Risk Underwriting Process and the 3Cs

According to Barnes et al. (2007) in explaining how S&P
issues ratings for Residential Mortgage Backed Securities
(RMBS), each of the characteristics of every loan in a
securitised pool has a probability of default and therefore
an ultimate loss. S&P‘s analysis addresses this via a layered
risk―or multiple characteristics of risk approach―as in:


1)  Loan structure review (checking for adjustable
rate mortgages or income verification details),


2)  Borrower credit character assessment (through
the use of FICO credit scores),


3) Assessing borrower‘s ability to repay the loan (or
capacity) and,


4) Determining amount of equity (or collateral) a
borrower may have in their home.


These characteristics are combined into a sophisticated
stress simulation test and analysis before any given asset
tranche can be subjectively rated ‗AAA‘ or ‗A‘ , for example.
To this list of requirements, S&P impose a crucial aspect,
namely fraud risk control, especially concerning data
integrity measures.


 Reduced Target Market after Filtering Rules


We can illustrate a broad process for making underwriting
decisions as in Figure 4 above. The approach above also
relates to a version of the process that Van Dijk and Garga
(2006) illustrate in their CML Mortgage Underwriting Report
covering the various parts of the underwriting system. In
essence, this crucial underwriting system depicts an
appropriate methodology for blending external and internal
data. The data inputs will not necessarily be of equal
importance across different applicants for final decision-
making purposes, but it is still crucial to ensure availability
of additional data, if required. The blue coloured box in

Figure 4 reflects external information that helps augment the
internal measures (obtained from the application form and
other sources). To some extent, the policy rules will thus need
to encapsulate this external information in order to allow for
cancelation of the current application if it breaches some pre-
defined parameter (or otherwise to demand a reconfiguration
of terms and conditions in order to obtain final underwriting
approval). Therefore, the process parts of 1) policy rules and
2) fraud checks, together with 3) external data sources, will
effectively act as ‗filters‘ over the target market―reducing the
number of applicants to only those able to pass through these
general policy barriers.


We can illustrate a more detailed underwriting design below:


Figure 5 - Overview of the Underwriting Process


Of note, is that the PD of an applicant can actually change
during this underwriting process, especially as the deal is
being put together (somewhat akin to how the odds of winning
can rapidly change during live betting for sports events) . For
example, applicants who want a bigger more expensive home
than their previous one will be creating greater potential for
default, especially for the maximum possible loan and as it
transpires, the affordability measure proves to be inaccurate
even though they have good credit character and are very
willing to repay the loan. In addition to internal scoring
approaches, one should also investigate any external measures
of credit character as provided by credit bureaux. These
additional sources of information can help refine the internal
models, or otherwise confirm a decision for applicants that a re
not definitively in the good or poor character segments. We
can elaborate further on the main constituent parts of this
Underwriting process in the following sections.