The Future of Computer Vision, Machine Learning and Artificial Intelligence in the Mortgage Industry

 

Artificial intelligence, deep learning, and machine learning are not new ideas in many sectors. However, their use in the mortgage sector has been lacking. The good news is that these concepts have moved beyond vision statements and can now be implemented by employing cutting-edge computer science methods while leveraging robust customer databases.

The simplicity with which Machine Learning or Artificial Intelligence can be applied to solve complicated issues with real-world significance has snowballed. Google, Facebook, and even Tesla have utilised artificial intelligence video games like Go to test neural networks, which mimic how the brain learns from experience. Perhaps more than anything else, this indicates that practical applications of these ideas are not far off.

The future of AI, ML, and computer vision in the mortgage business looks promising. As technology advances further, it’s conceivable that lenders will begin to rely heavily on computers to assess mortgage applicants’ creditworthiness. This could be a game-changer for many underserved areas where conventional banking has never been successful before.

 

What is Computer Vision?

Computer vision is a method in which computers can understand and generate information from images. Natural user interfaces such as speech, touch, and gesture recognition have been around for now. In the future, it’s possible that people won’t have to go through menus or type anything at all on mobile devices when they want to complete transactions.

This trend has already started with Amazon Go, an unmanned retail store concept that eliminates cashiers. This may seem like science fiction at the moment, but it would be shortsighted not to take this development seriously after watching what Amazon has accomplished so far with its drone delivery service.

 

Can artificial intelligence, particularly computer vision, assist mortgages in innovative, distinctive ways and adapt to the sector’s demands?

Yes, but it depends on how the latest technologies will be adopted. Mortgage companies can acquire top talent in deep learning and machine learning to assist their investment strategies.

Many business decisions could be taken with the assistance of computer vision, like mortgage approvals using face recognition or other biometric verification methods, which implies no more passwords for customers! The sector has always been vulnerable to cyber threats, so this seems like a significant step forward.

Before implementing advanced data capture, consider the following key performance indicators:

  • Set up accuracy requirements for various sorts of data and documents you intend to acquire.
  • Choose efficient, flexible solutions that include content management and automation based on your company’s needs.
  • The system should be reliable and intelligent enough to reflect human comprehension and decision-making capabilities through context and error detection.
  • Ensure it has self-learning capabilities, which is how well the solution performs with new data types or formats.

 

The approach of Digilytics to computer vision

DigilyticsTM Oculyse is a platform module that processes, manages, and generates insights from electronic documents, which is an AI extension to Electronic EDMs that improves the business process by adding intelligence and streamlining efficiency by giving access to AI-enabled analytics and automation capabilities.

This comprehensive toolkit can deal with several forms of integration, types of data, and highly complex procedures.

Uploading any mortgage document involves the following steps: Document split, Document upload AI-Based Text Recognition, ML Based Auto Classification, Final Review, and DigilyticsTM powered File.

 

Benefits of using a Computer Vision-based Product

All the critical documentation in a mortgage process can be digitised, benefiting both the lender and the borrower.

The whole deal could be completed online without involvement from any human being. This significantly reduces expenses since no physical paperwork needs to be printed, mailed or couriered.

With physical documents being phased out over time, this type of product will become indispensable in the future because it’s able to manage a wide array of data.

AI-powered applications can easily integrate with existing systems without training staff on upgrading software packages to keep up with technology innovations.

 

There are other advantages as well:

Speed – Digitised processes offer faster turnaround times when it comes down to receiving feedback from multiple parties involved when it comes down to loan approvals, for example.

Quality – Documents are more precise when machine learning and computer vision is introduced since they can detect errors in a way that human eyes simply cannot.

Decision-making – In the event of a dispute or discrepancy down the line, algorithms have access to an endless data pool which means they will be better equipped to handle complicated decision-making scenarios.

 

What is Digilytics’ role in the mortgage lending business? How did they develop and train unique machine learning models for mortgage-specific paperwork?

A variety of documents, including mortgage illustration, application declaration, payslips, bank statements, and affordability assessment form, are a rich source of valuable data that may be used to obtain valuable insights.

AI is a useful technology for automating processes, reducing delays, and reducing errors caused by manual document categorisation.

 

Method

The RecEl software from Financial Services uses DigilyticsTM Oculyse and recommendation engine components to automatically categorise papers based on structural properties (layout-based document classification), linguistic characteristics (content-based document classification), or a combination of the two (structural/linguistic).

Users may utilise the service to automatically categorise mortgage-specific documents such as payslips, bank statements, legal documentation, valuation papers, affordability opinions, correspondences, and others.

Information is more readily accessible to enable competent decision-making, reducing risk and cost from manual document management by shortening its time to deliver and fund.

Artificial intelligence algorithms are highly accurate and reliable as they handle complex data. Various kinds of algorithms are used to categorise papers.

Term Frequency-Inverse Document Frequency (TF-IDF) is a standard scoring technique in information retrieval (IR) and summarisation. The TF-IDF method compares each document to a random sample of documents. The goal is for the model to boost the relevance of a term that frequently appears in a text.

 

Built and trained proprietary machine learning model features for mortgage-specific documents

Deployment

To create and deploy models using REST APIs while tracking metadata on those models, look for applications that allow you to manage your data through a Web-based Graphical User Interface (GUI).

Management

To guarantee transparency and easy management of many models, use versioning across many deployments.

Performance

Get automated notifications when models’ performance degrades below a defined threshold, and analyse the efficacy of deployed models.

Explainability

Users should observe the model’s explainability to comprehend the logic behind why it is making a specific decision.

Workspace embedded

It’s simple to connect with transactional processes and fundamental systems (CRM and payment systems).

Role-based Access

Model management is now available on a more advanced role-based basis.

 

The Mortgage Industry: AI and the Future of the Mortgage Business

AI can intelligently process paper documents to significantly reduce reliance on paper and manual validations while also speeding up transaction time.

Also, categorising cases into different processing queues and keeping a real-time status for local internal staff and external partners would considerably lower operational costs and help underwriters with unrivalled knowledge.

Finally, AI explainability with reports will significantly enhance compliance, QA activity and discover new revenue streams for mortgage origination.

 

Takeaway

Automating every stage of the mortgage lifecycle is still a work in progress. The industry will be incorporating AI and machine learning into its processes at some point.

Origination is time-consuming and costly, but it doesn’t have to be. The unique algorithm will revolutionise the process by taking any third-party data sources you may already be using (CRM, marketing automation platforms, email/SMS platforms) and bringing them together in one central location.

It gives a bird’s eye view of all your customer interactions so that you can provide contextually relevant answers for every question while reducing guesswork. Additionally, the platform allows you to automate hundreds of manual steps by connecting directly with systems like Shopify or BigCommerce without writing custom code.

With the latest AI technology from Digilytics™ RevEl, you’ll be able to save time and money by revolutionising your origination process.

If you’d like to share your views or want to know more about our PG courses, please feel free to drop a message in the comments section or reach out to us at admissions@wpurise.com

 

References: