Artificial Intelligence, Data Analytics, Machine Learning, and Predictive Analytics are all techniques that have radically changed the way B2B and B2C companies operate. While the mortgage and lending industries have been a bit slow to jump on the digital bandwagon, they’re finally beginning to come around.
Like a great deal of technical jargon, these terms are liberally used, but rarely explained. In an effort to provide transparency, here’s a verybasic overview of this technology and how it’s dramatically changing the mortgage industry:
Data Analytics requires the collection, storage, and categorization of large quantities of data (like the information collected by FICO or the U.S. Census). In the past, only global enterprises and large institutions had the power to store and/or access significant amounts of data in-house. But now, even your average PC or Mac can access and interact with large sets of data because 1. cloud computing and data storage is more affordable, and 2. applications have become more user-friendly.
The term data analytics refers to the process of identifying patterns and relationships in data, including the use of fundamental statistical analysis. Data analytics has traditionally involved simple algorithms (think graph ‘trend lines’) to organize and forecast business outcomes. As the amount of available data expands, so too does the level of complexity with which it can be analyzed. The natural progression of which is machine learning.
Predictive Analytics is very similar to data analytics, and, really, the primary purpose of data analytics. It relies on both data analytics and machine learning to create baselines to determine the likelihood of something happening in the future. The name says it all: through data analysis, we can predict an outcome, relationship, trend, etc.
Machine learning and artificial intelligence are often used interchangeably, and, although they have similarities (they both refer to computer “smarts”), they are actually quite different.
The algorithms created to note patterns and trends in data analytics only work so well; because they were designed by humans, they are limited in scope and capability. Machine learning vastly improves upon the design. Machine learning “looks” at all the patterns and trends from the data analysis and draws conclusions. As more data is analyzed (and the longer the algorithm runs), the more efficient and accurate the algorithm becomes. This is the “learning” part of machine learning. It’s not true artificial intelligence because its operations still rely on human manipulation, but it is a significantly better way of analyzing data.
Artificial Intelligence focuses on providing the optimal solution by using learned intelligence similar to a human to achieve success in a given task. Unlike machine learning which focuses on analyzing data for the precise correct answer, Artificial Intelligence ‘thinks’ like a human would. Taking in the information from analyzing the data and other external learned elements to provide a logical answer, which may not be found in the data at all. No true artificial intelligence solution currently exists, though several companies are working on it.
The mortgage industry has been a bit of a reluctant participant when it comes to the digital revolution. It’s easy to see why, protocols and procedures in our business have been pretty static for decades.
In too many companies there’s still a heavy reliance on humans to sift through a mix of digital and paper files to analyze data. This human-centric paper pushing is laborious, inefficient, costly, and totally unnecessary.
The industry already has the capability to use data analytics to score loan applications, assess the risk of borrowers with scant credit history, verify application information, predict applicant income, improve marketing campaigns, and streamline customer experience. But the implementation of digital processing could be completely transformative for those brokers and underwriters who chose to take advantage of it. Housingwire notes that “digital processing should be able to reduce the turnaround time from origination to completion dramatically – from months to days, and eventually hours.”
The companies that join the digital revolution will be the top competitors in the upcoming decade. Those that don’t will become obsolete.
Digital predictive underwriting is the newest breakthrough in digitization for the mortgage industry, and States Title is proud to be on the forefront of the revolution.
In the traditional process of clearing title, title agents conduct exhaustive searches through public records (both digital and hardcopy), manually deleting duplicates and old issues, examining records, researching liens and other obligations, and spending hours investigating false positives.
It’s a time-intensive process that can take up to 12 days and cost more than $100 per order. Most importantly, in most cases, it’s a completely unnecessary process because it can be easily accomplished in a matter of minutes with predictive analytics.
States Title has created an algorithm that searches through multiple databases from many different sources to predict risk. The result: that multi-day, costly order is completed in minutes and with considerably less expense. And, because of machine learning and predictive analytics, 80% of the time our underwriting algorithm instantly issues clear-to-close ALTA commitments free of defects.
As a result of using States Title Predictive Underwriting, brokers can expect a 35% increase in profit margin overall. How Predictive Underwriting boosts lender margins.
Predictive Underwriting is just another way machine learning is transforming the way the mortgage industry does business. But out of all the ways you can digitize your practice, it’s probably the easiest.