Revolutionise Mortgage Document Classification with Digilytics AI

The United Kingdom's (UK) mortgage market reached 1.66 trillion euros of outstanding residential mortgage lending in the fourth quarter of 2020, leading all the other European countries.

But mortgage origination processin the UK isn’t a bed of roses for the buyer or lender. The entire process consists of various steps, from processing the applications to the final acceptance and funding stage.

Furthermore, just like any other application process, the loan origination process also involves a plethora of documents. The lender's job is to scrutinise these documents and verify all the numbers in detail.


Issues the System Faces


Application Stage

Looking into various mortgage deals and schemes like the ‘Help to Buy’ scheme or the ‘Right to Buy’ scheme is the first step that a buyer takes in applying for a mortgage.

Once a buyer decides on the right deal, they send in their application. Along with the application, the buyer also attaches various supporting documents like bank statements, payslips, ID proof, and other forms and statements for the purpose of verification.

The problems the buyers and lenders face in the application stage include:

  • Lack of documents
  • Incomplete paperwork by the borrower

The manual processing in this stage causes a lengthy feedback loop between the two, which leads to a delay in the entire mortgage origination.

Underwriting Stage

When all the documents are properly submitted, the case handler now has to segregate them into their respective categories like income and expenditure.

Once all the data is organised, the lender or underwriter processes everything.

By looking at all the financial data, including the buyer’s assets, liabilities, and credit score, the underwriter has to assess the risk associated with lending money to the borrower.

Due to the manual process involved in this step as well, there are high chances of

  • Error in the data entered and catalogued.
  • Misplaced documents as there can be multiple sources like e-mail, post, etc.

As a result of all these challenges, the average time taken to process the funding from the application stage is over 35 days.

This time-consuming process increases the operation cost of mortgage origination and sometimes leads to unsuccessful loan contracts.

Automated Approach

The automated approach in handling documents and performing analysis simplifies the entire process. It makes it more streamlined and reliable than the manual way by leveraging Artificial Intelligence (AI) and Machine Learning (ML).

Using advanced algorithms, AI mortgage tools compare the buyer’s financial information and check if it matches the mortgage requirements and the lender’s guidelines.

By providing detailed analytics, the lender can then make effective decisions on loan approvals or rejections.

The many advantages of using machine learning mortgage tools include:

  • The automated organisation of documents
  • Reduced errors
  • Increased efficiency
  • Enhanced team productivity
  • Reduced human workload
  • Elimination of problems rising due to inadequate availability of staff

To make your underwriting process and decision-making easy, Digilytics offers a state-of-the-art artificial intelligence product through which documentation and analysis become easy.


The Digilytics RevEl Midas Touch – Revolutionising Document Processing


Digilytics RevEl is easy to use, can be plugged on to any loan origination system. The Digilytics platform overcomes all these problems by using intelligent image processing. The AI powered mortgage lending tool can eliminate human interference in entering data and processing documents.


The 3C Health Check


Completeness

This check overcomes the problems involved in incomplete paperwork by validating a document, like a bank statement, to see if all the information fields required are complete. Post automated classification, the extracted data is checked to ensure the presence of the required data on documents.

Correctness

After checking the completeness of the documents, Digilytics RevEl measures the correctness of the data in the documents using intelligent data capture. By utilising a combination of deep neural networks, machine learning models, and Natural Language Processing (NLP), RevEl verifies the correctness of the extracted data.

Consistency

The last check that RevEl performs on the documents is for their consistency. The AI-based tools compare each data field in a document with another document to check the consistency of that data.

Inconsistent data across documents helps the underwriter in making better decisions.

Manually checking the completeness, correctness, and consistency of the data in a document would require a lot of manpower and time.

Classification

RevEl can also classify the documents into separate and relevant categories set by the user based on its content, irrespective of it being structured or unstructured.

For example, salary slips are put under the ‘Income’ category, and general monthly expenses on essential stuff are categorised under ‘Expenditure’.

Using AI-based text recognition and ML-based auto-classification, the documents are all segregated and organised into one single electronic file for smooth access.

Digilytics also gives you access to manually check the classification of the documents so that you can double-check the entire process.

Data Processing

Digilytics RevEl offers Intelligent Affordability Services (IAS) that allow seamless and accurate data extraction from various documents.

Based on the information and data in the application, the AI tools can also generate tasks or assign the application to an underwriter.

Underwriters can also annotate the pages in the documents or add notes to them. All these small details put together make RevEl your go-to product as it makes the loan origination process efficient, labour-saving, and time saving.


Digilytics RevEl – An all-rounder


RevEl isn’t restricted to the field of documentation and data processing alone. It also provide support in other areas, including:

  • Its chat support and query system help in improving the customer and intermediary experience.
  • Predictive models assist underwriters in deciding the risk involved in accepting a buyer’s application and funding loans.
  • Glass-pipe tracking provides real-time metrics and analytics that also assist a lender’s decision-making.

RevEl has benefitted many lenders across the UK in the following ways:

  • Increasing gross lending by 15%
  • Reducing operation costs by 30%
  • Improving productivity by 40%

Conclusion

The artificial intelligence mortgage industry is taking over the UK mortgage market. Leveraging the power of AI and ML has made significant improvements to document analysis, classification, and processing.

You, too, can revolutionise your mortgage origination using Digilytics RevEl to save time and resources you'd have otherwise spent on manually classifying documents.

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