are emerging that offer automated Data Extraction and Classification to enhance the business process by adding intelligence to improve process efficiency.

Automated Data Extraction and Classification

Most data extraction and classification solutions work by leveraging AI, ML, Computer Vision, Optical Character Recognition (OCR), Deep Learning, and Natural Language Processing (NLP).

These solutions naturally incorporate the decision-making process for determining which data is most important for attaining the overall Extract/Translate/Load aim.

Automated Data Extraction and Classification offers an agile and flexible architecture while ensuring both business and operation needs.

The framework of Automated Data Extraction

1. Document Upload

At this phase, records that form part of the mortgage application are uploaded onto the solution. This may include large PDFs, large volumes of paper files, scanned image files, etc. This is imported onto the system and is automated. This is done with the help of OCR technology and Computer vision.

2. Classification

Machine Learning is essentially about giving the system a dataset and letting it analyze and infer a hidden structure. The system then analyses the papers it receives and employs the learned model using ML tech and auto-classifiers to determine where a document begins and finishes.

3. Separation

Based on the above methods, the identified documents are then categorized into buckets. This gives the ability to output documents upon a user request. By using the SML model, allows for a very time-effective document.

4. Extraction

Based on the data parsing rules tied to the classification, the data of interest can be extracted. For instance, the rules can differentiate between different types of mortgages, terms of sales, consistency checks, and repayments. The extracted data can now be routed to the destination.

The success of these processes depends on the accuracy of the data when a user query is facilitated.

Role of Automated Data Extraction and Classification

1. Fast Time to Offer

Automation of data extraction and classification can reduce human errors, enhance the time consumed to process and close mortgages.

2. Accessibility

The processed data is usually stored on the cloud or in hybrid-cloud environments ensuring that they are accessible on-demand to all lending personnel in the organization. This can especially be beneficial for employees working in remote places.

3. Scalability

Automated data extraction can be effortlessly scaled without any major investments, thus reducing operational costs while increasing profitability for your business.

4. Improved Performance Metrics

Automated Data Extraction provides high accuracy and minuscule error rates when compared to a manual approach. It can save against lengthy processes and delayed funding.

RevEL Differentiators

Digilytics’ RevELl is a ground-breaking technology that is revolutionizing mortgage origination. RevEl uses Digilytics’ Oculyse, an intelligent data capture platform and a form of Electronic Document Management System. Oculyse offers the following features:

1. Intuitive User Experience and Integration

Facilitates viewing of multiple records concurrently. The ability to effortlessly link with not just document mailing and archiving solutions, but also transactional systems broaden the options and improve the user experience.

2. Data Validation

Verifies the accuracy of the information and checks consistency with the host system, such as vendor/client names, product details, passports, bank statements, and payslips.

3. AI and ML Technology Blend

To identify the material, artificial intelligence and OCR are used to detect text on specific pages. Machine Learning technology is used to automatically categorize pages into relevant documents in accordance with a predefined file order.

4. Indexing

Data extraction and auto-population from documents, emails, and other sources are simple and rapid. For example, when an "Account Number" entry is created, it auto-fills other relevant fields – such as payment information, address, and so on.

Features of Oculyse

Conclusion

Digilytics RevEL powered by Digilytics Oculyse offers immense value to lending organizations by providing automation and efficiency to reduce process time, ensuring data accuracy while gaining intelligence through repetitive operator interacting and learning throughout.

At Digilytics, we work towards letting underwriters focus attention on case-specific customer servicing while handling the mess of record processing.

Looking for experts to automate your data extraction and classification endeavors? We at Digilytics would be happy to assist your lending organization