Risk Management Manual of Examination Policies - FDIC

LOANS Section 3.2
Reference Bank – A bank that sets the lending rate
(LIBOR) at the moment of each loan rollover period
Tranche – In a large syndicated loan, different portions of
the facility may be made available at different time periods,
and in different currencies. These separate components are
known as “tranches” of the facility.
Underwriter - A bank that guarantees the lending of the
funds to the borrower irrespective of successful syndication
or not.
Zeta score - There are models which predict bankruptcy
based on the analysis of certain financial ratios. Edward
Altman of New York University developed a model in
1968 which is used by the regulatory agencies called Zeta.
The Zeta score methodology is intended to forecast the
probability of a company entering bankruptcy within a
twelve month period. It uses five financial ratios from
reported accounting information to produce an objective
measure of financial strength of a company. The ratios
included in the measurement are: working capital/total
assets; retained earnings/total assets; earnings before
interest and taxes/total assets; market value of common and
preferred equity/total liabilities (in non-public
organizations, the book value of common and preferred
equity should be substituted); and sales/total assets (for
non-manufacturing companies, this variable is eliminated).
CREDIT SCORING
Automated credit scoring systems allow institutions to
underwrite and price loans more quickly than was possible
in the past. This efficiency has enabled some banks to
expand their lending into national markets and originate
loan volumes once considered infeasible. Scoring also
reduces unit-underwriting costs, while yielding a more
consistent loan portfolio that is easily securitized. These
benefits have been the primary motivation for the
proliferation of credit scoring systems among both large
and small institutions.
Credit scoring systems identify specific characteristics that
help define predictive variables for acceptable performance
(delinquency, amount owed on accounts, length of credit
history, home ownership, occupation, income, etc.) and
assign point values relative to their overall importance.
These values are then totaled to calculate a credit score,
which helps institutions to rank order risk for a given
population. Generally, an individual with a higher score
will perform better relative to an individual with a lower
credit score.
Few, if any, institutions have an automated underwriting
system where the credit score is used exclusively to make
the credit decision. Some level of human review is usually
present to provide the flexibility needed to address
individual circumstances. Institutions typically establish a
minimum cut-off score below which applicants are denied
and a second cutoff score above which applicants are
approved. However, there is usually a range, or “gray
area,” in between the two cut-off scores where credits are
manually reviewed and credit decisions are judgmentally
determined.
Most, if not all, systems also provide for overrides of
established cut-off scores. If the institution’s scoring
system effectively predicts loss rates and reflects
management’s risk parameters, excessive overrides will
negate the benefits of an automated scoring system.
Therefore, it is critical for management to monitor and
control overrides. Institutions should develop acceptable
override limits and prepare monthly override reports that
provide comparisons over time and against the institution’s
parameters. Override reports should also identify the
approving officer and include the reason for the override.
Although banks often use more than one type of credit
scoring methodology in their underwriting and account
management practices, many systems incorporate credit
bureau scores. Credit bureau scores are updated
periodically and validated on an ongoing basis against
performance in credit bureau files. Scores are designed to
be comparable across the major credit bureaus; however,
the ability of any score to estimate performance outcome
probabilities depends on the quality, quantity, and timely
submission of lender data to the various credit bureaus.
Often, the depth and thoroughness of data available to each
credit bureau varies, and as a consequence, the quality of
scores varies.
As a precaution, institutions that rely on credit bureau
scores should sample and compare credit bureau reports to
determine which credit bureau most effectively captures
data for the market(s) in which the institution does
business. For institutions that acquire credit from multiple
regions, use of multiple scorecards may be appropriate,
depending on apparent regional credit bureau strength. In
some instances, it may be worthwhile for institutions to
pull scores from each of the major credit bureaus and
establish rules for selecting an average value. By tracking
credit bureau scores over time and capturing performance
data to differentiate which score seems to best indicate
probable performance outcome, institutions can select the
best score for any given market. Efforts to differentiate
and select the best credit bureau score should be
documented.
Loans (12-04) 3.2-64 DSC Risk Management Manual of Examination Policies
Federal Deposit Insurance Corporation