4. Introduction
Non-Maturity Deposits (NMDs) attract a lot of attention
from banks as well as regulators. This “demand liability”
constitutes a major portion of the liability side of a bank’s
balance sheet. NMDs, such as retail savings, interest and
non-interest bearing checking, and money-market accounts
have no stated maturities and depositors can withdraw
their funds at any time without penalty. The depositor’s
early redemption option is very challenging to model
accurately.
Banks typically view NMDs as a stable source of funding
for their credit and investment books on the asset side.
There are two primary types of models needed to forecast
deposit cash flows: 1) Rate Models for interest-bearing
NMDs and 2) Balance Models for “all” NMDs.
Deposit rate models attempt to ascertain the relationship
between short-term market interest rates and bank deposit
rates, i.e., rate beta ( ).
=
Change in Deposit Rate
Change in Market Rate
Betas may differ in rising versus falling market rate
scenarios. Historically, deposit rates tend to lag market
rates on the way up and adjust quicker than market rates
on the way down. Deposit rate beta models are often
based on a regression analysis. However, management
judgment is sometimes used to derive rate betas.
In addition, deposit rates offered by competitors can also
influence rate-setting behavior. More advanced deposit
rate models attempt to capture competitor deposit rates
and other non-market rate factors.
Deposit balance models attempt to determine the
relationship between short-term market interest rates
(along with non-rate factors) and deposit balances.
This paper will primarily focus on Balance Models.
The modeling of NMD balances is relatively more difficult
to model than rates. NMD balances can be volatile and
influenced by numerous factors. The accurate modeling
of NMDs is a very important part of the asset/liability
management (ALM) function for any commercial bank or
deposit taking Financial Institution.
It is not uncommon for bank assumptions about deposit
balances to have an outsized impact on their measurement
of interest rate risk and liquidity risk. Since the financial
crisis of 2008, bank managements and regulators have
increasingly focused their attention on the forecasting and
analysis of NMD, especially after an extended period of
low short-term market interest rates and the possibility of a
FED tightening.
This paper explores leading practice methodologies that
can be utilized to robustly model NMDs to assist banks
and other financial institutions in managing their interest
rate and liquidity risk more effectively. There are two main
reasons why banks should focus on developing robust
deposit models:
Interest Rate Risk Management
For a bank to control interest rate risk, management of the
Asset and Liability sides of the balance sheet is necessary
and require assumptions about the behavior of NMDs.
NMD assumptions affect interest rate risk measurements
related to net interest income simulation and market value
of equity or duration analysis. Inaccurate modeling of
NMDs can lead to misleading interest rate risk exposure
measurement and suboptimal decisions.
Liquidity Management
For a bank to remain solvent, liquidity needs to be
managed and for this NMDs play a pivotal role. NMD is
one of the most inexpensive forms of funding, but the on-
demand nature, i.e., redemption option, requires a financial
institution to understand the risk and stability of its deposit
base. New regulatory requirements, such as the Liquidity
Coverage Ratio (LCR) and the Net Stable Funding Ratio
(NSFR), also require a detailed understanding of NMD
behavior.
5. Explorationof
factorsthataffect
depositrates
Various Macro Economic Variables (MEVs) impact the
movement of NMD rates. There can be cases where the
three main categories of NMDs, i.e., Savings, Checking
and Money Market, can move in line with different MEVs.
A regression analysis of industry data was conducted
where NMD rates were considered dependent variables.
One and six month Libor rates and three, six and
one year Treasury rates were considered predictor or
independent variables.
The graph and the correlation study for the same is given
below for illustrative purpose to identify the potential
drivers which impact deposit rates.
3An introduction to non-maturity deposit rate and balance modeling
6. MEVs Savings Interest Checking Money Market
<100M
Libor 1 Month 0.78 0.79 0.80
Libor 6 Months 0.53 0.57 0.63
Treasury Rate 3 Months 0.72 0.71 0.71
Treasury Rate 6 Months 0.82 0.83 0.86
Treasury Rate 1 Year 0.85 0.87 0.91
Table – 1: Correlation analysis of MEVs with NMD interest rates
As previously mentioned, deposit rate modeling can include other factors beyond
a single rate beta. Enhancements to deposit rate models can include lags, multiple
betas under rising and falling market rates, and competitive deposit-setting
behavior. Now let us turn our attention to deposit balance modeling. We will start
with a general framework for analyzing deposit balances.
