Example of Machine Learning in Banking and Construction Loan Management

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.

Historically, due to flexibility and familiarity, a massive spreadsheet has been First Federal’s construction loan administration tool of choice for years. Meanwhile, recognition of potential errors and manual overload have often been overlooked.

Challenge

The problem is that, in addition to an abundance of manual work, spreadsheets do not aggregate project data and notify lenders of potential risks based on historical information. With an excess of documentation flying around in PDFs, emails, and Excel spreadsheets, it is often difficult to know precisely where an approval, loan, lien release or draw is in the process.

With the volume of work involved in administering their portfolio of loans, First Federal has embraced the benefits of modern machine learning, process automation, and construction loan software to improve the management of construction loans.

Solution

Through a combination of optical character recognition, computer vision, machine learning algorithms, and rule-based predictive modeling, loan management software parses the information from the countless emails and PDFs included within a construction loan draw request. It analyzes the data and performs “checks” to identify errors. Spreadsheets and forms capture the clean data, and the software creates recommendation reports based on rules and the data.

During the next 24 months, expect more companies to embrace the no-collar workforce by redesigning jobs and reimagining how work gets done in a hybrid human-and-machine environment, automating repetitive tasks and augmenting human performance. – QZ, The Tech Trends to Watch in 2018

A process that had taken hours of human interpretation is now done in seconds. With all of their loan information in one location, The First Federal construction loan administrators now quickly run their project and portfolio level reports like loan composition, cash flow projections, draw processing, and missing lien releases. Moreover, they have access to real-time information about payment status. This visibility allows both First Federal and their borrowers to see who is being paid, and when.

Outcome

The outcomes have led to more profitable loans, faster disbursements for borrowers and their contractors, fewer mechanic’s liens over non-payment disputes, and an opportunity for employees to spend time and effort on things that provide a greater sense of purpose and profits for the bank.

By aggregating and parsing the information from the countless emails and PDFs, machine learning and construction draw software has helped First Federal quickly answer questions:

“Is this invoice for this project?”

“Has this invoice been invoiced before?”

“Is the invoice within the contract amount?”

“Is the project within budget?”

“Does the invoice have the correct documentation?”

“Is the project on schedule?”

Other examples of how construction loan software has assisted the bank with the monthly draw request process include:

1. The borrower, lender, inspector, architect, etc. all quickly track approvals and documents related to the monthly draw process. Coordinating and collecting the approvals have become much more efficient and approvals can now take place in the field by way of mobile applications.
2. The use of a single software platform prevents data entry duplication and manual entry like contractors transferring invoices from their subcontractors, developers transferring invoices from their contractors, and lenders transferring information from their borrowers.
3. First Federal is distributing the appropriate amount of funds as quickly as possible while always ensuring that enough funds are remaining to complete the project and that lien releases are tied to fund disbursements.
4. The bank not only tracks draw disbursements but also monitors the required interest to be billed each month and withholds those funds from the loan amount.

For an example of how a single error led to an undetected $46,000 draw overpayment, see our related case study. In addition to describing how the error occurred, we provide steps taken to “attempt” to remedy the situation and how construction loan management software prevents these types of errors from ever occurring in the first place.

About Rabbet

Rabbet is the leading software for construction lenders and developers. Everything we do—from rapid-fire document processing to real-time reporting—is designed to improve efficiencies and mitigate risk through process automation.

Lenders love Rabbet because our machine learning algorithms help reduce risk and accelerate the approval process usually managed in large siloed spreadsheets. Borrowers love Rabbet because it simplifies the tedious nature of submitting draw requests and expedites draw disbursements.

Lenders and borrowers log into the Rabbet portal to quickly share and review draw requests, documents, invoices, receipts, lien releases, inspection reports, and overall project and payment progress. Everyone spends less time reviewing paperwork and waiting for payments, and more time completing projects.

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