Deliver First Time Right Applications with Documents & Online Data Such as Open Banking with our Proprietary AI-enabled SaaS Product

Introduction to Future of Artificial Intelligence

The COVID-19 pandemic had a significant impact on the UK mortgage lending market. Is your organization overwhelmed with too many mortgage applications? Are you aiming at delivering more first-time-right applications? If yes, then the future of artificial intelligence Digilytics AI-enabled SaaS product is here to save the day.

A Quick Status Update!

Life before the pandemic was a tad bit easy when it came to mortgage origination.

Satisfying these two conditions was enough to secure a mortgage for a home or business on most occasions

However, since the pandemic, other factors also play a role in deciding if the application will be approved or rejected.

According to a survey performed by Aldermore bank, four out of five mortgage applications by first-time buyers were rejected since the pandemic. That is, only 19% of applications were accepted compared to the 48% before the pandemic.

While poor credit history is the biggest reason, administrative errors by the lender are also significant contributors to loan rejections. For example, the administrative error rate was only 14% pre-pandemic before quickly rising to 35% in March 2021.

Reason Behind Errors by the Lenders

Over the years, the UK government has introduced many loan origination schemes to help and support both the buyers and the lenders. The Right to Buy scheme, mortgage guarantee scheme, and Bounce Back Loan Scheme are some schemes that help home-buyers and SME businesses.

These schemes increase the confidence among the public, which leads to an influx in mortgage applications. Lenders and case handlers in banks and other lending organizations then have to verify all the data. In the traditional loan origination process, two scenarios occur at this point.

It’s not Always the Lender’s Fault!

Sometimes, the borrower makes errors as well. These errors may be in the form of,

Self-employed professionals mostly face issues with incomplete data and lack of documents. 80% of self-employed UK citizens have faced mortgage rejections. Out of these citizens, over 39% felt that the mortgage origination process should be simplified, with the requirement of a few documents only.

Understandably, lenders cannot shift to processing applications with a few documents only, as they have to verify if the borrower will be able to repay the loan. This verification can only be done with the help of sufficient information.

To put it in a nutshell, these are the requirements that you must fulfill to process mortgage applications and deliver first-time-right applications,

But how can you meet all these requirements and quickly process all the applications at the same time?

Digilytics RevEl: A Product that Efficiently Checks all Your Boxes

Digilytics RevEl is a product that enhances the UK loan origination system using technologies like Artificial Intelligence (AI), Machine Learning (ML), and other advanced techniques.

With Digilytics, you can accurately extract and verify borrower information and deliver first-time-right applications in a single automated mortgage origination journey.

Our product, RevEL is the future of artificial intelligence, a SaaS-based solution that makes lending easy by,

Document Data Extraction

Manual processing involves applications submitted in the form of physical documents. The data is then manually entered into the computer by case handlers or extracted using OCR (Optical Character Recognition) tools.

OCR tools are only limited to structured documents. However, supporting documents like bank statements, tax returns, payslips, etc., are sometimes unstructured. To accurately read and extract data from these documents as well, RevEl utilizes One-shot Learning techniques.

OSL combines machine learning, computer vision, and natural language processing (NLP) to extract data with 95% accuracy. Using these techniques, RevEl enriches the data captured from OCR systems to read all the words in the document.

Document Data Verification

Once all the data is accurately extracted from the documents, RevEl’s artificial intelligence processing can help verify all the information for the underwriter.

RevEl performs the 3C checks on the data that include checking its completeness, correctness, and consistency. It also cross-validates the information with data from other documents.

RevEl can generate predictive models using the borrower’s credit history and buying patterns to help underwriters make better decisions on loan approvals and rejections through all these checks and validations.

As the entire process is automated with Digilytics, the frequency of administrative errors by members in your team is significantly reduced, thereby increasing the rate of first-time-right applications.

Verification Through Online Data Sources

Now that accurate data extraction and validation through documents is possible, what about processing applications of borrowers who cannot provide enough data?

Online data sources are beneficial in such situations. For example, Digilytics has partnered up with AccountScore, a platform that provides complete bank transaction data and analytics directly from the bank. This is made possible with the help of open banking.

RevEl offers Intelligent Affordability Service (IAS) with AccountScore, which enhances the income and expenditure analysis and provides real-time analytics and support so that you can scale your operations.

Other online data sources also help you verify the authenticity of the documents submitted, verify the borrower’s employment data, etc.

Conclusion

Spending a lot of time processing each application and lengthening the feedback loop between borrowers and lenders can increase the operating costs and affect the productivity of the mortgage origination team.

Digilytics can help you reduce your operating costs and time to fund by delivering more first-time-right applications using its AI-enabled SaaS product on documents and online data sources.