A visual model of a Markov chain, presented as a circle with lines representing the different random walks or decisions

Methodology and Approaches

We place great importance on transparency in our methodologies, and strive to ensure that they are accessible and understandable to all market participants. We provide detailed documentation of our methodology and assumptions, including the factors we consider in our credit analysis, the weightings we assign to each factor, and the process we use to assign credit ratings.

Methodologies for the
Life Cycle

Smart Promises
Structured Finance
A flow chart describing the approaches for credit ratings and structured finance
Corporate (Entity)
A flow chart demonstrating corporate methodologies and ratings credit
Bank Approaches
A foot chart describing bank, ratings, and methodologies and credit rating

Methodologies and Approaches

Promises Anchored To:

Entity Methodology

Spectrum’s Mature Entity Class rating methodology focuses on the balance sheet as the primary credit structure and simulates 10-year forward balance sheets and income statements in a Monte Carlo environment using public disclosure data, and computes embedded real options to produce a forward default probability curve that maps to the Spectrum Entity scale. Right-column, Entity flow chart.

Middle Market entities have a shorter operating history and more leverage in the capital structure but are cash flow positive and growing. The approach depends on the instrument being rated. Payables are a joint-and-several analysis that looks at the importance of the service as well as the borrower’s capacity to repay. Conditionality of the promise makes it a variation on the FACF methodology. On the other hand, when loan performance in CLOs is the focus, Spectrum uses market credit spreads to back out the implied rating of the underlying loans using a Spread Mapping Algorithm. Middle-columns, , Entity flow chart.

Micro entities have a short operating history and are not consistently (or ever) cash flow positive. Spectrum rates them by the SUSTAINABLE PROJECT methodology because it is well-suited to assess micro-entity survivorship and long-term bankability. Using dynamic programming, it ranks the optimality of allocated earmarked funds to achieve contractual, measurable goals along a time-series trajectory.

For both Mature and Middle Market entities, raw performance output is calibrated to the ENTITY scale. For Micro-entities, raw performance output is calibrated to the SmartCity Ratings™ scale.


Spectrum’s Forward Asset Cash Flow (FACF) class of promise comes into play whenever the payment quality of receivables on an entity’s books is substantially higher than the arm’s-length entity rating.

Candidates for using FACF are— 

• Middle market entities that produce special assets (e.g., tech or heavy capital); 
• Collateralized loan obligation funds that tranche the risks of loans issued from the middle market
 • Retail-facing franchises with a naturally highly diversified client base (e.g., consumer lending). 

The question of FACF payment strength is always:
How adequately does the capital structure protect market-required rates of return, up and down the hierarchy of claims, given the quality of forward contractual asset cash flows?

Since the question is always the same, Spectrum’s methodological answer is valid for rating all classes of FACF and maintaining an accurate point-in-time estimation. 

Further implementing details of different types of FACF collateral in different capital structural designs are separately addressed in the FACF APPROACHES.

Next to that schematic is the BANK APPROACH, because Spectrum regards banks and non-bank financial institutions as a special type of FACF that engage in maturity as well as credit transformation.


Spectrum’s Sustainable Project (off-balance sheet) Class dynamically evaluates to invest borrowed funds so as to enable and achieve stipulated and measurable sustainability goals on a trajectory of outcomes. This is a bespoke analysis where the borrower agrees to enter into a governance structure that links earmarked funds using dynamic programming to desired sustainability outcomes using time series data.

The borrower has complete control over the selection of control (funds), the state (sustainable goals/outcomes), the weights applied (to sum to 100%) and the slope of the trajectory corresponding to levels on the credit scale. By choosing an upward sloping trajectory the borrower is signing on to an automatic upgrade by meeting the milestones.

The ranking algorithm produces the ideal investment timeline and milestones against which the borrower agrees to be measured.