Is your office empty these days? For a large number of employees, summer vacation season is an important time of the year when they are able to get their head out of work for a while and simply relax.
Curious to see what machine learning in banking looks like for construction lenders and borrowers? We were too, so we built technology that combines AI and machine learning to change the face of construction loan management forever.
By connecting the dots between budgets, invoices, and lien releases, Contract Simply can help predict future issues and confirm everything is on track in minutes instead of days.
Seeing is believing! Check out the video below and afterward we'll set up a time to show you how it works with your draw documents.
In this example, a bank, let’s call them First Federal, is a commercial construction lender with a $500M portfolio. They manage dozens of loans and the deluge of administrative tasks related to draw requests. For example, within a single draw disbursement request submitted to First Federal, a general contractor pulls together 100's of legal and compliance-related documents including lien releases, invoices, the AIA G702, the AIA G703, receipts, change orders, inspection reports, approvals, and more. Each draw request requires the approval of several parties including but not limited to the loan administrator, third party inspector, title company, and other loan participants. The entire process applies to individual loans as well as an entire portfolio of loans and is core to construction loan management.
We recently attended the American Banker-hosted webcast Addressing Market Needs for Business Process Automation. The focal point of the panel discussion revolved around research conducted by Dana Jackson, Vice President of Research at SourceMedia and Canon U.S.A. They captured data from 300 loan servicing managers and executives. The insights from the study impact lenders including those curious about automation and machine learning in banking and loan servicing.
We find that the Fed typically reports about half of the actual lending commitments. So, with U.S. commercial real estate and land development lending volume approaching $660B (add another $200B for residential), one might assume that the amount of work and oversight required to execute a successful construction project in which all parties of the loan process are satisfied, is a tad high. Which is a gross understatement because a single draw request not only has to go through the hands of numerous professionals, but the process of working towards successful completion of a project without any legal disputes such as a mechanic's lien is a slow, time-consuming, and painful process.
With real estate and land development lending volume reaching more than 330 billion dollars in the US - and we find what is on their books is about half of their commitments - one might assume that the amount of work and oversight required to execute a successful construction project in which all parties of the loan process are satisfied, is a tad high. Which is a gross understatement because a single draw request not only has to go through the hands of numerous professionals, but the process of working towards successful completion of a project without any legal disputes such as a mechanic's lien is a slow, time-consuming, and painful process.
One of the hottest trends right now is machine learning in banking. The eye-opening implications technological advances like machine learning have on construction loan administration and process automation are now reaching the surface. Many of us experience the outcomes of machine learning every day. For example, Netflix and Amazon use algorithms to give us a movie or shopping recommendation based on our prior behaviors. CitiBank utilizes machine learning to evaluate “big data” to prevent fraud and monitor potential threats to customers. Even the United States Postal Service performs character recognition of handwritten characters using an algorithm and a computer vision system behind it.