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
Jun-09
Sep-09
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Dec-13
Mar-14
Jun-14
Sep-14
Dec-14
Mar-15
Jun-15
Percentages–Solid&DashedLines
Percentages–DottedLine
Quarters
Movement of NMD rates w.r.t. MEVs
Savings(NR) Interest Checking(NR) Money Market <100M(NR)
Libor 1 Month Treasury Rate 3 Months Treasury Rate 6 Months
Treasury Rate 1 Year Libor 6 Months
SelectiveMEVsfor
considerationinNMDrate
modeling(illustrativepurposes)
Graph-1 | The graph depicting the relationship of NMD A/c interest rates with
MEVs | Source for NMD rate data – FDIC
7. 5
Usingtherunoff,retention and
growthestimationfor
behavioralanalysis
Categorization of
Deposit Base into
Short term and
Long term buckets
Computation of
Runoff/Decay,
Replacement
and Growth
Macroeconomic
Scenario
Analysis done
using Multiple
Linear
Regression
Liquidity
Management/
Balance Sheet
Optimization/
Asset Liability
Mismatch
Management
Behavioral Analysis
for the depositors
are done using
Categorization of
the Depositors:
Consumer, Small
Business and
Commercial
Businesses
Chart – 1: Industry Leading Practices
Developing a more precise runoff profile of the deposit portfolio improves the
effectiveness—and potentially reduces the cost—of liquidity risk management.
Modeling for Non-maturity deposit volume requires the following:
–– Deposit Decay Rate/Run-off percentage
–– Retention Rate and
–– Forecasted Growth
An introduction to non-maturity deposit rate and balance modeling
8. For the above method the Run-off percentage, Retention rate is computed and
then the Decay rate is calculated using the following formula:
Decay Rate = Run-off ÷ Total Deposits
0 2 4 6
Time Period
NMDs, $
Non-maturity deposit runoff and replacement
8 10 12
Deposit Base
Forecasted Growth
Run-off Replacement
Graph-2 | The Graph depicting the NMD Runoff, Replacement and forecasted
growth | Source: FDIC
These targets are achieved based on a behavioral analysis of depositors. Runoff
behavior of deposit accounts varies significantly depending upon a wide range of
factors, market segments, and individual customer characteristics. A behavioral
assessment focusing on these factors enables the generation of more accurate
run-off expectations. For example, for retail customers, factors such as the
vintage, ATM visit frequency, types of accounts maintained and so on are
important. For small businesses, factors such as Credit Usages, Interest payment
frequency, and debt servicing history may be important. For Large Institutions,
Treasury trade usages, Industry segment and Net Borrowing positions could be
important factors.
Usingtherunoff,retention
andgrowthestimationfor
behavioralanalysis(cont..)
9. Keychallengesto
modelingNMD
While many banks face challenges in developing NMD
assumptions, global banks in particular are facing
increasing scrutiny over their NMD models. Basel recently
released new guidance on interest rate risk and how
deposit modeling impacts its’ measurement. Some of the
global key challenges which banks are facing that make
NMD modeling difficult are listed below.
No significant change in short-term market rates
since 2008
Post the 2008 financial crisis, there has been no significant
changes in short-term market rates. Key interest rates,
such as Fed Funds rate, have been very low for many
years. Deposit rates have remained very low over this
same period. Therefore, regressions based on this time
period will be greatly constrained by data from one
particular rate regime.
Influx of Deposits into Banking System
There was a very large increase in deposits due to a “flight-
to-quality” precipitated by the financial crisis of 2008.
These deposits have remained in the banking system.
There has been much debate on how sticky these deposits
will be when market rates begin to rise.
Regulatory Challenges and New Regulations (Basel III)
Post the financial crisis, regulatory scrutiny increased
substantially for banks. Banks are expected to model their
deposits accurately to conduct stress testing. In addition,
they are required to hold sufficient capital against interest
rate and liquidity risk.
New regulations from Basel, such as the LCR and NSFR,
focuses more on the core deposits (NMDs), held by a bank
for a particular tenure (30 days and 1 years respectively for
LCR and NSFR).
