Artificial intelligence and analytics will revolutionize commercial mortgage lending
(Published in Scotsman Guide, January 2021)
For commercial mortgage brokers and lenders to successfully identify funding opportunities during a downturn as severe as the current one, they must stay ultra-informed about the market at large, closely follow asset paper trails and quickly review property records.
Machine learning and other technological advances have allowed detailed data in user-friendly formats to be more readily available to commercial mortgage professionals. What the COVID-19 pandemic is teaching us is that mortgage brokers need to take greater advantage of this data to make sound decisions amid mass levels of economic and market uncertainty.
If you’ve ever purchased something from Amazon (and let’s be honest, who hasn’t?), you’ve witnessed firsthand one of the fastest-growing technologies at work — artificial intelligence (AI). Amazon has successfully used AI to provide tailored product recommendations and promotions based on its customer browsing and purchasing data. In turn, this helps the company generate 35% of its annual revenue.
In fact, AI is so instrumental to Amazon’s growth strategy that the company has reorganized its entire business around AI and machine-learning efforts. The resulting dominance of Amazon has fundamentally changed the online shopping and retail industry.
The financial-services industry is at a similar crossroads in needing to adopt newer technologies so it can compete with its more nimble, nontraditional competitors. And although there are many noteworthy technologies impacting commercial mortgage lending, none may be more important than the AI- and machine learning-based insights that allow financial institutions to transform their businesses and succeed in a rapidly changing market.
Online lending for commercial real estate is still in its early stages, although a number of crowdfunding platforms are now raising both debt and equity for real estate ventures. Meanwhile, financial-technology companies (or fintechs) have already revolutionized the market for direct business loans. Their platforms could become a future model for the commercial real estate industry.
Kabbage is one such online financial-technology company that provides funding directly to small businesses and consumers through an automated lending platform. The appeal can be summarized in a recent Kabbage commercial. A business owner needs funding to scale, but he worries about not having any options. A man from the future appears and shows the entrepreneur a path for securing a loan in only a couple of days, then celebrates with champagne.
This commercial drives the point home that the entire lending cycle can be compressed from weeks to mere hours. Using advanced technology, Kabbage can do the necessary due diligence much faster than the typical financial institution. It's not hard to see a future in which we will see similar platforms emerge in commercial real estate finance.
Since many banking interactions now happen online, borrowers have come to expect a higher level of service, as well as the ability to engage in seamless, multichannel banking. The online revolution has made catering to customers more complex, but it also has enabled lenders to differentiate themselves from the competition. The customer experience can set the lender apart from a competitor offering similar loan products.
In today’s lending environment, everything happens in a compressed time frame, requiring lenders to work faster and smarter. Just like Amazon, financial institutions collect vast amounts of data and analytics on their customers’ buying behavior.
The internet, mobile applications and social media, for example, are creating an unprecedented amount of data on consumer behavior. This offers a much richer dataset for financial institutions to use for their “know your customer” initiatives. Secondly, modern IT infrastructure enables lenders to aggregate large amounts of data in a timely and cost-efficient manner. Data is no longer locked up in functional silos. This data can give enterprising lenders an enriched behavioral understanding of their customers, resulting in more cross-selling and up-selling opportunities.
Once the data is gathered and organized, however, it needs to be extracted and analyzed, and here is where technology can really help companies. Big-data platforms and analytics are enabling companies to identify patterns in the data that were not previously possible. These analytics-based insights are hugely successful in providing the right experience to the right target consumer. The ability to identify the patterns (either positive or negative) leads to uncovering deeper customer insights, making better decisions and generating more profits.
Many financial institutions are implementing AI to add more efficiency to their back office and lessen security risk, while taking the customer experience to a new level. Commercial mortgage lenders operate on tight margins and with a constant need to manage risk.
Given this reality, the ability to leverage lending data and transaction analytics to spot borrower patterns, trends and market opportunities is incredibly valuable. Ultimately, it leads to more efficient and more profitable business. When it comes to the processes, AI is typically used to do due diligence on the borrower, to comply with regulations and to manage the lifecycle of the loan. It also is useful for keeping tabs on the changing status of the borrower’s business.
By using AI, commercial mortgage professionals can quickly process a massive amount of data at an expedited level. AI can significantly enhance the risk models that the lender or broker currently uses. Mortgage companies also can leverage their own data to apply predictive analytics and machine learning in their decision-making. Additional insights and speed enable them to make faster decisions and ensure they are working with a borrower who has the ability to repay a loan.
The loan origination process can produce many recording errors. For example, the smallest error in how a name is documented can cause huge problems down the road and even result in financial losses. Depending on the platform a lender uses, there is now the opportunity to have built-in intelligence that can check for errors.
With enough information about user’s behavior, machine learning can predict errors and prevent them before they happen. Mortgage companies can incorporate better processes and training to prevent their employees from making these errors. Lastly, AI has made optical character recognition technology much more flexible and easier to employ, enabling information to be extracted from scanned images with higher rates of accuracy.
Managing the commercial mortgage process doesn’t stop once the money is in the borrower’s hands. AI enables lenders and brokers to continually run risk analytics on a lending portfolio to ensure that none of their borrowers’ financial situations are changing. This allows mortgage professionals to engage proactively — and well before a borrower goes bankrupt. AI can help lenders and brokers to create early warning metrics that alert them to this type of risk.
For lenders, loan perfection is always top of mind. The loan documents may have been filed, but if it turns out that a name wasn’t spelled 100% correctly, the lender might not recover its collateral if the loan goes into default. AI helps lenders perfect their liens as they are filing them by constantly applying and updating the risk metrics, then alerting lenders to the borrowers who may create the most risk. With an analytics- and AI-based management system in place, lenders can be assured that exceptions will be flagged and addressed to minimize risk to their lending portfolios.
Although there is a lot of pressure on traditional lenders to evolve, the good news is that today’s transformation initiatives and new technology offer a fresh way of dealing with these challenges. In some cases, they offer the ability to leapfrog the competition. AI and analytics can be the innovations to help propel commercial real estate finance professionals in the market. The possibilities are endless.
Sidebar: Optical character recognition defined
Optical character recognition (OCR) is an advanced technology for identifying printed or handwritten text characters inside digital documents, such as a scanned paper document. OCR examines the text of a document and translates the characters into code that can be used for data processing. OCR also is sometimes referred to as text recognition. (Source: TechTarget.com)