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The Growing Role of Machine Learning in Mortgage Underwriting


Machine learning is in practically everything these days and is prevalent in the insurance industry as well as the financial services industry. When combined with artificial intelligence, machine learning provides businesses from all different types of industries with the ability to streamline workflows and automate much of the decision-making process. One sector that's set to benefit most from machine learning is underwriting.


Machine Learning Blue Button


What Is Machine Learning in the Financial Sector?

Machine learning is a type of artificial intelligence that uses statistical models taking into account millions of data points to make accurate predictions. It’s power stems from the huge amount of data sets that would otherwise be impossible for a human to ingest, understand and use.

AI is an essential tool in the financial sector that has already been incorporated into calculating credit scores, evaluating risk levels, managing assets, and approving loans.

When implemented properly, this tool learns and improves from the experience it has gained. This data science happens without further programming once the switch is turned on. That’s because machine learning creates its own models. In comparison, traditional models must be developed and set by programmers, which is time consuming and is limited by the programmers’ understanding of risk.


Machine Learning + Underwriting = A Match Made in Heaven?

Even though machine learning is being incorporated into nearly every industry, using artificial intelligence in the underwriting field is a natural shoe-in. The AI learns from predictable workflows, clear outcomes (financed or not), and a data set that grows every day. It’s much easier for the AI to learn from mortgage application outcomes than predicting weather patterns.

Human underwriters must navigate through so many file formats, open various software, and manipulate data when analyzing risk and validating documents. AI does all that in seconds and minutes.

Artificial intelligence and machine learning can also be helpful for finding those edge cases that a human underwriter might miss, which ensures that mistakes are kept to a minimum.


Reduced Default Rates Lead to Cost Savings for Lenders

Over time, a machine learning algorithm can train itself on how to properly analyze consumer data. Metrics include the person's age, occupation, income, and credit behavior. Credit behavior extends to any foreclosures, if loan payments have been made on time, and any past instances of a loan default. The algorithm will then compile this data, compare to what it has learned, and detect potential outcomes to determine if the consumer qualifies for an insurance policy or loan.

Because of how machine learning is able to analyze data before predicting outcomes, underwriters gain a better understanding of the risk that lenders will take on when providing borrowers with a loan. While human underwriters can analyze this same data, they may come to incorrect conclusions. Even if the conclusion is correct, traditional algorithms and analysis take much longer to implement when compared to machine learning algorithms.

You can view machine learning as a form of risk management. Using machine learning and artificial intelligence to identify the potential risk of approving a loan should help you avoid approving a loan that's deemed to be too risky. As a result, loan default rates should decrease, which leads to significant cost savings for lenders.


Ability to Increase Approval Rates Helps Move More Volume

One of the top benefits attributed to machine learning is that the underwriting process can be completed in a much more efficient manner. Machine learning algorithms are able to make quick decisions on credit scoring and underwriting, which saves the lender time and ample financial resources. It's common for human underwriters to request additional documents before they are able to complete the underwriting process and approve the loan.

In comparison, machine learning uses automation to analyze millions of pieces of consumer data, which reduces the need for additional documentation. What this means is that the loan underwriting process can be completed in a shorter period of time, which should result in an increase in approval rates. Higher approval rates indicate that machine learning algorithms can move more volume in regards to applications.


Benefits of Machine Learning in Mortgage Origination

When implemented correctly, a machine learning algorithm can:

  • Reduce operational costs
  • Leverage consumer analytics for risk mitigation
  • Get rid of unnecessary guesswork

Nearly 75% of employees who work for companies using automation via machine learning and other techniques are more satisfied with their jobs. More satisfaction leads to better productivity by a rate of 13%.


Big data and machine learning


Reduce Operational Costs

Chatbots that are based on machine learning have the capabilities needed to field as much as 90% of applicant questions, which lowers the amount of time that loan officers must spend answering these questions. Reducing workloads invariably lowers operational costs.

It's also possible for machine learning to reduce operational costs via workflow automation. Modern automation technology allows for documents to be sent automatically for signing. The many tasks and big data that machine learning algorithms are able to handle streamlines the entire loan origination process, which reduces operational expenses.


Leverage Consumer Analytics for Risk Mitigation

Machine learning during the loan origination process goes beyond the decision-making aspect of origination. Mortgage lenders can use consumer analytics for risk assessment and mitigation. By using compliant strategies, a lender can identify if they should modify a loan or go forward with a short sale or forbearance.

These processes require ample amounts of paperwork and must be properly coordinated to make sure that everything complies with federal regulations. Machine learning helps loan officers better navigate this process by automating tasks and monitoring the workflow.