Excess Liquidity at many banks post-financial crisis
Excess liquidity poses a problem for banks as the same
must be invest NMDs at currently low rates. This has
reduced the net interest margin for banks. Deposit rates
are at a floor while asset rates have continued to fall over
the last number of years.
Small amount of data and in some cases unavailability
of historical data
Many banks, especially community banks and de novo
banks, do not have sufficient historical data or no historical
data at all. This poses problems for conducting robust
deposit analysis. We have also found large institutions that
face challenges with sourcing granular deposit data over a
sufficiently long historical period to analyze.
Difficult to predict the behavior of retail customers
It is difficult to accurately predict the behavior of individuals
on a purely economic or rational basis. For example,
although deposit rates were low during and after the crisis,
depositors accepted low rates in order to safeguard their
funds, i.e., flight-to-safety. These non-economic factors
complicate statistical analysis of deposits and often require
the use of categorical variables.
Potential inaccurate deposit assumptions in stressed
scenarios
Banks may under or over-estimate the stability of their
deposits. This can lead to inaccurate asset and liability
management decisions. Model risk makes it necessary to
perform assumption sensitivity testing.
7An introduction to non-maturity deposit rate and balance modeling
10. HowKPMGcanhelp–
detailedapproach
KPMG, with its expertise in the field of Deposit Model development,
can help banks develop models to accurately estimate the NMD of a
bank’s portfolio. KPMG approach includes collecting data, creating a
data mart, segmenting data, choosing modeling techniques, and stress
testing. These steps are described in detail below.
Datarequirements
KPMG explores various sources of data to create a data mart to
conduct a detailed exploratory data analysis to define the segmentation
and modeling approach.
Data Mart
FDIC
DDA
NOW
MMDA
Savings
Key
MEVs
Other
Sources
Federal
Reserve
Internal
Data
Pool
Chart – 2: Different Data Sources
11. Selective MEVs for consideration in NMD
balance modeling (illustrative purposes)
There are numerous macro-economic variables (MEVs) that may
impact a financial institution’s deposit balances. The significance of
particular MEVs vary by institution. The following are a few potential
MEVs that may impact the expansion or contraction of deposits.
U.S. Unemployment rate
The US unemployment rate affects economic growth and the Fed’s
decision making on lowering or hiking the interest rates. Changing
unemployment rates may impact deposit balances.
5 Year treasury rate
The 5 year Treasury Rate reflects the long term risk premium required
by investors. Changes in long term rates or the yield curve may
impact deposit balances or movements among deposit types, e.g.,
NMD versus Certificates of Deposit.
3 Month LIBOR rate
Short-term rates are a key indicator of liquidity in the market. They
also serve as a proxy for investments that depositors may use as
alternative to bank deposits.
Repo rate
The repo rate is the rate banks receive for short-term lending of
investments, i.e., U.S. Treasuries. This is a short-term market rate
which, along with LIBOR and other rates, may impact deposit balances.
Negative
Correlation
Type of Relationship of NMD with the MEVs
VIX
Real GDP growth Rate
(Inflation Adjusted)
House Price Index United S.tates
unemployment rate
Mortgage Rate
Repo Rate and Fed
Funds Rate
5-Year Treasury Yieldand 1 M LIBOR Rate
Positive
Correlation
Chart – 3: Relationship of MEVs with NMD
Dow jones total stock market index
The Dow Jones Total Stock Market index is a popular proxy for the
broad U.S. Stock market. Depositors may see common stocks as an
alternative to deposits under certain conditions.
9An introduction to non-maturity deposit rate and balance modeling
12. Graph depicting the movement of NMD balance w.r.t. MEVs
(illustrative purposes)
0.00
5,000,000.00
10,000,000.00
15,000,000.00
20,000,000.00
25,000,000.00
-2.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
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Jun-09
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Jun-14
Sep-14
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Mar-15
US$.Million
Percentage(%)
Quarters
NMD Deposit Base w.r.t. MEVs
Un-employment rate 5-year Treasury yield
3M Libor Treasury GCF Repo Weighted Average Rate
Dow Jones Total Stock Market Index (Level) (Scalled up by 1000) NMD
House Price Index (Level) (Scalleup by 100,000)
Graph – 3: Relationship of potential MEVs with NMD
Note: Scaling factors have been used for fitting the MEVs to the graph above.
