As highlighted in On Demand Credit Facility Overview and Process, there are three main steps in onboarding a facility with our Managed Credit Facility Offering. While the interface allows for a user to onboard fully self sufficiently, dv01 is happy to help with the onboarding:


  1. Runs and Create Borrowing Base
    1. Access different runs (scenarios) and create a borrowing base.
    2. Linking and configuring the datasets that dv01 has ingested as well as mapping the fields needed for each dataset.
  2. Templates and Defining Variables - Borrowing Base Generation
    1. Creating or using a template.
    2. Defining eligibility restrictions, advance rates, excess concentrations, leverage reduction amounts (reserve accounts), manual inputs, and aggregations.
  3. Generate a Borrowing Base
    1. Automation of the borrowing base output.
    2. View the output via our front end and download the excel report highlighting the loan tape, borrowing base specific output, and custom aggregations.


In this section, we will run through what you need when building a borrowing base.  This includes:

  • Creating and Utilizing Templates.
  • Defining eligibility restrictions, advance rates, excess concentrations.
  • Building reporting aggregates and leverage reduction amounts.
  • Creating Manual Inputs.


Templates


Users have the ability to upload a template or leverage an existing template they have for their borrowing base. 


Our team leverages our backend technology scripting to align templates to the user defined variables in the front end. Users can define the variables in their template by using brackets {}. Templates can be customized to clients' preference. Users can also input the transaction date so the determination date in the borrowing base report will be updated.


After you Select Report Template, a modal pops up where you can select from existing templates or upload new templates. The download button next within Actions of Existing Templates allows a user to download the formulaic aspect of the borrowing base template showcasing the defined variables. A backend example of what the borrowing base template looks like can be seen in the video below.

 




Eligibility Restrictions


These are the hard knockouts from an underwriting perspective. In other words, the characteristics we define will dictate the type of loans shown in this vehicle.


We have the ability to name the first column, while the second column follows SQL syntax. The application has auto-complete functionality and users can move in the tables using their keyboard.


Users can amend, move, remove, copy and insert rows by right clicking on the table or simply add a row selecting the green button. An example of this can be seen below.


Advanced Rate


We can be granular on the exact advance rates based on different criteria. Users will have three columns to fill out including:

  • Name - can be customized to any nomenclature.
  • Test - the SQL expression used to define the loan characteristics for the advance rate.
  • Value - decimal format of what the advance rate will be.

Depending on the facility and criteria, advance rates can differ as shown below.


Excess Concentrations


Users can create the exact excess concentrations that their facility needs to abide by. There are five columns including:

  • Name - can be customized to any nomenclature.
  • Field - either a field from the data table (i.e. loan_rate_gross) or a SQL expression (loan_term_orig > 360). Popular SQL expressions include:
    • asset_state IN (asset_state1, asset_state2 ....) used for state concentration within certain states.        
    • coalesce(fico_orig, 300) used for ficos with null values will have a value of 300.
    • loan_term_orig > 360 used for original loan terms greater than 360 months.
  • Condition - referring whether the concentration limit is a minimum or maximum.
  • Limit - the numeric limit to use for the excess concentration test.
  • Aggregation - can input wa (weighted average), sa (simple average), count or sum.
  • Tag Only (optional) - tag if the loan passed or failed this test, but tagged excess concentrations will not impact the run.
  • Denominator Filter (optional) - filter the excess concentration by a specific subset of the population.


Reporting Aggregates


Users can create reporting aggregates that will show on the report. This is helpful if there are metrics that they want to capture, whether it is required or not for their facility. This could give insight on how potential amendments may fair in the current facility. There are four columns including:

  • Name - can be customized to any nomenclature.
  • Field - either a field from the data table (i.e. loan_rate_gross) or a SQL expression (loan_term_orig > 360). Popular SQL expressions include:
    • asset_state IN (asset_state1, asset_state2 ....) used for state concentration within certain states.        
    • coalesce(fico_orig, 300) used for ficos with null values will have a value of 300.
    • loan_term_orig > 360 used for original loan terms greater than 360 months.
  • Aggregation - can input wa (weighted average), sa (simple average), count or sum.
  • Denominator Filter (optional) - filter the excess concentration by a specific subset of the population.


Leverage Reduction Amount


Most leverage reduction amounts like a reserve account is based off a percentage of unpaid principal balance. This amount will be used to subtracted from the advance amount. You can define additional terms in the Loan Aggregates table that can be referred to in the Leverage Reduction Amount table as shown below.


Manual Inputs


Users can also build out manual outputs that would be extracted once the borrowing base is ran. Think of these are hard coded values for the borrowing base waterfall.