Get Rid of Unnecessary Guesswork

In the past, many mortgage providers used scorecards to aid in the loan origination process. These cards would collate data before assigning scores to a set of characteristics that determined a borrower's creditworthiness. While these scorecards are convenient, they are also prone to inaccuracies.

Machine learning bypasses these issues by looking at much more data over a considerably shorter period of time. Machine learning provides lenders with the ability to get more from big data. This data can be captured and organized in an easy-to-understand manner. Machine learning algorithms can also use this data to make decisions during the lending process.


The Compliance Concerns with Machine Learning in FinTech

While the FinTech industry is on the rise, there are some compliance concerns in regard to using machine learning in FinTech. These concerns are similar to the ones that exist with many modern technologies. During mortgage underwriting, sensitive consumer data is handled and processed.


Privacy and cybersecurity

When human underwriters manage data, they assess a handful of files at a time. This makes it considerably more difficult for hackers to gain access to large amounts of personal information. Machine learning models on the other hand require millions of data sets. That can be a treasure trove for hackers and identity thieves.

If you don't pair machine learning algorithms with strong security, you may find that you don't comply with important financial regulations in the U.S, Canada, and the EU.


Explainability challenges

If security wasn’t enough, most machine learning models operate in a sort of black box. The algorithm’s output is an answer even the programmers and developers can’t know for certain how it was calculated. The issue of explainability stems from the millions of data points the AI used to create its model and the impossibility for any human to recreate that calculation. It could be hard for a lender to explain to a compliance body how it’s assessing risk.


How Is AI Used in Mortgage Underwriting Now?

Today, many companies have begun to use AI and machine learning in the mortgage underwriting process. For instance, FundMore is built on machine learning and can complete the underwriting process in as little as a few minutes. The many tasks that software built on AI and machine learning can perform include income validation, digital authentication, document storage, and consumer data analysis.

Machine learning algorithms are able to make decisions on underwriting that save a considerable amount of time and resources. Data scientists train algorithms on data analysis to search for any unique exceptions. These exceptions form the basis of the software's decision-making process, which can determine if a consumer qualifies for insurance or a mortgage.

Some additional machine learning underwriting applications include:

  • Auto-generate risk quote: Machine learning algorithms can translate risk factors into a suggested interest rate. The results are based on any historical data that the algorithm has access to.
  • Enhanced document validation: The underwriting process requires reviewing a high number of documents as well as the information found in the documentation. You can optimize machine learning algorithms to validate this information.
  • Decision-making assistance: When assessing a property's market value, human underwriters make numerous decisions, several of which can require human discretion. Even though machine learning algorithms can't make every decision, they can learn from past underwriting cases to make quick decisions in the future.


Other use cases for machine learning underwriting

It’s not just the mortgage industry that’s benefiting from a boost from AI. Insurance companies are creating powerful risk models to compare applicants against. Even the personal credit and car loan companies are using machine learning to assess risk in an expedient manner.


artificial intelligence robot


The Future of Machine Learning Algorithms in Underwriting

Machine learning algorithms are becoming increasingly sophisticated with each passing day. As machine learning becomes more popular, vendors will gain access to larger data sets and data sources. More data means a better decision-making process for the AI.

The future of machine learning with underwriting is also displayed with Optical Character Recognition (OCR) technology. This technology provides underwriters with options for finding, recognizing, and viewing text displayed in labels and images. Without OCR and machine learning in place, the documents and text that an underwriter must review will be comprised of a large amount of paperwork, which is difficult to store and can be lost. OCR helps with data digitization.

This aspect of machine learning is able to process typewritten or handwritten text displayed on a digital image into text that can be read by a machine. Underwriters can then save or process this data as a text file. What this means for customers is that OCR can take text data from many different sources and convert it into a more structured format that makes the data easier to interpret. Once implemented, underwriting should become more streamlined, which will reduce the length of the underwriting process.


Industry Risks of AI in Mortgage Underwriting

Artificial intelligence has already proven to be highly beneficial when paired with the mortgage underwriting process. However, there are potential risks that must be taken into account. The myriad advantages derived from machine learning come at a cost. For the meantime, only larger companies with access to a considerable amount of resources can afford AI. Since only larger companies have access to this technology, they will experience a large efficiency boost that will allow them to outpace their smaller competitors.

Over time, smaller companies could be forced out of the market, which reduces competition and leads to consolidation. Any instance of consolidation is never good for borrowers since they have less options, which means that lenders don't need to offer competitive rates or terms. The upside to this is that the technology is starting to become so prevalent that the price is dropping with each passing year.



Machine learning is here to stay. When it comes to the mortgage underwriting industry, machine learning makes key decisions in a fraction of the time that human underwriters do. Lenders that want to reduce costs and increase approval rates must incorporate AI into their underwriting processes.