It can be difficult to visually see the relationship between MEVs and deposit balances in graphs,
such as Graph 1 above. Therefore, statistical methods are often used to clearly identify these relationships.
For example, Table 1 below shows the relationship between various MEVs and Deposit Balances derived
via statistical methods.
Correlation between sample MEVs and NMDs based on aggregate
industry deposit data
MEVs Mar 2005-Mar 2015 Dec 2009-Mar 2015 Mar 2012-Mar 2015
Unemployment rate 0.41 -0.99 -0.98
5-year Treasury yield -0.83 -0.27 0.91
Dow Jones Total Stock
Market Index
0.70 0.95 0.96
House Price Index -0.44 0.87 0.97
3M Libor -0.80 -0.45 -0.74
Repo Rate (Treasury) -0.73 0.17 -0.45
Table – 2: Correlation analysis of MEVs with NMD balances
Note: The figures above are correlation coefficients ranging from -1.00 to + 1.00 with -1 meaning
a perfect inverse relationship and +1.00 meaning perfect direct relationship.
Datarequirements
(cont..)
13. Keyfactorsincludedinthe
modelingofnon-maturity
depositbalances
Long term trend
For estimation purposes, the long term deposit trend
needs to be taken into account as the short term trend
may not provide the correct estimate. In addition, many
banks desire to derive long term forecasts.
Underlying or Core deposit trend
The Core deposit trends needs to be taken into
consideration as it reflects the relationship between
stable and less-stable deposits. It shows the bank deposit
stability trend over a period of time with respect to the
volatile trend.
Seasonality
NMD movement can be seasonal based on the behavior
of the customers of a bank. This can be observed
within a span of one year or beyond. For example, this
impact can often be seen around holidays that involve
increased purchases.
Annualized (Daily) volatility
The volatility of NMD balances can be attributed to the
attrition, acquisition, and/or cross product movement of
customers of a bank. This can pose a serious challenge
to a bank and should be addressed while building an
estimation model.
Stochastic movement
There may be some component of a deposit balance which
could be very volatile because of depositor or market
conditions. This can be predicted using the stochastic
process of balance movement and capture the probability-
oriented balance.
Trend Breaks
It has been seen from past experience that there can be
a trend break in deposit balances because of no activity in
accounts or sudden inflows of deposits that makes a trend
model unstable. It is necessary to capture trend break
points when applying modeling techniques.
Segmentation – “stable” versus
“less-stable”
NMD
Accounts
Stable Less-Stable
Chart – 4: NMD Segments
NMD accounts can be classified into “Stable” versus
“Less Stable” segments depending on the potential of
funds to move into and out of these accounts. There
can be withdrawals deposits, or cross migration of funds
among multiple NMD account types, e.g., checking,
money market and Savings. In addition, funds can move
completely out of NMD to longer-term fixed maturity
deposits (CDs). On the other hand, some portion of NMD
can be considered “Stable.” This portion of NMD tend
to be more stable over time due to minimum balance
requirements and customers’ needs. Typically, the “Less
Stable” balances are modeled with interest rate sensitivity.
There are a number of approaches that can be used to
segment deposits into “Stable” versus “Less Stable”
cohorts. Most of these approaches require analyzing
historical deposit data.
