Borrower Underwriting
Data Tape Ingestion
After River receives historical asset performance data it will start with validation that fields are accurate by determining:
The field type, boolean, integer, string, float, or other.
It will then determine the bounds of the fields, average, STD, and other statistical measurements over multiple time periods to find statistical anomalies
The data will then be validated by cross referencing unstructured data like the companies credit policies, as well as structured data such as transaction level information to help identify anomalies and that the data is accurate.
Stratification
Limits are set on different criteria in the asset pool to limit exposure to riskier portions of the assets. For example, if loans in specific industries have inappropriately high risk a threshold will be set to limit the exposure of the loans in the overall pool
River segments the asset pool by key characteristics such as credit score, term, LTV, DTI, loan type, collateral type, industry, and geography.
Assess granularity to ensure no material obligor or asset concentration.
Compare pool composition to the originator’s historical vintages and market benchmarks to gauge representativeness and quality. The goal is to ensure borrowers are able to fund the maximum amount of assets while River can take the minimal amount of risk.
River will adjust for changes in underwriting standards, economic conditions, or asset eligibility criteria over time.
Base Case Loss Determination
River determines a base case loss assumption to understand the loss characteristics of every portfolio. That loss assumption serves as an input for normal course borrower operations for a consistent pool of assets compared to historical organizations.
Use historical data tapes including cohort loss data to calculate gross and net cumulative or annualized losses. If necessary, stratification limits can be incorporated to reduce the base case loss determination for poorly performing segments of the portfolio determined in the step above.
River will also compare borrower specific data to proxy data from peers or market analogs.
A standard loss case assumption is determined over the term of the facility taking into account macro expectations and historical loss performance.
Stress Case Loss Assumption
River will apply a multiple of base-case losses to simulate performance under stress (e.g., 2–5× depending on volatility and advance rate). That stressed loss assumption is meant to assume a recessionary scenario within the term of the financing arrangement and ensure full principal repayment to River.
Additional stresses to loss timing, recovery rates, prepayment speeds, utilization, depreciation, renewal rates, will be run to take into account nuances of the Borrowers platform.
Final modeled cash flows are run to confirm the structure can meet timely interest and ultimate principal payments under each stress scenario.
Determine Advance Rates / Credit Enhancement
The Advance Rate will be measured as borrowed capital divided by their eligible asset pool for each originator. A higher advance rate means more exposure to the assets and less room for an increase in credit losses. Borrowers may be able to increase their advance rate through additional collateral enhancement.
River will derive advance rates based on stressed loss coverage—targeting sufficient subordination, overcollateralization, reserve accounts, and excess spread.
Additional Structural Enhancements will be incorporated
Trigger mechanisms (early amortization, collateral deficiency tests, coverage ratios, excess spread)
Buy back of underperforming assets
Corporate Guaranties (optional)
Lockbox Account
Confirm adequacy through cash flow modeling across stress scenarios
Additional Key Risk Factors
River will look at a number of other risks that are important in determining the quality of the opportunities. Those include:
Originator/servicer strength: operational, financial, and governance quality directly affect collateral performance.
Legal structure: verify true sale, bankruptcy remoteness, and perfected security interest of the SPV assets.
Counterparty risk: assess exposure to banks, trustees, hedging agents, and liquidity providers.
Macroeconomic and sector cyclicality: consider how downturns or secular changes could impair obligor performance or asset resale values.
Data integrity and surveillance: ensure ongoing performance monitoring and trigger management throughout the deal life.
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