11An introduction to non-maturity deposit rate and balance modeling
14. $-
$200
$400
Q2'2013 Q3'2013 Q4'2013 Q1'2014 Q2'2014 Q3'2014 Q4'2014 Q1'2015
Balances(inMn)
Stable Deposits = $288 Mn (82%)
Stable (Average of Predicted Deposits) Less-stable (Actual Predicted Deposits)
Stable vs. Less – stable component
Graph-4 | Graph depicting the movement of Stable and Less Stable component
The following formula can be used to compute the Stable component ratio:
Stable Component Ratio =
Stable Component
99% Confidence Interval of Predicted Deposit Balances
Where,
–– Stable component is derived as shown below in Approach 1, Approach 2 or Approach 3; and
–– Less-Stable component is the actual predicted deposits
Some Simple approach are listed below:
Approach
Stable component Ratio = Average of Predicted Deposits
Time (Month) Predicted Deposits
1 601
2 616
3 604
4 638
5 674
6 642
7 684
8 704
9 730
10 723
- -
- -
- -
- -
- -
N 798
Table – 3 | Depicting the Stable component ratio
Apart from the above approach the following approaches can be used
Stable Component =
Average (Predicted Deposits)
15. Using Moving Averages:
Stable component ratio = Average (Moving averages of 6 months actual predicted deposits)
Using Moving Medians:
Stable component ratio = Average (Moving medians of 6 months actual predicted deposits)
There are some complicated advanced approaches as well:
–– Identify the characteristic of “Stable” vs. “less Stable” using data analysis and then use one of
the quantitate approach mentioned below to predict.
–– Holt Winter Exponential Smoothing.
Deposit modeling methodologies
Methodologies to model NMD balances be followed can be broadly classified into (a) Quantitative,
(b) Non-Quantitative, and (c) Hybrid approach.
a. Quantitative approach
Quantitative or statistical modeling approaches can be used for the estimation of deposit balance
behavior. There are several techniques which could be used as potential modeling techniques. Pros
and cons are given below for each technique:
Method Pros Cons
Multiple Linear
Regression
–– Simple statistical model that is
useful for forecasting and is easy to
interpret
–– Can use more than one predictor;
synergistic relationship can be
modeled
–– Approach allows easy
handling of autocorrelation and
heteroscedasticity
–– Significant dependency on external/
macroeconomic factors
–– It is not always possible to get
a linear relationship between
dependent and independent
variables and transformations
become complex in nature
ARIMAX
approach
–– Time dependent linear relationship
may provide better forecast
–– Better fit, less error, captures time
impact and autocorrelation which
happens over a period of time
–– Research shows better fit on smaller
data set and captures trend breaks
–– Relies exclusively on past demand
data
–– Conversion of real equation to
evaluate error is complex or
complicated to fit
Vintage based
approach
–– Takes into account the historical data
and creates several overlapping time
series data with different starting
times for prediction
–– Used in banking industry for various
applications
–– Requires long horizon of data
–– Less statistical in the nature
Markov Chain
approach
–– Approach to capture the dynamic
nature of balance and probability
association of volatility from the past
time series
–– Captures movement of accounts
from one state to another
–– It is complex to build and understand
–– Need to make some key
assumptions before it can be applied
Table – 4: Pros and Cons of different modeling approaches
13An introduction to non-maturity deposit rate and balance modeling
16. When a bank’s historical data set is small, KPMG may
provide an estimation of deposits using ARIMA(X) or
Markov chain approach. On the other hand, when the data
set is large, i.e., long term historical data is available for a
bank, then Multiple Linear Regression or Vintage-based
approach can also be employed along with ARIMAX and
Markov Chain.
b. Non-quantitative approach
Qualitative factors after communicating with lines of
business and expert judgment may be taken into account
for deposit behavior estimation.
c. Hybrid approach
A combined solution of quantitative and non-quantitative
approaches may be used for estimation. This involves
allowing a quantitative model to provide a base estimate
of deposit behavior and overlaying results with justifiable
management overlays.
17. Depositmodelinginthe
contextofasset/liability
management
Robust modeling of non-maturity deposits can lead to more
accurate measurements of interest rate risk and liquidity
risk, respectively, within the context of ALM.
Interest rate risk – market value of equity (MVE)
A deposit balance model and rate model to project cash
flows, as well as an appropriate discount rate, are required
to derive a present value of deposits for Risk to MVE
purposes. Some practitioners also analyze branch sale
premiums to get an indication of the value of deposits.
Deriving the initial value of NMDs is the first step in
MVE analysis. The change in market value of deposits
under market rate changes can be calculated using a
full re-valuation or an estimate using the duration and
convexity of the deposits.
The assumptions used for deposits, i.e., duration/life
estimates, can have a significant influence on the Risk to
MVE measurement for a typical bank. The measurement
of Risk to MVE helps inform management’s longer-term
decisions regarding NMD strategy and interest rate
risk hedging.
Interest rate risk – net interest income (NII)
The deposit balance and rate model are also needed to
project interest expense for measuring interest rate risk
to NII. However, due to the often short-term nature of
these measurements, deposit rate models tend to be
relatively more impactful.
Net Interest Income can be projected under different
rate scenarios to assess whether a balance sheet is
asset-sensitive, i.e., assets repricing faster than liabilities,
or liability-sensitive, i.e., liabilities repricing faster than
assets. The measurement of Risk to NII helps inform
management’s decisions regarding NMD rate-setting
and retention strategies, as well as natural and synthetic
interest rate risk hedging decisions.
Liquidity risk
The deposit balance model and rate model are also needed
to measure liquidity risk. However, deposit balance
models are more impactful for liquidity risk measurement.
The primary question that these models help answer is
how reliable are NMDs for funding purposes in normal
and stressed environments. In the current environment,
there is a lot of concern from banks and regulators on
whether the deposit influx prompted by the financial
crisis will become a rush to the exits if market rates
increase significantly.
Assumption sensitivity analysis
All models are subject to model risk. NMD models are no
exception. Leading practice is to conduct NMD assumption
sensitivity testing. This testing answers the question of
how interest rate risk and liquidity risk measures change
under alternative NMD assumptions. The assumptions
that are typically tested are deposit rate beta and deposit
life assumptions.
15An introduction to non-maturity deposit rate and balance modeling
18. Chart5:KPMG’srobustmodeling
approach
MasterData
Modeling Data
Validation Data
Validation of Model Final Model Yes Meet Performance Criteria
Data Mart
FDIC
DDA
NOW
MMDA
Savings
Key
MEVs
Other
Sources
Federal
Reserve
Internal
Data pool
NO
InitialModel
Remediation
Segmentation
Stable Balances
Quantitative
ARIMA
Linear Regression
Non Quantitative Expert Judgment
Less-Stable
Balances
Quantitative
ARIMA
Markov Chain
Vintage Based
Hybrid Ensemble Solution
Stress Testing,
Sensitivity Analysis
and Parameter
Uncertainty
Run Model equation with same
beta coefficient across CCAR
and DFAST scenarios
Creation of
Documents
Benchmarking and
Outcome Analysis
Conclusion
Modeling NMDs is very challenging for many financial
institutions. KPMG can assist management in solving
key issues and complexities related to the estimation of
non-Maturity deposit rate and balance behavior. We have
expertise to assist financial institutions in building leading
practice deposit rate models that are based on market rate
and non-market rate factors. We will utilize an appropriate
approach to modeling deposit balances for your organization
whether quantitative, non-quantitative, or hybrid.
KPMG has ready approaches to assist organizations in
modeling deposits when minimal historical deposit data is
available. In addition, we have the capacity to use advanced
statistical modeling approaches to facilitate the assessment
of the impact of macro-economic variables on deposit
rates and balances. KPMG’s deposit modeling approach is
designed to assist financial institutions in meeting stringent
regulatory requirements and guidance.
19. Sources
–– Federal Reserve Bank Web site, FRB H8 data set for Large
Commercial banks in the US Banks, http://www.federalreserve.
gov/datadownload/Choose.aspx?rel=H8.
–– Federal Deposit Insurance Corporation Web site, Interest Rate
data for NMD section, https://www.fdic.gov/.
–– Bank for International Settlements Web site, BIS Consultative
paper on Interest rate risk in the banking book, https://www.bis.
org/bcbs/publ/d319.htm, June 2015.
–– Bank for International Settlements Web site,
http://www.bis.org/.
–– Bureau of Labor Statistics Web site, http://www.bls.gov/.
–– Federal Reserve Bank Web site, http://www.federalreserve.gov/.
–– Federal Reserve Bank of St. Louis Web site, http://research.
stlouisfed.org/fred2/.
–– DTCC Web site, http://www.dtcc.com.
The information contained herein is of a general nature and is not intended
to address the circumstances of any particular individual or entity. Although
we endeavor to provide accurate and timely information, there can be no
guarantee that such information is accurate as of the date it is received or
that it will continue to be accurate in the future. No one should act upon
such information without appropriate professional advice after a thorough
examination of the particular situation.
17An introduction to non-maturity deposit rate and balance modeling
